Enrico Salvatori, Qualcomm Europe

Carlo Bagnoli, STMicroelectronics

Carlo Bagnoli is Director of Infotainment BU System and Applications at STMicroelectronics. The company is a global semiconductor leader focusing on smart driving and the internet of things, creating intelligent and energy-efficient products that enable intelligent transport as well as smarter factories, cities and homes.

Within the infotainment business unit, Bagnoli and his team work to develop positioning receivers, broadcast receivers and communication processors for the automotive market. Doing so means gathering GNSS signals from far and wide.

Carlo Bagnoli is Director of Infotainment BU System and Applications at STMicroelectronics. The company is a global semiconductor leader focusing on smart driving and the internet of things, creating intelligent and energy-efficient products that enable intelligent transport as well as smarter factories, cities and homes.

Within the infotainment business unit, Bagnoli and his team work to develop positioning receivers, broadcast receivers and communication processors for the automotive market. Doing so means gathering GNSS signals from far and wide.

Once upon a time, Bagnoli says, “everybody thought GPS was enough. Now it’s the multi-constellation system that is a sort of de facto requirement.”

Coming Up
Early believers in the power of multiconstellation in situations such as urban canyons STMicroelectronics beat all major competitors to the punch when it unveiled its dual-constellation, GPS+Glonass receiver in 2011. But in fact the company had already been working for years on blending GPS+Galileo signals.

“We started in 2004 with some high-level exploratory work,” Bagnoli explained, “ and then we did our first funded research under the European Union’s FP7 Program, working to develop a Galileo-ready positioning terminal. Based on the outcome of that research we created the navigation CPU CartesioPlus product.”

So Galileo was already present in ST’s GPS/GNSS receiver hardware by the mid-2000s, with a new RF and an FPGA-based baseband. A production version of CartesioPlus followed in high volume from 2009, but it was in reality still a GPS-only chipset, because there were not yet any operational Galileo satellites in orbit.

“We were still looking into increasing the number of supporting constellations,” Bagnoli said, “So with Galileo still under development, we began working on a new product that could also support Glonass, called ‘Teseo’, which we completed and launched in 2011.”

By then, he said, the rest of the mobile industry had already understood that Glonass was quite relevant for improving the user experience in urban canyons. However, he said, the rest of the automotive mission-critical industry was late: “With Teseo, putting together the first multi-constellation chip, we anticipated the work of the others by 12-24 months.”

Today, Bagnoli says, the accuracy of Glonass has improved, through better geometry, but not because of the accuracy of the actual signal, which is still a problem.

Galileo is Born
For STMicroelectronics, the launch of Galileo initial services in December 2016 was a real breakthrough, Bagnoli said: “For a GNSS receiver company, the birth of any new Open Service GNSS system has to be considered an opportunity for new integration as it improves the user experience with no major steady-state cost added.”

The case for Galileo, he said, was a no-brainer. “ST decided to include Galileo from its inception and we have had it both in our CartesioPlus navigation CPUs and in our dedicated standalone Teseo receivers. And we are completely committed to including it on our next-generation multi-band precise positioning platforms.

“With Galileo there is no major cost of development; the development applies to multiple system-on-chip platforms, so the relative effort of adding Galileo to a set of already -supported constellations is very manageable.”

Bagnoli said the his company definitely made the right decision in preparing for Galileo early: “ST is focused on automotive and ITS and in these markets research and development cycles take significant time. Thanks to foresight, we now have two mature product families and we are looking forward to the business development of our Galileo-capable receivers.”

Multi, Multi, Multi…
Everyone seems to agree; a new type of mission-critical GNSS receiver is now needed, one that can work in conjunction with correction data available from multiple sources, ultimately providing sub-meter accuracy.

To this end, Bagnoli said, STMicroelectronics is looking at ways to combine all the functional GNSS constellations: “Finally, as we have more and more automotive ITS, integrity is becoming more and more important, so redundancy is useful and, for example, cooperative multi-constellation anti-spoofing is something that we have already worked on.

“We now have a solution where we do very fine monitoring of the different systems in order to provide more integrity beyond the accuracy dimension, and this is going to be particularly relevant for regulated services and so on.”

Indeed, while these single- frequency, multi-constellation GNSS solutions are still viable for traditional Infotainment applications, emerging intelligent transportation systems (ITS) and liability- and safety-critical applications such as advanced driver assistance systems (ADAS) are raising performance and integrity requirements for GNSS receivers, creating demand for new and even more advanced GNSS solutions.

Bagnoli describes the clear advantages of a rapidly maturing Galileo system in terms of its high accuracy and high integrity, the latter of which he says has been an overlooked aspect in many markets. “If your aim is to increase accuracy, augmenting GPS through a multi-constellation configuration, then Galileo is really the best way to go, particularly for automotive and ITS systems; And its integrity features will serve well in support of regulated services.

“Right now, with Beidou3 still under construction, GPS+Galileo is going to be a very solid mode, and this is the only harmonized pure L1 receiver combination available.”

Again, Bagnoli says, for his company the key market for the Galileo open service is ITS, including liability- and safety-critical applications. “STMicroelectronics is focusing to this market and aiming to be a key player there,” he said. “Further, we expect that this will become a multi-frequency as well as a multi-constellation equation, as more augmented driving applications arrive, and we are excited by the challenge and the business opportunity.”

The post GSA’s GNSS Opinion Leaders for August 2017 appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

1996 soccer game in the Midwest, (Rick Dikeman image)

Nouméa ground station after the flood

A pencil and a coffee cup show the size of NASA’s teeny tiny PhoneSat

Bonus Hotspot: Naro Tartaruga AUV

Pacific lamprey spawning (photo by Jeremy Monroe, Fresh Waters Illustrated)

“Return of the Bucentaurn to the Molo on Ascension Day”, by (Giovanni Antonio Canal) Canaletto

The U.S. Naval Observatory Alternate Master Clock at 2nd Space Operations Squadron, Schriever AFB in Colorado. This photo was taken in January, 2006 during the addition of a leap second. The USNO master clocks control GPS timing. They are accurate to within one second every 20 million years (Satellites are so picky! Humans, on the other hand, just want to know if we’re too late for lunch) USAF photo by A1C Jason Ridder.

Detail of Compass/ BeiDou2 system diagram

Hotspot 6: Beluga A300 600ST

1. Sweet Wheels
Maringa, Brazil
√ The latest self-steering Volvo truck innovates the way Brazilian farmers handle their crops. The Swedish manufacturing company is on a mission to revolutionize the Brazilian sugarcane industry by providing a smart and crop-friendly solution.

1. Sweet Wheels
Maringa, Brazil
√ The latest self-steering Volvo truck innovates the way Brazilian farmers handle their crops. The Swedish manufacturing company is on a mission to revolutionize the Brazilian sugarcane industry by providing a smart and crop-friendly solution.

The Usina Santa Terezinha Group provided Volvo with a test area to try out the company’s new self-driving truck. Located in Maringa, west from Sao Paolo, the sugarcane field produces sugar and ethanol for the Brazilian group. The Volvo truck was developed to investigate how automated driving could solve the problem of steering over crops. If successful, this could improve the condition of planted crops for quality harvest – up to 10 tons per hectare per year, which also means increased revenue.

The bespoke Volvo truck is equipped with a driver assistance system that automates steering. The technology is designed to ensure that the vehicle is always on the right course as it drives alongside the harvester. By doing so, the crops remain untouched and in good condition. Using GPS receivers, the truck follows a coordinated-based map as it drives through the sugarcane field. The front wheels and the entirety of the truck are driven with utmost precision by equipping the vehicle with two gyroscopes. This ensures that the truck doesn’t veer for more than 25 millimeters laterally from the programmed path. The technology promotes a more convenient way of harvesting sugarcane crops as drivers are being freed from the exhausting job of constant precision steering. It makes it easier for them to remain focused on the overall task in a more relaxed and safe method throughout the shift.

2. Floating Technology
China’s Fujian province
√ Titan Technologies Corporation has ordered two Fraunhofer IWES LiDAR measuring buoys for the surveying of the Zhangpu and Changle off-shore wind farms planned for off the coast of China’s Fujian province. This will be the first time a floating LiDAR (Light Detection and Ranging) system will be used for offshore wind measurements in China.

The buoys will be used to measure the wind conditions in the designated locations to allow precise calculation of the wind farm’s electricity yield. GPS can be used with this technology to give the position of these “floaters” directly.

The projected wind farms are owned by the China Three Gorges Corporation (CTG), which received the contract to build two wind farms with a total capacity of 2.8 GW. Titan Technologies has been engaged by CTG to perform the measurements. The company will also be completing the installation work, servicing, and data evaluation. Fraunhofer IWES researchers developed not only the design of the IWES LiDAR buoy but also the correction algorithm, which eliminates buoy movements from the measurements.

The Fraunhofer IWES LiDAR buoy has already been used multiple times for offshore measuring; most recently off the Scottish coast for the projected Firth of Forth wind farm. It measures wind speed up to 200 meters above the surface of the water. The buoy not only passed the Carbon Trust tests for floating LiDAR devices, but surpassed the requirements for accuracy and availability.

3. Robot Delivery for Mom
Sunnyvale, California
√ Residents in this California community were not surprised to get Mother’s Day gifts delivered to their homes last May, but some were caught off guard by the means of delivery —a small, white robot blasting classical music.

Starship Technologies, a London-based company that uses fleets of wheeled robots to make deliveries around the world, debuted in Sunnyvale on Mother’s Day. These robots, which resemble rolling coolers, are being used around the globe. Also in May, Starship announced its first UK robot grocery delivery.

Starship’s robots can deliver up to 20 pounds of goods over short distances and travel at a maximum speed of 4 mph. They use GPS technology to navigate sidewalks on the way to their destination.

Human operators monitor the robots remotely in case extra assistance is needed, and the company says they can handle round-trip deliveries of up to six miles. The robots run on rechargeable batteries.

4. Colliding Drones Underground
Sicily, Italy
√ European Space Agency (ESA) astronaut Luca Parmitano recently helped to explore the caverns under Sicily using a drone that deliberately bumped into its surroundings to build a map. ESA has been testing equipment, techniques and working methods for missions with astronauts in inner space for many years. Delving inside Earth and exploring caves often parallels the exploration of outer space, from a lack of sunlight to working in cramped spaces and relying on equipment for safety.

This CAVES-X1 expedition saw Luca join a scientific expedition organized by La Venta Association and the Commissione Grotte Eugenio Boegan in the La Cucchiara caves near Sciacca, Sicily.

Luca took geological samples and tried a new way of probing hard-to-reach spaces: a Flyability drone deliberately bumped into walls to learn how to navigate and to map tight areas that are too dangerous for humans.

ESA’s course coordinator, Francesco Sauro, an experienced caver and field geologist, remarks: “The drone used its thermal camera to map how the cave continued all the way to an unexplored area featuring water, impossible to reach for humans. These tests will help us understand which technologies can be used in future exploration of lava tubes on Mars, for example.” ESA’s strategy sees humans and robots working together to explore and build settlements on planetary bodies, as well as improving our understanding of our origins, and the origins of life in our Solar System.

The post GNSS Hotspots | August 2017 appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Like many massive infrastructure projects, the NextGen program has suffered numerous setbacks, many self-inflicted. An August 2016 report from the Office of the Inspector General (OIG) of FAA’s parent, the Department of Transportation, noted, “FAA’s plans have proven to be unrealistic, lacking stable investment priorities and requirements for NextGen systems.”

With more than $7 billion in modernization funds already expended, the FAA is currently projecting costs of $14.8 billion from Fiscal Year (FY) 2015 to 2030, according to the OIG report. Despite the characterization of NextGen as being wildly over budget, however, total cost estimates for the program “have evolved, but not increased markedly since FY 2004,” the OIG says. The president’s proposed FY18 budget requests $988 million for continued NextGen development, down from $1.055 billion in FY17.

As other infrastructure modernization efforts involving GNSS have shown, getting the technology right is the easy part. The Global Positioning System has a 22-year operational history to bolster expectations about its performance, which has continued to improve steadily. The arrival of other GNSS systems has only strengthened this technological resource.

Instead, the sticking points arise from such issues as enterprise architecture, systems integration with other technologies such as data communications and weather forecasting, interagency cooperation, human factors, cybersecurity, operational procedures, and regulatory updates to accommodate modernization.

Like America’s healthcare insurance system, modernization of the National Air Space is more complicated than casual observers might think.

NextGen needs to happen, first, because it will pay off in improved aviation operations, greater capacity, and better use of the crowded National Air Space (NAS). FAA modernization has already provided $2.7 billion in savings from such things as less usage of fuel and is expected to provide another $160 billion in benefits through NextGen’s targeted 2025 completion date.

Efforts so far have barely scratched the surface of what GNSS and other NextGen technologies can provide.

However, the need to get NextGen back on track has gained heightened urgency with the renewed push to privatize U.S. air traffic control (ATC). On June 27, the House Transportation and Infrastructure Committee approved a measure that would turn the nation’s taxpayer-funded ATC infrastructure and operations (carried out by 30,000 public employees) over to a nonprofit organization controlled by aviation industry representatives.

The measure, previously backed unsuccessfully by House Transportation Committee chairman Bill Shuster, has gained important support from President Donald Trump.

NAS modernization is a perhaps uniquely complicated undertaking with many elements subject to inevitable changes as technologies and operational environments (including the political and economic context) evolve. NextGen is a moving target being shot at from a moving platform.

Attempting to privatize air traffic control at this point in the process would throw a very large monkey wrench into some very delicate works in progress. As Senate Appropriations Committee Chairman Thad Cochran (R-Miss.) and committee Vice Chairman Sen. Patrick Leahy (D-Vt.) said in a February 28 letter to Senate Commerce, Science and Transportation Committee Chairman Sen. John Thune, “If air traffic control were separated during this critical period of technological advancement, the progress already being made to synchronize investment from government and industry related to safety, equipage, training, operational changes, and overall integration would be lost.”

And the FAA, not some newly convened group dominated by stakeholders with their own interests in mind, should continue to lead this project. As a special National Research Council committee concluded in a congressionally mandated analysis of NextGen in 2015, “Replacing or upgrading systems while continuously and safely operating the whole system is an intricate undertaking, a process that the FAA seems to have mastered.”

The post Turn NextGen into ThisGen appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Return to main article: "Feature Selection for GNSS Receiver Fingerprinting"

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Return to main article: "Feature Selection for GNSS Receiver Fingerprinting"

The post Figures 4, 5 & 6, Table 1: Feature Selection for GNSS Receiver Fingerprinting appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Return to main article: "Feature Selection for GNSS Receiver Fingerprinting"

The post Figures 1, 2 & 3: Feature Selection for GNSS Receiver Fingerprinting appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Working Papers explore the technical and scientific themes that underpin GNSS programs and applications. This regular column is coordinated by Prof. Dr.-Ing. Günter Hein, head of Europe’s Galileo Operations and Evolution.

Working Papers explore the technical and scientific themes that underpin GNSS programs and applications. This regular column is coordinated by Prof. Dr.-Ing. Günter Hein, head of Europe’s Galileo Operations and Evolution.

Several advanced services rely on Global Navigation Satellite System (GNSS) receivers as data providers. GNSS-derived position, velocity, and time (PVT) information enables applications such as proximity-based marketing, real-time travel services, traffic updates, precision farming, weather reports, and roadside assistance, to mention a few examples.

GNSS receivers also play a significant role in several regulated applications where security is an important aspect. In the road transportation sector, the new EU Regulation 165/2014 (see European Commission in Additional Resources) adopted in February 2014 by the European Parliament and the Council foresees the introduction of a new generation of Digital Tachographs (DTs), called “smart tachographs,” with increased security mechanisms, a GNSS component, and different communication interfaces. Tachographs record driving time and mitigate the risk of tired drivers having looser control of vehicles with higher risk of accidents. There are potential economic incentives for infringement of the regulation and tampering with the tachograph system. In this respect, the secure provision of PVT information from a trusted GNSS receiver is an important asset.

Integrated in smartphones, GNSS receivers can also be used to increase the security of mobile banking services (see A. Pujante in Additional Resources). In addition, there may be economic interests around smartphone usage to falsify the data provided by a GNSS receiver.

In this respect, GNSS receivers can be interpreted as nodes in a network where they provide location data to higher service levels. In the tachograph, the vehicle unit, i.e., the recording equipment installed in the commercial vehicle to monitor the driver behavior, implements and provides these higher service levels. In smartphones, these levels are the final user applications: electronic fraud can take advantage of possible vulnerabilities of the communication channel between GNSS receivers and higher service levels. In particular, GNSS Faking Software (GFS) applications can be installed on the smartphone to falsify the user position with the final goal of obtaining a personal or commercial benefit.

GNSS data faking consists of intercepting genuine GNSS data and replacing them with forged location information. Differently from jamming and spoofing, which operate at the Signal-in-Space (SiS) level, GNSS data faking operates at the receiver level. GNSS data faking tries to intercept and falsify the messages between the GNSS receiver and the application nodes.

In GNSS spoofing, an attack can be detected by exploiting SiS-specific features which are difficult to counterfeit (see A. Jafarnia-Jahromi et alia in Additional Resources). Similarly, a possible solution to GNSS data faking is the usage of device-specific features which are difficult to counterfeit. This approach is usually referred to as device fingerprinting and is defined as “the process of gathering device information to generate device-specific signatures using them to identify individual devices” (Q. Xu et alia, Additional Resources). Fingerprinting has gained significant interest in the field of wireless networks where node forgery or impersonation has become a threat. Node forgery consists of the acquisition of legitimate credentials by an adversary who will use them to conduct fraudulent activities. GNSS data faking is similar to node forgery in a wireless network.

In particular, a simulator or another device can be used to impersonate an actual GNSS receiver. In this way, misleading PVT information can be sent to the final PVT user. GNSS receiver fingerprinting can be adopted in security- enhanced applications that will be able, at least to a certain extent, to verify the authenticity of GNSS data. In such applications, the device which relies on GNSS data, such as the vehicle unit of the tachograph, will also extract from the received GNSS messages unique features which could be used to validate the identity of the GNSS receiver by comparing it to the previously recorded data. In a potential deployment scenario for the DT, the vehicle unit could record the fingerprints of the GNSS receiver in the initial installation phase or during the periodic calibration checks (e.g., every two years as defined by the regulation). The installation and calibration phases are executed in a controlled environment (e.g., workshop) where the identity of the GNSS receiver can be checked by the installer.

The first step in device fingerprinting is the selection of appropriate features, which should satisfy two basic properties: the features should be difficult to counterfeit and be stable with respect to environmental changes.

We investigate the selection of appropriate features for GNSS receiver fingerprinting. This process consists of considering, at first, a set of redundant metrics that have the potential to identify the receiver. A fingerprint, i.e., a subset of the original set of metrics, is then selected using a filtering approach.

We first investigate metrics related to the receiver clock, summarizing the results obtained by the authors in the paper presented at the 2016 ION GNSS+ conference and listed in Additional Resources. We then extend the analysis to clock-unrelated features.

Clock-Based Metrics
Fingerprinting of electronic devices is often based on distinctive imperfections such as the errors generated by the local oscillator of the device under test. In the context of wireless networks, Radio Frequency (RF) oscillator imperfections have been used as a source of reliable, forge-resistant features (see, for example, A. C. Polak and D. L. Goeckel in Additional Resources).

Consider for example, the normalized frequency error shown in Figure 1. The time series have been obtained by normalizing the receiver clock drift estimated as part of the navigation solution of a GNSS receiver and shows distinctive random effects with (possibly) stable characteristics. These characteristics must be identified and used as features.

We analyzed several metrics that are adopted in the literature to characterize the behavior of a time/frequency source.

The metrics considered are illustrated in Figure 2 that also describes the main elements of the methodology adopted for their evaluation. GNSS measurements are used to compute the user PVT solution. The normalized receiver frequency error, f_e[n], is then computed from the clock bias, dt_r[n], as 

Equation (1) (for equations, see inset photo, above right)

here n is the time index and Ts is the sampling rate. f_e[n] can also be computed by normalizing the clock drift by the GNSS center frequency, in this case f_L1=1575.42 MHz. The time series shown in Figure 1 have been obtained by normalizing the clock drift estimated during a static data collection. It is noted that the clock drift and the clock bias are computed from different observables, Doppler measurements, and pseudoranges. Thus, they have different characteristics. We showed in our paper presented at the ION 2016 GNSS+ conference that the normalized frequency error derived from Doppler measurements leads to the features that are more stable to environmental changes. Doppler measurements are less affected by the different error sources and thus should be preferred for the determination of receiver features.

The normalized frequency error is then used to compute different metrics such as the Allan Deviation defined as (see S. Bregni, Additional Resources):

Equation (2)

Equation (3)

The Allan Deviation is a curve which depends on the averaging time, τ. For this reason, it cannot be used directly as a feature for fingerprinting. Therefore, summary statistics, describing the behavior of the Allan Deviation are needed. We selected the Allan Deviations at τ = 1 second and at τ = 30 seconds, the curve slope between τ = 1 second and τ = 30 seconds, the minimum value, and the averaging time corresponding to the minimum Allan Deviation. In this way, five features where obtained from the Allan Deviation.

A similar process was undertaken for other performance curves that are generally used for characterizing time and frequency sources. We considered the Root Mean Square Time Interval Error (RMS-TIE), the Maximum Time Interval Error (MTIE), and the correlation between the samples of the normalized frequency error. As for the Allan Deviation, summary statistics were selected. In this way, a total of 13 features were determined. Additional details on the different features selected can be found in D. Borio et alia.

Clock-Unrelated Metrics
Many mass-market receivers only provide the user location and velocity. In this case, it is not possible to compute the clock-based metrics discussed above. For this reason, we considered clock-unrelated features for receiver identification. The term “clock-unrelated” is used to denote features derived from the position and velocity time series, i.e., from data that do not include the receiver clock bias and clock drift. The rationale behind the analysis conducted is that the errors affecting the clock components and the vertical components in the navigation solution should, in general, be highly correlated. In this way, it should also be possible to extract effective features for receiver fingerprinting from the spatial components of the navigation solution.

We followed an approach similar to that detailed for the clock-related features. In particular, the features described in the previous section were computed using velocity and position components. For example, the Allan Deviation is computed using the velocity time series. In this case, the Allan Deviation does not characterize the stability of the receiver oscillator but determines the quality of the velocity solution.

From the analysis conducted, it emerged that clock-unrelated features are not, in general, strongly related to their clock-based counterpart. Figure 3 compares the Allan Deviations computed using the different PVT components for two different receivers. The left column of the figure considers Allan Deviation curves computed using Doppler-based time series. Since velocity components and clock drifts have different normalizations, the curves have been shifted in order to make the initial point of each plot coincide. In particular, the Allan Deviations were shifted to start at one. A good match between Allan Deviations is found between the different curves for τ ∈ [1 – 100] for the one receiver considered in the top row of Figure 3. The same result, however, is not true for the other receiver considered in the bottom row. Although a better match is found when considering pseudorange-derived metrics (see right column of Figure 3), clock-unrelated metrics convey, in general, different information than their clock-based counterparts. Thus, the results obtained from the clock bias and drift cannot be directly applied to features extracted from position and velocity time series.

Filtering and Feature Selection
After selecting a redundant set of candidate features, it is necessary to apply a selection process in order to determine the most effective subset of features for classification. Feature selection algorithms are broadly classified as filter and wrapper methods (see the review paper from G. Chandrashekar and F. Sahin, Additional Resources). The former approaches use a cost function to rank the different subsets of features. The latter techniques wrap the selection process around a classifier/predictor, i.e., the final “user” of the subset of features selected. In particular, wrapper methods select the subset of features with the highest classification performance.

We adopted a filter approach as a compromise between complexity and performance. To apply the filtering approach, it is first necessary to preprocess the time series obtained from the GNSS receivers. The pre-processing applied here is briefly summarized in Figure 4. The time series collected for the feature computation are first segmented into data blocks of limited duration. Each segment of data will be used for computing a different realization of the metrics described above. In this way, several realizations of feature vectors are obtained. Note that several receivers of different models have been used for the analysis described in the next sections. Each receiver model represents a class. In this way, several realizations of the feature vectors are obtained for the different classes. The components of the feature vectors are heterogeneous and can assume significantly different values. Thus, a normalization is required. The following normalization is used here:

Equation (4)

where χ_j^k denotes the jth realization of the kth feature. The overline notation is used to denote normalized quantities. In the following, an additional index will be used to denote membership to a specific class or receiver type. The maximum and minimum values are obtained considering all the feature realizations from all receiver classes. Using Equation (4), normalized feature vectors are obtained where each component takes values within the [0, 1] range.

After data pre-processing, feature filtering is applied. The score function considered here is

Equation (5)

where F denotes the subset under analysis and d_i,j(F) is the inter-class distance between classes i and j. d_i,j(F) is the intra-class distance of the ith class. The intra- and inter-class distances are defined in terms of normalized features (4). In particular, the intra-class distance is defined as

Equation (6)

Equation (7)

and describes the average distance between two classes. Figure 5 provides a geometric interpretation of the different quantities defined here. It emerges that score function (5) is the ratio between the minimum distance between classes and the larger class size. Thus, subset F is selected in order to maximize the spread between classes and minimize the class dimensions.

Experimental Setup
The theoretical framework described in the previous sections has been implemented and tested using the data collected during two data collections. The tests were performed in different weeks and in different signal conditions. Two different scenarios were selected in order to evaluate the feature stability to environmental changes.

The first test was conducted using a geodetic antenna located on the European Microwave Signature Laboratory (EMSL) at the Joint Research Centre (JRC) premises in Ispra, Italy. The EMSL is the highest building in the area and no obstacles are present around the antenna. Hence, the first test was carried out in open-sky conditions.

The second test was performed using an antenna mounted on the rooftop of an office building in the JRC campus. In this case, the building is surrounded by taller constructions and by high trees which cause multipath and fading creating a disturbed signal environment.

The locations of the antennas used for the data collection are shown in Figure 6.

A common setup was designed and adopted for the two data collections. In each setup, several receivers were connected to the same antenna using an RF splitter and used to collect almost four days of data for each experiment. The length of each data collection justifies the data segmentation introduced in the previous section. The receivers logged raw GNSS observables, i.e., pseudoranges and Doppler shifts, with a 1 hertz data rate. Different types of receivers were used, including mass-market and professional multi-constellation receivers.

In order to have the same conditions, only GPS measurements were used for the data analysis. Moreover, a common set of ephemerides were adopted for all the receivers. In this way, the same operational conditions were adopted for the different receivers.

The list of receivers used in the two tests is provided in Table 1 along with the number of devices of the same type. The actual model of the devices can be found in the Manufacturers section.

Five GNSS timing modules were used for the two data collections. Among them, one was updated with the latest firmware that enabled the processing of Galileo signals. The update was performed to analyze the impact of firmware changes on devices of the same type.

Experimental Results
The data collected during the two tests described above were used for feature selection. In particular, subsets of two and three elements were considered. For each subset, score function (5) was computed. We considered only features derived from Doppler measurements, i.e., computed from the velocity/clock drift solution, because of the higher stability of these types of observables to errors and environmental changes. The features have been computed using data segments of one hour, i.e., 3,600 elements.

Subsets of three elements are analyzed in Figure 7 where both clock-based and clock-unrelated metrics are considered. In the clock-based case, features are computed from the receiver clock drift. In the clock-unrelated case, the up component of the velocity solution is used. Since 13 features were originally considered, a total of 286 subsets is found. The abscissa in Figure 7 is the index used to enumerate the different subsets of three elements. From the results reported in Figure 7, it clearly emerges that clock-based features significantly outperform their clock-unrelated counterparts. In the clock-based case, the maximum value of the score function is greater than six. This implies that, for the feature subset leading to the maximum of (5), the smallest inter-class distance is more than six times bigger than the largest inter-class distance. In this way, classes/receiver types are clearly separated and effective clustering can be performed.

This fact is further analyzed in Figure 8 showing the clusters formed using the three features leading to the maximum value of (5). These features are all derived from the Allan Deviation curve and are the Allan Deviations at τ = 1 second and τ = 30 seconds, and the averaging time leading to the minimum Allan Deviation value. The different receivers can be easily identified in the feature space depicted in Figure 8. The professional receivers from one manufacturer show enhanced performance in terms of Allan Deviation with respect to mass-market devices. This is expected given the different market segment, i.e., that of professional receivers. Mass-market receiver of type a is the only device showing significantly different behaviors in the two data collections. In the open-sky scenario, this receiver has features similar to those obtained for the timing modules mentioned above. Figure 8 also shows that firmware updates can affect the receiver behavior. This fact clearly emerges when considering the behavior of the one device updated with the Galileo firmware: the cluster defined by the features determined for this device is clearly distinct from that of the standard timing modules.

In the clock-unrelated case, the score function is always lower than 0.5. This implies a significant overlapping between classes in terms of clock-unrelated features. This fact is further investigated in Figure 9 showing feature selection results in the two-dimensional case. Two-dimensional feature vectors are considered here for clarity reasons. When the three-dimensional case is considered, the feature space representation is quite cluttered making the interpretation of the results more difficult. Moreover, the score function reported in the right part of Figure 9 shows that, in the clock-unrelated case, there is no significant gain when moving from fingerprints with two features to vectors with three elements.

The receiver classes represented in the left part of Figure 9 show that the one manufacturer’s receivers of different types have similar features. The overlapping between classes observed in Figure 9a compromises the overall score that does not increase even when an additional feature is included for fingerprinting. However, the results observed suggest that clock-unrelated features may allow for the identification of different receiver manufacturers. When considering receivers from the other manufacturer, the Allan Deviation at one second progressively decreases as a function of the receiver generation. This result reflects the fact that more recent receiver models have better Allan Deviations than older models.

Conclusion
This working paper provides initial results towards the fingerprinting of GNSS devices. The PVT solutions provided by GNSS receivers were considered as possible sources of features for fingerprints. It was shown that Doppler-derived time series, i.e., the three velocity components and the receiver clock drift, are more stable to environmental changes and thus should be preferred for receiver fingerprinting. Moreover, clock-related features, i.e., metrics derived from the receiver clock bias and drift, better discriminate the different receiver models. In this respect, a vector of three clock-derived features is sufficient to characterize a receiver model. Clock-unrelated features, i.e., based on the velocity time series, do not always allow for the identification of the receiver model. Despite this fact, experimental results indicate that manufacturer identification should at least be possible using clock-unrelated features.

Additional data collections will be performed as future work to confirm the preliminary results discussed here. A classification framework based on the features identified will also be implemented to demonstrate automatic receiver identification.

Additional Resources
[1] Borio, D., Gioia, C., Baldini, G., and Fortuny, J., “GNSS Receiver Fingerprinting for Security- Enhanced Applications,” Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), Portland, OR, September 2016
[2] Bregni, S, Synchronization of Digital Telecommunications Networks, Wiley, June 2002
[3] Chandrashekar, G. and Sahin, F., “A Survey on Feature Selection Methods,” Computers & Electrical Engineering, Volume: 40, Issue: 1, 2014.
[4] European Commission, “Regulation (EU) No 165/2014 of the European Parliament and of the Council of 4 February 2014 on Tachographs in Road Transport,” on-line, 2014
[5] Jafarnia-Jahromi, A., Broumandan, A., Nielsen, J., and Lachapelle, G., “GPS Vulnerability to Spoofing Threats and a Review of Anti-Spoofing Techniques,” International Journal of Navigation and Observation, May 2012
[6] Polak, A. C. and Goeckel, D. L., “Wireless Device Identification based on RF Oscillator Imperfections,” IEEE Transactions on Information Forensics and Security, Volume: 10, December 2015
[7] Pujante, A., “Location Authentication, Enabling New Smartphone Apps,” Inside GNSS, Volume: 9, May-June 2014
[8] Xu, Q., Zheng, R., Saad, W., and Han, Z., “Device Fingerprinting in Wireless Networks: Challenges and Opportunities,” IEEE Communications Surveys and Tutorials, Volume: 18, First Quarter 2016

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Present satellite-based navigation service providers are improving their services by adding new signals as well as by improving signal structure. Many countries are launching their own satellite constellations for navigation services, which will provide their services on earth and surrounding space. Today navigation operators are exploring the Space Service: Position, Velocity, and Timing (PVT) of other spacecraft equipped with navigation receivers. It is a totally new utility being developed beyond its original purpose of providing PVT services for land, maritime, and air applications.

Earth and space weather prediction, space vehicle formation flying, earth and space science research, exploration missions to the Moon and beyond, as well as military applications will all benefit from these new capabilities.

In the future, GNSS will have two types of service regions: Terrestrial Service Volume (TSV) and SSV. The TSV can be viewed as a shell that begins at the surface of the earth and up to 3,000 kilometers altitude. The transmitted position finding parameters are valid for the entire region. That means the PVT performance will remain same for all users in this region. Users in the TSV have coverage from the main beams of the satellite antenna.

The SSV is a shell extending from 3,000 kilometers altitude to approximately the geostationary altitude, that is 36,000 kilometers. The SSV is further subdivided into two regions: 3,000 kilometers to 8,000 kilometers, and 8,000 kilometers to 36,000 kilometers (as described by F.H. Bauer et alia, in Additional Resources). The space users (SU) will have a different level of performance as per altitude. Within the SSV, nearly all navigation signals emanate from satellites across the limb of the Earth. Users may experience periods when no navigation satellites are available and received power levels will be weaker than the terrestrial users (TU). Timing correction for SU need to be provided. Figure 1 (see photo at the top of this article for all figures) shows the shells of different service regions. The following sections will describe the NavIC constellation, as well as detailed analysis and simulation results of NavIC space service availability, signal strength and other related details.
IRNSS/NavIC Constellation
The IRNSS has been established for regional navigation services over India using a combination of GEO and GSO spacecrafts.

The NavIC constellation consists of seven satellites — three Satellites in GEO orbit (at 34 degrees E, 83 degrees E and 131.5 degrees E) and four Satellites in GSO orbit inclined at 29 degrees to the equatorial plane with their longitude crossings as 55 degrees E and 111.5 degrees E (two in each plane) as shown in Figure 2 (see P. Majithiya et alia, Additional Resources). All the satellites are visible over the Indian region for 24 hours.

The IRNSS System is expected to provide two sigma position accuracy of better than 20 meters over India and a region extending to about 1,500 kilometers around India.

The IRNSS provides two types of services i) Standard Positioning Service (SPS) and ii) Restricted/Authorize Service (RS). Both of these services will be provided at two frequencies — one in the L5 band and the other in S-band.

IRNSS/NavIC Space Service
The antenna beam pointing location (boresight) of all IRNSS satellites is 83 degrees, E 5 degrees N. The nominal received power for the L5 terrestrial user is -157.8 decibel/watts (dBW) over its service region. The simulations have been carried out to find out the signal availability of IRNSS signals for different satellite orbits i.e., HEO, Geo Synchronous Orbit (GSO), GEO, MEO and Lower Earth Orbit (LEO). The GEO and GSO height satellite orbits are more suitable for this type of service, as it provides better coverage and availability. The coverage will be over ± 28.7 degrees (L5 signal) and 24.5 degrees (S Band signal) off boresight angle for each satellite as shown in Figure 3. The following sections discuss various system parameters for space navigation service.

NavIC Space Service Volume (SSV)
IRNSS GEO and GSO constellations can provide navigation services to the space users in LEO, MEO, HEO and GEO. All satellite antenna beams are pointed towards the center of the NavIC service area. The maximum transmit power will be available to the terrestrial user at the beam center of the antenna. These satellites are also emitting power from its main as well as side lobe across the limb of the Earth. Based on the IRNSS antenna gain pattern, the coverage considered for space service is ± 28.7 degrees and ± 24.5 degrees off boresight angle for L5 and S band signals, respectively.

Received Signal Power Level
The primary service of all navigation constellations is terrestrial so they focus major signal power on the earth surface. Because of this, the signal power will be less to the space user compared to terrestrial user at the edge of the main lobe or the side lobe of the antenna gain pattern. The signal strength will also vary for various satellite orbits. The minimum received power is measured by simulation at the output of a 0 dBi (decibels relative to isotropic) right-hand circularly polarized user receiving antenna. The power provided in the Table 1 (see inset photo, above right, for tables) are at worst orbital location with normal orientation of the user, at the off nadir angles.

The antenna radiation gain pattern is considered up to off-boresight angle ± 28.7 degrees for L band signal and ± 24.5 degrees for S band signal. The minimum and maximum received power at 0dBi gain receive antenna output were determined by simulating different orbital users over 1,148 grid points for each orbital user as shown in Figure 4.

The power given in Table 1 is the worst case power of any orbital user out of any IRNSS satellite. The power variation range (from its nominal received power of -157.8 dBW for terrestrial user) for different orbital users are approximately less than 60 decibel and 42 decibel, respectively, in L5 and S band signals.

Satellite Availability
The number of satellites decide the service availability and its performance. In space service navigation, at least one satellite signal should be available to any type of orbiting user to maintain the system time. As satellite visibility is an issue of geometry and statistics, the simulation is carried out based on grid sample points — 1,148 at each altitude as shown in Figure 4. In fact, user height is the dominant variable that determines GNSS satellite visibility in the SSV. Based on simulation of the NavIC constellation, the signal availability has been determined. The percentage of L5 and S band signals availability for no signal, at least one signal, four signals and all seven signals is shown in Table 2. Figure 4 shows the simulation for different locations of the user over an orbit. L5 band users in LEO orbit will get a minimum four satellite signals availability at 66.99% of the area at the altitude of 1,000 kilometers, and GSO orbit users will get four signals availability at 34.58% of the area at the altitude 36,000 kilometers. S band users in LEO orbit will get minimum four satellite signals availability at 66.99% of the area at the altitude 1,000 kilometers, and GSO orbit users will get four signals availability at 49.48% of the area at the altitude 36,000 kilometers. HEO (maximum 70,000 kilometers altitude) orbit satellite users will get lesser four signals availability compared to MEO and GSO signals, that is 20.73% and 23.26% of the area at the altitude 70,000 kilometers, respectively, in L5 and S band signals. IRNSS constellation will provide good availability for at least one signal for HEO users, that is 57.84% and 61.50% of the area at the altitude 70,000 kilometers, respectively, in L5 and S band. MEO orbit users will have very good signal availability for four satellites, that is 58.71% and 94.08% of the area at the altitude 25,000 kilometers, respectively, for L5 and S band services. GEO users will get the lowest number of at least one and four number of signals values (as shown in Table 2), but this is still better than MEO GNSS constellation. These availability figures show that the IRNSS constellation will play major role in this service.

Today, the major goal for the space vehicles in the MEO and GSO/GEO volume is to maximize the availability of navigation signals, with four satellites always in view to ensure robust navigation performance within the coverage. The major goal for space vehicles in the HEO orbit is the availability of at least one navigation signal at all times. This ensures precise on-board timing, at all times within the HEO volume, reducing the need for very expensive clocks on-board. Combining GPS and Galileo would enable an average of three satellites in view at GEO, with four satellites in view 30 percent of the time. Using all constellations (GPS, Galileo, GLONASS, BeiDou, QZSS, and NavIC) would enable four satellites visible at Geo approximately 95 percent of the time using the signals in the L1 frequency band (see J. Miller et alia, Additional Resources).

Signal Group Delay Parameters
The navigation signal transmission antenna in IRNSS is a helix array antenna, which will have amplitude and phase contour for off-nadir angle. The delay variation depends on off-nadir angle and frequency, so there is phase contour which gives delay difference for space navigation users with respect to terrestrial users. This delay bias, hereafter called Space User Delay (SUD), will be measured at the time of payload testing (see again P. Majithiya et alia, Additional Resources). The SUD will give additional accuracy to the space users who are using the signal over the limb of the earth at the off-nadir angle of greater than 8.4 degrees with respect to the IRNSS satellite. This bias term is frequency specific and satellite specific.

The S_SPS single frequency space user should use the following equation to get better accuracy:

(Δt_SV)_S-SPS = Δt_SV − T_GD − SUD_S    (1)

Where T_GD is provided to the user in the first subframe of navigation data.

T_GD = (t_S-SPS − t_L5-SPS) × 1/(1 − γ_SL5)

Where, γ_SL5 = Ratio of S and L5 Band Squared Frequency: (f_S/f_L5)² = (2492.028/1176.45)²

The L5SPS single space user should use the following equation to get better accuracy:

(Δt_SV)_L5-SPS = Δt_SV −  γ_SL5T_GD − SUD_L5    (2)

The typical navigation payload delay performance for worst case difference w.r.t. nadir is of the order of 3nsec for SSV users.

Ionosphere Error Correction
The SV clock offset estimation reflected in the af0 clock correction coefficient is based on the effective PRN code phase as apparent with two frequencies (S_SPS and L5_SPS) and ionosphereic error corrections. Thus, the user employing both L5 and S in the ionosphereic error correction need make not further correction.

Space users should use the following equation to get ionosphere error correction:

PR = ((PR_L5-RS − γ_SL5PR_S-RS) + c(SUD_L5 − γ_SL5SUD_S)) / (1 – γ_SL5)        (3)

Where,

PR = Pseudo range corrected for ionosphereic effect

PR_L5-SPS/S-SPS = Pseudo range measured on the L5_SPS or S_SPS frequency channel, SUD_L5/S = Space User (payload hardware)

Delay for L5 and S band signals c = Speed of light

User Range Error (URE)
URE is the error bound on range measurement. This is a function of accuracy of orbit and clock solutions from ground segment, Age of Data and Uncertainty in physical and modeling parameters such as antenna group delay and phase center variation as a function of off-nadir angle. The current targeted URE requirement by the GNSS community is ≤ 0.8 meter (rms) for this SSV service.

Space User Receiver Requirement
A navigation receiver design relies on the assumption of good signal visibility and high signal strength, neither of which are available when we try to use them in space service volume. At high altitudes in addition to presence of extreme signal dynamics, the power levels are weaker, signal visibility is sparse and geometries are poorer. Heritage navigation receivers cannot be directly used in SSV because of differences in vehicle dynamics, signal levels and geometrical coverage.

The space user receiver will receive signals from the edge of the main lobe or from the side lobe so the signal strength will be less when compared to terrestrial service. Therefore, the receiver should be very sensitive and it should work at low power signal. Space borne receivers have to handle strong signal dynamics during their orbital flight, which require adaptive signal processing algorithms and dedicated strategies for satellite search and selection. In addition, accurate orbit propagation and dynamic models to propagate the vehicle state estimate is essential for navigation receivers to be used at high altitudes. Furthermore, capabilities to support the tracking of navigation satellite through multiple antennas in multiple orientations should be explored in addition to the use of earth pointing high gain antennas. Also the components with sufficient level of radiation tolerance should be considered. Due to power constraint, rapid cold start capability, fast acquisition, satellite selection and tracking of all available signals when the carrier-to-noise density (C/N₀) reduces should be considered.

The recovery from temporal jamming condition needs to be addressed when the receiver in HEO orbit comes in close proximity to one of the satellites. The most fundamental requirement for an HEO receiver is a robust navigation filter and clock model to enable operation when fewer than four satellites are visible simultaneously. One of the major requirements of user receiver for space service application is that it should be multichannel and multi constellation.

Conclusion
Navigation space service is an immerging application. It is a challenge for any GNSS service provider to cover full space service volume so compatibility and interoperability with other GNSS signals are a must. The NavIC SSV will be over ± 28.7 degrees and ± 24.5 degrees off boresight angle for L5 and S band signals, respectively. Simulation shows that the GEO/GSO constellation provides very good availability of a number of signals with reasonable received power. IRNSS signals are also available to GEO orbit user with good received power. This paper addresses the contribution of NavIC in this service so that in the future space users can plan for a suitable receiver. NavIC constellation can be used for a full range of Earth orbiting missions from LEO to GEO and beyond for both scientific and commercial programs.

Acknowledgements
The authors are grateful to Shri. Tapan Misra, Director, Space Applications Centre (SAC), Ahmedabad and Shri D K Das, Associate Director, SAC for providing overall guidance and encouragement to carry out this work.

Additional Resources
[1] Bauer, F.H., M.C. Moreau, M.E. Dahle-Melsaether, W.P. Petrofski, B.J. Stanton, S. Thomason, G.A Harris, R.P. Sena, L. Parker, “The GPS Space Service Volume”, Proceedings of the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2006), Sep. 2006.
[2] Majithiya, P., K. Khatri, J. K. Hota, “Navigation Satellite System Correction Parameters for Timing Group Delays”, Inside GNSS, January/February 2011, Vo. 6, No. 1.
[3] Miller, J., F.H. Bauer, J. Donaldson, A. J. Oria, S. Pace, J. Parker, B. Welch, “Navigation in Space, Taking GNSS to New Heights”, Inside GNSS, November/ December 2016, Vol. 11, No. 6.

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Collision avoidance will be more practically and universally achievable, even in skies crowded with unmanned aerial vehicles (UAVs), if the aviation community takes advantage of the raw measurements already present in today’s GPS receivers, but largely ignored in favor of using GPS position coordinates (see “The Advantages of Raw Measurements” sidebar, below.) Changes to existing aviation equipment could enable aircraft to estimate the flight paths of other aircraft far more accurately enabling safer operations even under impaired conditions. The use of the raw data, once integrated into standardized flight protocols, could dramatically help prevent midair accidents even if one or both of the aircraft is unmanned.

This flight-validated approach uses established algorithms, readily available universal access transceivers (UATs) and existing communication message formats (as described by P. Duan et alia in Additional Resources). There is one essential departure from current practice: including the raw pseudorange and carrier phase data within the automatic dependent surveillance-broadcast (ADS-B) messages in place of the derived position coordinates. This same approach could be applied with great benefit should a separate system, similar to ADS-B, need to be developed to support UAV operations.

For more than 50 years it has been feasible to combine intermittent partial data – of different types at varying accuracies with different sensitivities from different directions at different times – and extract all benefit therein. The seemingly unspectacular step of using raw measurements in the message opens the door to using powerful, widely understood methods for predicting flight paths — and therefore the points of potential collision — and handling situations where position determination is hampered due to jamming or because there are not enough satellites in view. It should be possible to do all of this for aircraft at various altitudes without building and certifying extensive new radar or other ground infrastructure.

This paper primarily involves GPS and airborne operation, but a half-century of experience combining data enables this technique to be dramatically extended. Integration of different sensors (eLoran, DME, etc.) is straightforward; a claim that has been verified and documented.

Raw Measurements Improve Estimates
For a host of reasons, techniques using raw measurements — which are present in any navigation sensor — will outperform by orders of magnitude techniques relying on position reporting. Differential GPS (DGPS) owes its spectacular success to its use of raw measurements. A Kalman tracker uses weights based on an extensive array of data. There are across-axis correlations between error components in different directions, between components of position and velocity, etc. — and the sensitivity of each individual observation to every one of those components is taken into account.

Unfortunately none of those features can be used when starting with coordinates derived from raw measurements. Since ADS-B link bandwidth can’t hold its existing content plus all that correlation information, ADS-B messages contain no correlations — but airborne computation armed with a history of raw measurements can deduce all. The contrast could hardly be more compelling.

The extended squitter message can remain unchanged except for the replacement of position and velocity by raw measurements. Since navigation systems commonly allow multiple message types, however, position reports are not strictly ruled out. Occasionally another message type could be used to broadcast the position for, say, track file initiation. To realize the performance potential, however, messages containing raw measurement data would be far more frequent.

But why go to the trouble of changing an established methodology (that is position reporting) for one based on Kalman filtering considerations? There are multiple reasons. Probably most obvious, instantaneous position is fleeting for anything airborne; data must be combined. Even satellite navigation uses several observations to get a position fix (i.e., four satellites in air). Another reason stems from the nature of the position that must be determined: To support collision avoidance the position information for one object must be determined relative to the other objects in nearby space and projected into the future. Using raw data makes these calculations far, far more accurate.

Consider, for example, a pair of position coordinates, one with perfect longitude but a kilometer of error in the north direction, and the other with exact latitude but its east/west position is off by a kilometer. Averaging them gives “only” 500 meters of error in both!

Even if different tolerances of different position reports are taken into account, ignoring wide variations in sensitivity and correlation parameters is ruinous. There are no tight velocity accuracy requirements specified for ADS-B as a result.

Accurate Velocity Essential
In fact the velocity requirement for ADS-B is loose in multiple ways. For characteristics that matter in regard to collision avoidance, velocity is a vector — a vector relative to other objects in nearby space regardless of those objects’ latitude or longitude — and with errors having statistical properties. Error values of several meters/second, even as much as 10 meters/second, have been published in connection with ADS-B. A more subtle point is that even a substantially lower 1 meter/second error value is dangerous statistically. Without detailed elaboration, this much needs to be recognized: extreme value theory (EVT) shows that, even if all errors were Gaussian, mixed Gaussian probability offers far less assurance than intuition would suggest. Instances of exceeding 10 sigma cannot be discounted (see Farrell J., and F. van Graas, Additional Resources). To ignore that is to accept an excessive and unsafe risk; “unlikely” is often not unlikely enough. A 10 meter/second error is therefore not too farfetched to consider. A sequence of position reports can suffice for transoceanic flight but not within crowded airspace.

Avoiding mid-air crashes requires a fresh look at system priorities. For example it is not critical to have highly accurate position reports. For airliners with wingspans tens of meters long moving at hundreds of kilometers per hour, precise position is fleeting and unnecessary. A few meters of current position error will be insignificant and working to refine that position error would be pointless.

It is essential, however, to have highly accurate velocities. The product of (velocity error) × (time to closest approach) is dominant when it comes to collision avoidance. Instead of meters per second velocity accuracy, airliners need centimeters/second accuracy; otherwise the projected position over time is so crude as to be useless for collision avoidance.

Collision avoidance demands assurance of sufficient distance at time of closest approach. That clearly requires accurate knowledge of velocity — specifically the relative velocity vector. Stitching coordinates together from reports of latitude + longitude + altitude (“LLH”) cannot deliver that, which explains why ADS-B does not promise good velocity. Errors of 10 meters/second have been published with little elaboration (not relative, not vectorial, and with no statistical boundaries). If the closest approach is a minute away, then even the most elementary arithmetic assigns 600 meters of uncertainty to that future position.

Consider, for example, two aircraft flying at the same altitude: “Ownship” at location O with velocity VO and “Another-ship” at location A with velocity VA (see inset photo, above right). They are instantaneously separated by vector R which, for closing scenarios, is shrinking. The closest approach will occur at time T when the component of relative velocity (vector difference VA – VO, not shown) parallel to R passes through zero — the perpendicular component is miss distance. That simple scenario has appeared in countless context-dependent forms (e.g., with intruder at A and evader at O in or with target at A and an interceptor or projectile at O in military operations). Determination of T and minimum separation distance follows easily from relations just stated, readily superseded whenever maneuvers subsequently change either velocity.

Now to confirm the case for precise velocity: rather than current position, collision avoidance requires accurate future position (i.e., at time T). When position is projected T seconds ahead, a 10 meter/second velocity error will cause that predicted future position to be in error by 10 T meters. With that much error in each of two horizontal axes that product will be squared, producing an unacceptably large area of uncertainty. Trying to steer away from an unknown place has no meaning. The Traffic Collision Avoidance System (TCAS) uses climb/dive maneuvers instead. Imagine that becoming a commonplace event as the skies fill with unmanned aircraft.

There are many facets to this subject, but the simple, fixed-altitude case above is useful for establishing some fundamentals:

• Time T used above matches the “tau” of the traffic collision avoidance system (TCAS) only on a collision course
• Effects of current position error matter far less than velocity error 
• Centimeter/second accuracy for velocity rather than meters/ second can enable horizontal evasive strategies 
• Longer values of T (earlier evasive action) is also thereby made possible 
• Earlier evasive action is highly preferable to TCAS’s abrupt violent maneuvering 
• TCAS cannot act early because valid decisions require accurate tracks 
• TCAS tracks are informed by accurate range but very crude cross-range data 
• TCAS cross-range information improves only as the sightline rotates 
• Sightline rotation increases at close range — exactly the waterloo for collision avoidance! 
• The precise satellite navigation data used in the previously mentioned P. Daun et alia article (Additional Resources) provides full 3-D tracks quickly 
• Requisite speed changes and T have been quantified for many cases (see Farrell, J., “Collision avoidance by speed change,” Additional Resources).

Raw Measurements Improve Tracking
As noted in the article on airport surface surveillance (J. Farrell and E. McConkey, Additional Resources) computers now can easily maintain integrated track files for every participant in any scenario. Even in the 1970s two missiles plus two aircraft were simultaneously tracked in real time with an electronically steered radar antenna at White Sands. The estimation algorithms in the White Sands case were fed by raw observations (range, azimuth and elevation in that case) — never with coordinate pseudo-measurements — and tracking from high dynamic platforms with “Ownship” navigation is a straightforward extension of tracking from a stationary location.

Today’s computing capabilities readily enable each participant to maintain a bank of extended Kalman filters (EKFs) with a separate track file for each participant and with every participant having a designated slot in the sequence of transmitted messages from all the participants. The full set of participants should include every object that could be involved in any collision. The track file in any participant’s database is not tied to coordinates; it’s scalar. From those scalars each participant can construct a set of vectors and all those vectors will be correct and can be expressed in his own perceived reference. If that perception differs from the other participants’ (due to misalignments or even a different datum), performance does not suffer one iota.

Air-to-air tracking has always placed the Ownship described in the example above at the center of its “own little world” without any degradation. What matters is relative state (position, velocity…) in Ownship’s “own little world” expressed and maintained consistently the same way for all. Sharing satellite navigation data with others will not introduce any error since those measurements are scalar —unattached to any coordinate frame. If the presence of one participant with overriding authority must be identified, one of the participants could be a tower. With the exception of the tower, if there is one, moving participants would make path adjustments with each message received as the scenario unfolds. Those smaller, repeated adjustments over time will prove far less abrupt than making a start-from-scratch change at close range.

Dealing With Too Few Satellites
Using the raw data also enables the development of track files in situations where there are not enough GPS satellites in view. In fact, in some urban canyon scenarios it is possible to have situations where there are never enough satellites available with good enough geometries.

As things now stand, if an aircraft’s GNSS receiver does not have enough satellites in view it is not able to determine its position and therefore has nothing to broadcast on ADS-B. That is a scandalous waste of very accurate information. Raw data measured every second or so will give you a far better track file than the usage of GPS coordinates. Stitching coordinates together to get velocity gives totally inadequate performance. That is why ADS-B, even with all the ADS-B Out and ADS-B In information, will not provide accurate velocity.

UAV-Specific Considerations
While UAVs will be responsible for taking evasive action, they will be less burdened in other respects. Their lower speed affords multiple advantages: more time for evasion, track file initiation at short range (allowing operation at low power) and the ability to make tighter turns. All of these factors make sense and avoid easier for UAVs than it is for fast-moving airliners.

Nor will UAVs require the sophistication used by P. Duan et alia in Additional Resources, which used 1-second changes in meticulously prepared carrier phase measurements. Many satellite navigation receivers don’t use carrier phase but all have pseudoranges — those will suffice as long as they are made available with appropriate time stamps. Also, decimeters/second rather than centimeter/second velocity error will be acceptable — again because of a UAV’s slower speed. With evasion by acceleration or deceleration, for example, the simple program (see J. Farrell, “Collision avoidance by speed change,” in Additional Resources) can just have different parameters. Finally, evasion strategy won’t be limited to speed changes; descent or turns can be used in some circumstances.

The Challenge
Though the advantages of using raw measurement are clear, change is not easy. Using position, and over recent decades GPS-derived position, in ADS-B messaging has long been the established approach. However the integration of unmanned aircraft is such a monumental challenge that new techniques and air traffic management systems for UAVs are being considered. Incorporating raw measurements not only offers a capability that supports safe UAV integration, but offers real advantages to manned flight operations as well — and there is a rock-solid track record supporting both double differencing (see “Double Differencing” sidebar, below) and all modes of tracking (air-to-air, air-to-surface, surface-to-air, surface-to-surface) by adaptive modern estimation.

Conclusions and Recommendations
Working with the raw measurements instead of relying only on the position calculated using those measurements makes it possible to apply the techniques that made differential GPS so spectacularly successful. This approach also opens the door for the integration of data from information sources completely different from GNSS and from each other. Raw measurements offer the only way to achieve true integration with systems like DME, eLoran and Iridium and, especially for cooperating UAVs, signals-of-opportunity (see R. Kapoor et alia, Additional Resources).The scope can also be extended to include observations of nonparticipants (see Fig. 9.4 in J. Farrell, “ GNSS Aided Navigation and Tracking – Inertially Augmented or Autonomous,” in Additional Resources).

The improvements in situational awareness are dramatic enough to suggest redefining availability and continuity of operation. Less obvious but equally decisive is how this approach strengthens integrity. Every individual measurement can be acceptance-tested — directly, easily, and independently of all others, supported by demonstrated equivalence to rigorous, widely accepted parity methods (see “Integrity Testing: Ultra-simple And Rigorously Validated” sidebar, below).

These dramatic improvements do not require new discoveries or the invention of new equipment. A revision of the ADS-B message content, hopefully via a software update — and the inclusion of raw measurements in any new system developed to support UAVs — will enable a host of spectacular benefits from already readily available data. In fact the use of raw measurements is so promising that SAE International has begun developing standards to bring this approach into the mainstream.

There also are documented non-proprietary navigation algorithms already available that make it possible to tap the value of the raw measurements. These algorithms could help keep costs down and speed the launch of a pilot project to test this approach, especially in the case of unmanned aircraft. There is enormous commercial, political and regulatory pressure to integrate UAVs into the national airspace. A pilot project could support both manned and unmanned aviation by strengthening reliability and robustness while boosting accuracy and integrity — thereby helping keep aircraft out of each other’s way.

An old movie scene showed Bob Hope trudging through a desert, desperately uttering “water, water” — then finding himself waist deep in a stream moments later, mumbling “mirage, mirage.” The advantages of using raw measurements for ADS-B and systems similar to ADS-B are not a mirage. Between what we know and what we do is a wide gulf. Let’s close it.

Appendix—Additional Topics
Two separate but related articles from a recent Institute of Navigation newsletter discuss important developments in GPS/GNSS interfacing. Starting on page 1 and continued on page 7, the first describes major improvements in Android handsets. The second, on pages 14-15, announces formation of a Society of Automotive Engineers (SAE) International working group, which will work on the standards cited in the Conclusions and Recommendations section of this article, ensuring the extension of benefits to the vast majority of devices. (SAE International is a global association of more than 128,000 engineers and related technical experts in the aerospace, automotive and commercial- vehicle industries.) These were preceded by other publications emphasizing the benefits offered by working with measurement data. One, more than 25 years old (J. Farrell and F. van Graas, Additional Resources) was in fact preceded by an obscure (1977) NAECON paper. Two more recent videos here and here plus a presentation offer additional background.

The centimeter/second residuals achieved in flight test described previously by P. Daun et alia, in Additional Resources, were obtained by using sequential changes in carrier phase measurements measured once a second. Unlike the carrier phases themselves, 1-second changes in them are interoperable (i.e., regardless of different timing and/or geoid conventions used for separate constellations) and immune to catastrophic error (see links to https://jameslfarrell.com content in Additional Resources). Furthermore, because two main sources of propagation error change very little over a second, there is no need for a mask angle — a trait that benefits geometric dilution of precision (GDOP) for velocity.

Additional Resources
1. Bayliss, E., R. E. Boisvert, M. L. Burrows, and W. H. Harman, “Aircraft surveillance based on GPS position broadcasts from Mode-S beacon transponders,” ION-GPS94.
2. Duan, P., M.U. De Haag and J. Farrell, “Flight test results of a measurement-based ADS-B system for separation assurance,”, NAVIGATION, Journal of the Institute of Navigation, Volume 60, Number 3, 2013, pp. 221-234; Abstract
3. Farrell, J. and E. D. McConkey “Quantum improvement in airport surface surveillance,” IONNTM, 1998
4. Farrell, J., E. D. McConkey, and C. G. Stephens, "Send measurements, not coordinates," NAVIGATION, Journal of the Institute of Navigation, Volume 60, Number 3, 1999, pp.203-215). 
5. Farrell, J., GNSS Aided Navigation and Tracking — Inertially Augmented or Autonomus, American Literary Press, 2007
6. Farrell, J., "Collision avoidance by speed change," COORDINATES Volume VIII Number 9, Sept. 2012, pp. 8-12
7. Farrell, J., “Letters: Get a Start on GNSS Interoperability Now,”
8. Farrell J. and F. van Graas, “That all-important interface,” James L. Farrell and Frank van Graas, ION-GPS90
9. Farrell J., and M. L. Farrell, “ADSB (2nd-) Best Foot Forward?” Journal of Air Traffic Control, Summer 2008, 44 17-18.
10. Farrell J. and F. van Graas, “Containment Limits for Free-Inertial Coast,” ION-GNSS2010
11. Farrell, J., "Single-Measurement RAIM"
12. Farrell, J., "Send Measurements, Not Coordinates" — pages 14-15
13. Farrell, J., "Dead Reckoning by GPS Carrier Phase"
14. Farrell, J., "1-sec Carrier Phase (again)"
15. Kapoor, R. S. Ramasamy, A. Gardi, R. Sabatini, “UAV Navigation Using Signals of Opportunity in Urban Environments: An Overview of Existing Methods,” 1st International Conference on Energy and Power, ICEP2016, 14-16 December 2016 (Available online here)
16. SAE International, Remote Identification and Interrogation of Unmanned Systems
17. Van Sickle, G., “GPS for military surveillance,” GPS World, Nov. 1996. 

SIDEBAR: The Advantages of Raw Measurements

A 2012 flight validation used GPS without augmenting system corrections but with raw measurements from receivers. Twenty years earlier Lincoln Labs successfully demonstrated GPS broadcasts with Mode S beacon transponders at Logan Airport as described in E. T. Bayliss et alia. (Note: the 2012 flight in the first case used UATs instead of Mode S). Transmitted positions enabled each participant to track every other participant’s data while minimizing or eliminating garble, by replacing conventional interrogations with information in assigned time slots (as ADS-B currently prescribes).

One basic modification of the Lincoln Labs methodology was advocated in the work by J. Farrell and E. McConkey and linked with another system — the Joint Tactical Information Distribution System (JTIDS) to form a more general application. Instead of coordinates, the transmitted message’s 48 information bits can contain raw uncorrected measurements. Data compression and the cycling of in-view GPS satellites can mitigate bandwidth limitations.

The introductory paragraphs of an earlier article titled “Send measurements, not coordinates,” (J. Farrell et alia) noted eight crucial advantages. In combination with each — tracking-every-other feature already noted — a later expansion of that paper (J. Farrell and M. Farrell, Additional Resources) offered an even more extensive list of advantages:

• Two decades of stunningly successful differential GPS operations demonstrate this approach
• Error source cancellation capability is intrinsic to differential GPS 
• The ability to account for specific sensitivities of each individual measurement 
• The opportunity to employ those sensitivities to assign data weighting adaptively 
• Widely known techniques for minimization of statistical error resulting from that adaptivity 
• Prompt determination of full information (cross-range as well as along range) 
• Presence in that information of accurate dynamics as well as current position 
• Ability to use the dynamics to anticipate time of closest approach 
• Ability to deduce, from the dynamics, the miss distance at that future time 
• Ability to resolve conflicts by turns or speed change instead of climb/dive 
• Applicability to both 3-D (in-air) and 2-D (runway incursion) encounters 
• Removal of potential danger in the event of datum reference non-uniformity 
• Full usage of available data when too few satellites are visible for a full fix 
• Integrity checks enabled with any number of satellites observed 
• Unrestricted algorithm release (no strings attached or proprietary claims) 
• No need for augmentation (corrections) from ground stations 
• Opportunity for participants to share observations of nonparticipants 
• Retention of applicability with or without prospective modernizations 
• Insensitivity to different models used in different constellations

These benefits are utterly absent if calculations must rely only on instantaneous position reports.

SIDEBAR: Integrity Testing: Ultra-simple and Rigorously Validated

Volumes have been written on Receiver Autonomous Integrity Monitoring (RAIM), often supported by sophisticated analytical methods and substantial mathematical development. The good news is the hard work has been done. All a program needs is a set of expressions to put into code. Even more fortuitous is further simplification of those expressions—and that also has been done. Moreover, the way that simplification has been done allows extension beyond GNSS, to include every morsel of data used for navigation.

Conventional RAIM uses five satellites for fault detection and six satellites for fault exclusion or isolation. Because every subset of four within those sets must support adequate geometric dilution of precision (GDOP), exclusion or isolation is not always available. Then, when the five-satellite detection indicates excessive error, conventional RAIM rejects the whole quintet, the good along with the bad. Forcing valid data to suffer from “guilt-by-association” is extremely wasteful and unnecessary. See here. Reversing the loss is especially urgent when data availability is marginal. A variety of advanced integrity features offers:

• Addition of cyclic bias estimation without changing navigation solutions
• Circumvention of parity vector operations added for conventional fault isolation/ exclusion 
• Replacement of that parity vector by a parity scalar with no loss of capability 
• Normalization of that parity scalar to a form with variance equal to one (dimensionless) 
• Accounting for effects of correlations incurred by differencing 
• Inclusion of closed form matrix solutions for fault detection and isolation/ exclusion with correlations 
• Extension to separate validation of each individual measurement, whether others are present or not 
• Opportunity to verify single-measurement tests when multi-satellite isolation/ exclusion is feasible 
• Support by rigorous theory (matrix decomposition etc.) with no need to employ it in operation. 
• The normalized parity scalar test for every individual measurement (everyone understands a dimensionless scalar random variable with sigma = 1) provides a vital means of operating with any and every available source of navigation information.

SIDEBAR: Double Differencing

Ignoring wide variations in sensitivity and correlation parameters is ruinous, but it is possible to recover that information.

In August 2000 I presented the raw measurements-in-squitter-messages concept as a natural extension of GPS double differencing and asked RTCA SC186WG4 members to imagine two happenings:

• Let every system and every plan in existence be only supplemental/ backup
• Let every participant compare his own data from each separate satellite to corresponding measurements from all other participants, weighting every individual difference adaptively according to its information content (we’ve been optimizing partial information weights for a half century).

A rock solid track record supports double differencing and all modes of tracking (air-to-air, air-to- surface, surface-to-air, surface-to-surface) by modern estimation. A sequence of position reports can suffice for transoceanic flight but not within crowded airspace. As noted in [4] computerized “bookkeeping” can easily maintain track files for every participant in any scenario.    

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Federal budgeteers have made clear their support for satellite navigation though problems with military space programs in general, and GPS programs in particular, have lawmakers working to shake up the Pentagon’s management structure and put limits on new federal business to contractors whose projects go awry.

So far members of Congress have largely approved full GPS funding despite delays impacting the space, ground and user equipment segments. As of press time, congressional authorizers were further along in their work with the full House passing H.R. 2810, their version of the FY18 National Defense Authorization Act (NDAA). The Senate Armed Services Committee moved its version of the NDAA out of committee on July 14 and sent it to the full Senate for a vote.

The House Appropriations Subcommittee on Defense passed its 2018 Defense Appropriations bill and the full committee sent it to the House for approval June 29. The Senate Appropriations Subcommittee on Defense has held hearings but has not yet approved a bill.

The Big Picture
Throughout this process both Republican lawmakers and the White House have been basing their budgets on their mutual belief that the Defense Department needs more resources than it has been getting. Though they agree on the problem they have yet to agree on a level of funding to fix it.

The President asked for $574 billion for the Department of Defense base budget —an amount exceeded by every congressional budget bill so far.

Looking just at the overall defense totals the House NDAA authorized $631 billion in discretionary defense spending (the base budget) while the Senate set a level of $640 billion. The full House Appropriations Committee reported out a bill with total discretionary defense spending of $584 billion while the non-binding House Budget Committee’s 10-year spending plan set the amount at $622 for fiscal year 2018. None of these numbers include the tens of millions budgeted separately for ongoing conflicts — that is funding listed under the account for Overseas Contingency Operations (OCO)/Global War on Terrorism.

While certainly consistent, all this enthusiasm is not anchored in reality. The congressionally approved spending caps created by the Budget Control Act of 2011 (BCA) are still in place. Though Congress has tweaked the numbers over the last six years, the defense funding cap for fiscal year 2018 is $549 billion — $25 billion to some $91 billion less than what is being proposed.

“It all sounds real good but sequestration is still the law of the land,” said former Air Force Secretary Deborah Lee James on the July 16 broadcast of Government Matters.

Moreover, while there appears to be broad bipartisan agreement that sequestration caps should be eased, the Democrats have been insisting that a defense spending boost needs to be accompanied by a bump-up in non-defense spending, something Republicans have generally opposed. There is no clear mechanism to change that dynamic.

“As I talk to people on Capitol Hill I’m still not seeing a path forward to lift sequestration,” said James. “Ultimately if sequestration does not get lifted then we’re back to square one and all of this talk is, indeed, just talk. Sequestration must be lifted.”

The Numbers So Far
Given the sequestration dilemma it is possible, even likely, that the amounts approved for GPS will change. Even so it seems clear from their funding choices that lawmakers understand and appreciate GPS and are more likely to make it a priority, even in a budget squeeze.

For example, House appropriators shaved the administration’s budget request of $1.09 billion by just $30.0 million — $20.0 million of that from the $243.4 million request for GPS III development and $10.0 million from the $253.9 million request for user equipment. The Defense Department asked for, and got, $510.9 million for development work on the Next Generation Operational Control System (OCX) and the GPS Enterprise Integrator.

The House Appropriations Committee also approved the request for $85.9 million for GPS III procurement, concurring with the DoD’s decision to delay procurement of the eleventh of the new GPS III satellites until after the Air Force has decided on how to proceed with the GPS III follow-on contract. Lawmakers do not want to push things off too long, however, and said in the report accompanying the bill that it “expects the Secretary of the Air Force to request procurement funds in fiscal year 2019 for the acquisition of space vehicles 11 and 12.”

The authorizing committees in both the House and Senate agreed with the appropriator’s approach and approved $85.9 million for GPS III procurement. They also fully funded the request for the OCX program.

They inserted money to speed the slow procurement of Military GPS User Equipment (MGUE). The House authorizers added $10.0 million to the administration’s request and the Senate $98.5 million. The Senate authorizers also slipped another $40.3 million into the pot for development of GPS III including the Search & Rescue Payload and work on a new M-Code Hosted Payload.

The bump ups in authorized spending are really just a wish list of sorts unless there is a matching appropriation. The authorizers have a lot more clout, however, when it comes to setting policy and they aimed that clout squarely at both the Air Force and at those members of the contracting community whose space programs are running less than smoothly. It’s easy to understand why.

The Military Space Problem
Though news reports have detailed delays in one program or cost overruns in another, it is harder to follow outcomes across the entire military space portfolio, especially when budgets and schedules get re-baselined. But the Government Accountability Office (GAO) has been keeping track — and the books don’t look good.

According to a June presentation by Cristina Chaplain, who leads GAO’s oversight of military space programs, only one of the nine programs she discussed is not either over budget or years behind schedule.

For the total the Air Force portfolio, not including the Joint Strike Fighter, acquisition costs run about 30 percent above their first estimates. For space programs, however, it’s about 60 percent, she told the June 16 Strategic National Security Space FY18 Budget Forum in Washington.

Congress is particularly concerned about the GPS programs, she told attendees. The new, cyber-toughened ground system (GPS OCX), is 53 percent over its initial budget estimate budget and 5-plus years behind schedule. The GPS III program is almost four years late and now expected to cost 35 percent more than originally projected. The MGUE program has been “very slow in getting that stuff rolled out,” she said.

“When you have the Army folks coming to GAO to tell you they need more centralized authority on user equipment, you know there’s an issue,” she said, referring to the MGUE program. “You don’t go to GAO unless something is wrong.”

On top of this, several programs need to be recapitalized and there are increasing threats to space assets that require even more funding, she said. Then “poor acquisition outcomes drain the money that you have to pay for this stuff.”

Space Corps
To help address these problems the House proposed in June to establish a U.S. Space Command and create a new Space Corps under the command of the Air Force Secretary, but separate from the Air Force. The role of the principal DoD space advisor and the Defense Space Council would be abolished and a new chief of staff of the Space Corps would be appointed. That person, who would be a member of the Joint Chiefs of Staff and would report directly to the Secretary of the Air Force, would serve for six years.

Under this approach there would be also be a subordinate unified command called Space Command established under the United States Strategic Command — one of the Pentagon’s nine unified commands.

If this measure — which faces substantial opposition — is approved, the new structure would need to be in place by Jan. 1, 2019. And lawmakers want reports on the implementation plan by March 1 and Aug. 1 of 2018.

Senate Armed Services approached the problem differently splitting the current job of the DoD’s Chief Information Officer. The business functions would stay with the CIO but a new Chief Information War Officer would take over defense-wide information war-fighting functions including: 1) Space and space launch systems; (2) Communications networks and information technology (other than business systems); (3) National Security Systems; (4) Information assurance and cybersecurity; (5) Electronic warfare and cyber warfare; (6) Nuclear command and control and senior leadership communications systems; (7) Command and control systems and networks; (8) The electromagnetic spectrum — and, (9) Positioning, navigation, and timing.

The need for change is clear, the senators said in their report.

“With respect to space, numerous studies over the past two decades have exposed issues with the programmatic decision-making that is fragmented across more than 60 offices in the Department of Defense,” they wrote. Funding for space programs within the Air Force is also near 30-year lows, while the threats and our reliance on space are at their highest and growing. The Air Force was also unable to prioritize and fund $772.0 million worth of space priorities in its fiscal year 2018 budget request, opting instead to include those requirements on an unfunded priorities list.”

The Senate committee does not propose taking Space Command out from under the Air Force, but it does want the commanders to stay there a while and apply their expertise. If approved the head of Space Command would hold the job for six years.

Senate authorizers also want to ratchet up the pressure on contractors to improve outcomes by limiting new federal business for firms that miss their targets.

The legislation would have the Air Force create a “watch list of contractors with a history of poor performance on space procurement or research, development, test, and evaluation program contracts.” The commander of the Air Force Space and Missile Systems Center would be responsible for the list and have discretion to list or delist a firm or a particular division of a company. There are other reasons to land on the list — including financial concerns; felony or civil judgments; and security or foreign ownership and control issues — but being put on the watch list is not supposed to be considered de facto suspension or debarment, the report said. Being listed means the Air Force Space and Missile Systems Center could not “solicit an offer from, award a contract to, execute an engineering change proposal with, or exercise an option on any Air Force space program” with that firm without prior approval of the Center’s commander.

The measure could impact many, if not most, firms in the GPS contractor community depending on how far back the performance history goes. The Air Force has been quite clear about its frustrations with Lockheed Martin’s work on the first tranche of GPS III satellites, and positively fuming about Raytheon’s problems with OCX. The provision also could be particularly impactful if, for example, a company that struggled with a GPS contract suddenly finds itself limited in pursuing future remote sensing satellite or communication satellite work and vice versa.

Building Resiliency
Recognizing that the military is reliant on Positioning, Navigation, and Timing (PNT), the Senate authorizers also want the DoD to deploy an alternate source of time and location as a way to boost PNT resilience. This backup, which lawmakers want to deliver UTC time globally, should be space-based, they said. PNT managers could use the DoD and/or commercial systems to get it running “rapidly and at reduced cost.”

There are several services that might be able to do that for the U.S. military including Europe’s soon-to-be-completed Galileo constellation and the Satelles services offered on the Iridium constellation. Satelles is certainly pitching to the Pentagon and the DoD has been seeking access to Galileo’s Public Regulated Service (PRS) signal for some time. In fact there has been extensive work done to make Galileo signals both compatible and interoperable with GPS.

A measure in the House defense authorization bill, however, could complicate finding a space-based backup if left in the final language by congressional conferees.

The House wants to amend current law to bar the Pentagon from using satellite services provided by any organization that launched their satellite(s) on launch vehicles built, provided or launched by a “covered” country. The measure adds Russia to the list of covered countries and notes that it does not matter where the launch actually takes place. The prohibition, therefore, would certainly seem to include Soyuz rocket launches from the Arianespace Spaceport in French Guiana.

The legislation applies only to deals going forward. While both Galileo and Satelles appear to be relying on American or European launchers for the immediate future, they have used Russian launchers in the past. If approved the language could give suppliers pause if they have to forgo using Russian launchers in the future for satellite replacement.

Other Measures
In addition to finding a global backup the House encouraged the Pentagon to expand cooperation with Japan. The Committee wants a report from both the DoD and the State Department on U.S. Japanese cooperation by December 1 of this year.

The House also wants a previously ordered report on PNT resiliency in the United States. In addition, the House Committee on Armed Services wants a briefing by this December 15 on the risks associated with GPS disruptions “that could affect defense of the homeland and other defense activities in the United States.”

That briefing is supposed to cover the requirements for PNT reliability and redundancy for military operations in the United States, an analysis of the extent to which homeland defense operations rely on accurate PNT signals from GPS, and an assessment of alternative sources of PNT.

On a separate note, the Senate Armed Services Committee directed the Army and the Air Force to conduct large-scale, joint exercises to work through interoperability issues.

“Large-scale, joint training exercises that stress interoperability across domains,” they wrote, “are a vital part of establishing and maintaining military readiness for conflicts involving near-peer competitors.”

To get the ball rolling the bill would require a report from the Secretary of Defense within six months detailing what exercises involving air and land domains already exist and the DoD’s plans for expanding them and developing new ones — including where those new exercises might be held. The senators specifically want the planners to allow the room for the “robust use of the electromagnetic spectrum, including global positioning system (GPS), atmospheric, and communications-jamming.”

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