SouthPAN will use Lockheed Martin’s Second-Generation Satellite-Based Augmentation System (SBAS), broadcasting on two frequencies to augment signals from two GNSS: the U.S.’s GPS and the European Union’s Galileo system. The network is expected to be fully operational by 2028. It will be provided as a service for 19 years with an option to extend, improving accuracy from 5 to 10 meters to within 10 centimeters.

SouthPAN is a partnership between Geoscience Australia and Toitū Te Whenua Land Information New Zealand (LINZ) under the Australia New Zealand Science, Research and Innovation Cooperation Agreement.

“SouthPAN will deliver instant enhanced precision and reliable positioning to a number of industries,” said Andre Trotter, Lockheed Martin’s VP for the Navigation Systems mission area. “We have the ability to expand this enabling technology globally, and we’re really excited about that. We already have an understanding of the benefits of second-generation SBAS, and we expect more will be realized as we bring in users and learn about new applications of the technology.”

The benefits
The second-generation SBAS augments messages for dual frequencies via multiple constellations (DFMC), using both L1 and L5 frequencies from the GPS constellation and E1 and E5a frequencies from Galileo. This enhances integrity and accuracy and eliminates reliance on just one GNSS. Industries that can take advantage of SouthPAN include aviation, agriculture, maritime, construction, mining, rail and utilities.

The Lockheed Martin team developing SouthPAN has experience with every certified SBAS currently deployed, said Bob Jackson, Lockheed Martin’s SouthPAN program manager. They looked at what has worked well with the other programs and what could be improved, and “designed a system to maximize those lessons learned.” These include the U.S.’s Wide Area Augmentation System (WAAS), the European Geostationary Navigation Overlay Service (EGNOS) and Japan’s Multi-functional Satellite Augmentation System (MSAS), all of which only augment GPS-like L1 signals.

Introducing the DFMC signal on the L5 frequency represents a huge step forward, Jackson said, as does bringing precise point positioning (PPP) signal broadcasting to the E5a signal.

“DFMC SBAS has some promising capabilities, including much more expansive coverage with fewer assets,” Jackson said. “It will also perform very well in the equatorial region, which is known to have been a constraint of legacy L1 SBAS. The introduction of PPP on E5a has some exciting medium to longer term potential applications for end users as well. They’re not only getting very precise positioning, but they’re broadcasting it on a frequency in the protected aeronautical band for safety of life applications, so that has a lot of good potential going forward.”

SouthPAN has a widely dispersed set of reference stations, Jackson said, with good accuracy and integrity because of their geometry to the GPS and Galileo satellites they’re augmenting. The team also has made improvements to the system design, simplifying and streamlining the architecture to make it more robust and the process more secure.

Second-Generation SBAS will be optimized as more E5a and L5-capable Galileo and GPS III/IIIF satellites continue to enhance those constellations.

How it works
The second generation SBAS receives and monitors basic signals from multiple GNSS through widely distributed reference stations. The data is collected by a SBAS testbed master station that computes corrections and integrity bounds for each signal and then generates augmentation messages.

Those messages are sent to an SBAS payload hosted on an Inmarsat geostationary Earth orbit satellite via an uplink antenna in Uralla, New South Wales. The Inmarsat satellite rebroadcasts the augmentation messages with the corrections and integrity data to end users’ GNSS receivers. And it all happens in less than 6 seconds.

In partnership with Geoscience Australia, a variety of demos and trials of various capabilities were conducted through a testbed between 2017 and 2020, Jackson said, providing the governments with “the information and confidence they needed to go forward with the operational system.”

For example, an autonomous vehicle was configured to drive around a test track and demonstrate the navigation of a vehicle using the SBAS signal, Jackson said. Innovative applications were also demonstrated for precision farming that go beyond applying fertilizer and crop field monitoring. One example is putting collars on cattle to manage grazing patterns in the field, enabling more efficient use of the land, reducing environmental degradation and increasing the farm’s output.

The maritime industry, as another example, is interested in the efficiency the system can bring to autonomously docking ships at port as well as maximizing ship loads for increased efficiency.
This, of course, is only the beginning, with new applications expected to emerge over time.

The future
The plan is to expand the technology globally, Trotter said, and to make it available through a service-based business model so acquisition is fast and easy. Multiple users will have access to the same system, much like GPS today, so the cost can be spread out among them, making it more affordable.

“This is an enabling technology, and all the end users will be able to innovate,” Trotter said. “That is the intent, to provide the technology and allow the art of the possible to come to fruition.”

Photo credit: Michael Hull.

The post Lockheed Martin Awarded Contract to Establish SouthPAN, Enhancing PNT in Australia and New Zealand appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

The launch was attended last week by the Minister for Resources and Northern Australia, Senator Matt Canavan, who said CQUniversity is leading one of more than 30 projects that will test how industries in Australia and New Zealand can benefit from improved satellite positioning technology.

“We know that working closely with industries like agriculture is the key to understanding what Australia can gain from investing in technologies that may improve positioning accuracy from the current five to 10 meters down to less than 10 centimeters,” Minister Canavan said.

The two-year trial is being funded with $12 million from the Australian Government and a further $2 million from the New Zealand Government. It is being managed by Geoscience Australia and Land Information New Zealand, in partnership with the global technology companies GMV, Inmarsat and Lockheed Martin. The Australia and New Zealand CRC for Spatial Information (CRC SI) is managing the industry projects which will demonstrate the benefits and applications of improved positioning capability.

Chief Executive Officer Dr. James Johnson said Geoscience Australia was excited to be leading a trial that is working with 10 industry sectors to test three new satellite positioning technologies, including the world-first second generation SBAS and Precise Point Positioning.

“This trial exemplifies the benefits of government working closing with industry to translate the latest in satellite positioning technology into real-world applications. It’s all about government innovation supporting and driving entrepreneurship within industry,” Johnson said.

CRC SI’s SBAS Program Manager, Julia Mitchell said to date 11 contracts have been signed with participants from a range of industry sectors across Australia and New Zealand, including agriculture, resources, transport, construction, utility and spatial.

“It is great to see interest from a range of sectors, with the projects chosen demonstrating a wide range of uses from the livestock tracking demonstrated by CQUniversity today, to community safety applications, and testing driverless and connected cars,” Mitchell said.

In September, Lockheed Martin announced that its second-generation satellite-based augmentation system (2nd Gen SBAS) testbed started broadcasting in dual frequency, multi-constellation (DFMC) during testing that was moved up from originally scheduled dates.

Early in 2017, Geoscience Australia and Lockheed Martin announced they had entered into a collaborative research project to show how augmenting signals from multiple GNSS constellations can enhance positioning, navigation, and timing for a range of applications. GNSS signals are critical tools for industries requiring exact precision and high confidence, and this new testbed was designed to demonstrate enhanced navigation performance for several critical industrial sectors in Australia.

On the scheduled date of June 1, the program reached an initial milestone by broadcasting the L1 legacy SBAS, which broadcasts similar messages to what Wide Area Augmentation System (WAAS) and EGNOS are broadcasting, said Bob Jackson, Global SBAS Project Lead with Lockheed Martin.

“The next big milestone was going to be broadcasting for the first time of dual frequency multi-constellation SBAS using GPS L1/L5 and Galileo E1 and E5a,” Jackson told Inside GNSS in September. “We actually overachieved and the first broadcast was earlier this week, so we’re pleased.”

Also, for the first time, Lockheed Martin says it’s broadcasting precise point positioning capability off an SBAS satellite, on both L1 and L5.

“Basically, whenever you’re running a program, sooner is better,” Jackson said. “You want to get as much information as you can as soon as you can. So, we’re quite pleased at how the team has worked very well together to accomplish this.”

For more background on the SBAS research projects, read: “Geoscience Australia, New Zealand, Lockheed Martin all Part of Second-Generation SBAS Research Project” by clicking here.

The post Industry Trial of Australian SBAS Officially Launched appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

The advent of multiple constellations provides the opportunity to eliminate geometry weakness as a source of satellite-based augmentation system (SBAS) unavailability. GPS users occasionally encounter areas where an insufficient density of satellites exists to support all desired operations. This most often occurs when a primary slot satellite is out of service. However, adding one or more constellations easily compensates for this geometric shortcoming. In fact, we may now experience the opposite problem of having more satellites that can be tracked by a receiver.

The advent of multiple constellations provides the opportunity to eliminate geometry weakness as a source of satellite-based augmentation system (SBAS) unavailability. GPS users occasionally encounter areas where an insufficient density of satellites exists to support all desired operations. This most often occurs when a primary slot satellite is out of service. However, adding one or more constellations easily compensates for this geometric shortcoming. In fact, we may now experience the opposite problem of having more satellites that can be tracked by a receiver.

There are many possible methods for selecting a set of satellites to use for the GPS position solution. Very often, elevation angle is used to rank satellites. A receiver may sort the satellites by their elevation angle and keep k (number of receiver hardware channels) highest ones. While this choice is good from a tracking robustness point of view, it does not lead to the best availability.

Ideally, when choosing from n total satellites in view, the user will be able to find k that produce protection level values that are below the required integrity alert limits. In general, for aviation SBAS users it is desirable to find an algorithm that minimizes the vertical protection level (VPL) and the horizontal protection level (HPL). A brute force search, through all combinations, yields the optimal set for a given k, but may be costly and impractical when there are many possible satellite subsets.

In this article, we examine and compare several methods that are more practical than the “optimal” brute force search. One such method is a “greedy” algorithm that iteratively removes the single least important satellite one at a time until only k satellites remain.

An important consideration is that the optimal set of satellites depends on the specific protection level being minimized. The best sets will be different for SBAS VPL and SBAS HPL. Therefore, we need to define a balance when choosing between deselecting a satellite that least affects the VPL versus deselecting a satellite that least affects the HPL.

Another factor is that the receiver is also capable of reverting to advanced receiver autonomous integrity monitoring (ARAIM) when leaving the SBAS service area – or in the event of an SBAS outage. The optimal satellite sets for ARAIM VPL and HPL differ even further from the SBAS sets; so, we may want to pursue another desirable goal: finding a satellite set that simultaneously allows the SBAS and ARAIM VPLs and HPLs to remain below their respective alert limits. We then use these algorithms to evaluate the decrease in performance relative to the all-in-view protection levels.

We perform this analysis for dual-constellation conditions in order to examine sensitivity to satellite redundancy and geometric strength. Later, different constellation scenarios should be evaluated to determine the robustness of the techniques to initial geometric strength, and total numbers of satellites. This article will address several important questions:

• How quickly the protection levels increase as the number of tracking channels is decreased?
• How should tracking requirements be specified?
• If we specify a minimum number of channels, what is the correct value?

Prior Satellite Selection Algorithms
Specifying a large required number of tracking channels does not automatically assure good performance. Cases will probably always arise in which the receiver cannot track all satellites in view and, as a result, has to choose which ones to track and which to ignore. A poor selection algorithm can lead to poor performance, even when tracking a large number of satellites. Conversely, a relatively small number of satellites may lead to good performance if those satellites are well chosen. This section will describe some commonly understood methods for satellite selection.

Probably the most common satellite-selection method is to use the elevation angle as a discriminator. The receiver may determine elevation angle, given a rough position estimate and the satellite almanac files that describe the approximate satellite orbital locations. The user does not need to track the satellites to estimate their elevation angle for the assumed location. The receiver will determine the elevation angle for every satellite for which it has almanac data. It can then eliminate from consideration signals from all of those satellites whose elevation angle falls below some elevation mask angle (e.g., five degrees as in today’s GPS aviation receivers).

If the receiver has enough channels to track all of the remaining satellites then no further selection is required. However, if more satellites remain than the number of receiver tracking channels, the receiver must choose a set of satellites to track (or equivalently, the complementary set of satellites to exclude).

The “elevation” method sorts the satellites by elevation angle and keeps the k satellites with the largest values. If more satellites are present above the mask angle than the receiver has tracking channels, the lowest elevation satellites are excluded. The lowest elevation satellites typically have the lowest received power and are the most vulnerable to loss due to aircraft banking. However, they are often quite important for good vertical geometry. Removing the lowest satellites can significantly increase the vertical dilution of precision (VDOP) and, in turn, VPL for SBAS and ARAIM.

We should note, however, that the elevation method does not take into account satellite health or weighting factors. Higher elevation satellites may be unmonitored by SBAS or have large variances associated with their corrections. Simply looking at elevation angles discards this additional information.

A better method would also make use of the health and weighting information that is broadcast from the SBAS satellites. This information should be used together with the satellite locations. Only satellites designated as healthy by the SBAS should be included in the n satellites to be considered for tracking. An “optimal” brute force method would look at all possible combinations of k out of n satellites to determine the best performance. This method is optimal in terms of returning the best possible outcome, but is distinctly non-optimal in terms of computational cost. If there were n healthy satellites above the mask, a receiver with k channels would have to evaluate N_opt geometries where N_opt is given by

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

If n = 30 and k = 24, then N_opt = 593,775 geometries to evaluate. As n becomes larger or k becomes smaller, the number of geometries to evaluate becomes even larger. As we will show later, we can efficiently code subset evaluations without having to compute full matrix inversions for each. However, even with efficient implementations, this approach has significant computational cost. We have used it for a few isolated geometries to compare the optimal result to the results from other methodologies.

The “greedy” method is similar to the optimal in that it evaluates the performance of the subsets. The key difference is that the greedy method removes one satellite at a time and then uses the resulting geometry with the corresponding satellite removed to evaluate the next iteration. For a case with 30 initial satellites, all 30 subsets containing 29 satellites are evaluated. Then the one with the best metric is used for the next step where 29 subsets each containing 28 satellites are evaluated. This continues until only the desired number of satellites remains. The number of subsets to be evaluated by this method, N_greedy, is given by:

Equation (2)

For n = 30 and k = 24, then N_greedy = 165 geometries to evaluate. This is certainly more work that the elevation angle method, but far less than the optimal. Ideally, we would like to find a method that has an even smaller computational cost.

A large number of selection algorithms have been developed over time. (See the Additional Resources near the end of this article for some examples.) Many of these seek to minimize the geometrical dilution of precision (GDOP) and do so by maximizing the volume of a polyhedron defined by the satellite locations. However, such methods do not account for the SBAS weights and are therefore not as well suited for our application.

Performance Optimization
In this section we will quantitatively define how we evaluate performance and therefore how we rank one set of satellites as being better than another. The desired property is to maximize availability for SBAS operations. SBAS provides different service levels with different horizontal and vertical alert limits.

If the receiver knows the vertical alert limit VAL and the horizontal alert limit (HAL), it could use a cost function designed to try to keep the VPL and the HPL below these thresholds. Such cost functions would be small while the protection levels are below their respective alert limits but would dramatically increase as the protection level approaches or exceeds these thresholds. However, some classes of SBAS receiver merely output position estimates and protection levels. They do not know which service levels or alert limits are being targeted. Such receivers do not know how much margin they have against the alert limit thresholds.

In the more demanding SBAS services, the VAL is smaller than the HAL. Also, the user almost always has a larger VPL than HPL. Therefore, it is typically much more important to minimize the VPL than it is to keep the HPL small. However, one should take both into account and try to prevent either one from exceeding their respective alert limits. We have therefore chosen to use the following cost function for ranking geometries:

Equation (3)

where

Equation (4)

and

Equation (5)

is the position estimate covariance matrix in the East-North-Up (ENU) frame, G is the geometry matrix (also in the ENU frame), and W is the weighting matrix.

This cost function represents a trade-off between the vertical horizontal protection levels. The factor of ¼ multiplying the square of the HPL shifts priority to minimizing the VPL over minimizing the HPL. This factor is arbitrary and could easily be adjusted to shift the balance in one direction or the other.

Indeed, the cost function itself was subjectively chosen. It was chosen in large part due to its simplicity. We had initially optimized only the VPL but found that sometimes satellite sets were chosen that had large HPL values. We found that by including the horizontal terms as in (3) we prevented large growth in the HPL. There are likely other cost functions that would lead to superior availability, however we believe that (3) is reasonable first choice.

Measurement Downdate Method
Because we are trying to optimize elements of the covariance matrix, we return to the approach of the greedy algorithm. It is trying to identify the subset with the smallest value for (3). Rather than performing n separate matrix inversions to find the n subset versions of C, we can obtain them through

Equation (6)

where C_(i) is the position covariance matrix with the i_th satellite removed, S_i is the i_th column of the S matrix

Equation (7)

and

Equation (8)

Thus, starting from a single matrix inversion to obtain the all-in-view position estimate covariance matrix C, we can then find all the subset position estimate covariance matrices using much less computationally costly matrix multiplications rather than inversions.

While this downdate method points to a more efficient means to implement the greedy algorithm, we can see that it also points the way to an even more efficient algorithm. From (6) we can see that

Equation (9)

The last term in (9) represents the increase in the covariance matrix, along the j_th user position axis, when removing the i_th satellite. The smaller this term is, the less impact it has in increasing the corresponding covariance term. Therefore, if we calculate

Equation (10)

and find the minimum value over all satellites, i, we will approach the cost function of (3).

Most often we will have identified the satellite that the greedy algorithm would choose to exclude at the first step. However, rather than following the greedy algorithm and calculating the covariance matrices for sub-subsets, we can simply sort the values in (10) from the all-in-view calculation and retain the satellites corresponding to the k largest values.

We will call this the “downdate” method. We can see that it is much more efficient than the greedy method. As with the elevation angle method, we determine a set of values once for the all-in-view solution and then use the satellites with the k largest values.

In the elevation method, the retained satellite elevation angles are maximized. In the downdate method, the retained satellite values given by (10) are maximized. Although it requires more effort to determine the downdate values than it does to determine the elevation angles, the downdate method is still very efficient compared to other alternatives.

A similar method was recently proposed for GBAS by Gerbeth et alia (Additional Resources), that uses s_3,i to sort satellites. Although s_3,i correlates well with VPL, the authors had to add further logic to ensure the minimum VPL was found. For SBAS, s²_3,i/p_i,i is proportional to ΔVPL², so excluding the satellite with the minimum value directly corresponds to finding the one-out subset with the smallest VPL. In the next sections we will compare the ability of the various selection routines to optimize performance.

Simulation Setup
We used our Matlab algorithm availability simulation tool (MAAST) to create simulated geometries and weights. In order to test the algorithms’ performance against a large number of potential satellites in view, we used a GPS almanac with 31 satellites (as of May 6, 2016), a Galileo almanac with 30 satellites, and the three active wide-area augmentation system (WAAS) geostationary satellites. We simulated both the cur-rent single-frequency (SF) integrity algorithm performance and future dual-frequency (DF) algorithm performance.

We evaluated performance for users spaced on a two-degree by two-degree grid and used 300 evenly spaced time steps over one sidereal day. User positions were constrained to be in a lat/lon box between 15 degrees North and 75 degrees North, and between 175 degrees West and 50 degrees West. This set up was expected to create many different user scenarios, including ones where many satellites were in view, but with very different weights.

The weights in particular are subject to variability. It is uncertain what values will be obtained for the weighting terms by the various SBAS providers, especially in a future DF environment. Thus, the absolute values of the protection levels are subject to change, however, we believe that the relative percentage change due to removing satellites should be representative.

The SF simulation created 158,788 valid position estimates with 23,768 of them having more than 24 usable satellites in view. The DF simulation created 188,200 valid position estimates, with 26,709 having more than 24 usable satellites.

Figure 1 shows histograms for the relative numbers in view for each case. The maximum number in view for this constellation configuration was 31 satellites.

The different simulations created a wide variety of user scenarios featuring different weighting and geometry conditions. We then applied the elevation, greedy, and downdate methods to simulate receivers that had differing values for the maximum number of satellites that could be tracked.

Example Geometry
Figure 2 shows a skyplot for an example geometry corresponding to the dual-frequency simulation and for a user with 31 satellites in view. The numbers in the circles correspond to the PRN/SBAS slot numbers where values from 1 to 32 correspond to GPS, 75 to 111 to Galileo, and 120–158 to SBAS geostationary Earth orbit satellites (GEOs). The coloring indicates the sigma values used to create the weighting matrix.

The SBAS GEOs, as is typical, have much higher sigmas, and therefore, much lower weighting.

Figure 3 shows which satellites are excluded by the elevation, greedy, or downdate method assuming a maximum of 24 satellites can be tracked. Satellites excluded by the elevation method are indicated by the blue pie wedges at the top of the numbered circles identifying the PRNs and location of the spacecraft. Satellites excluded by the greedy method are indicated by the cyan pie wedges at the bottom left of the numbered circles, and those excluded by the downdate method are indicated by the yellow pie wedges at the bottom right of the circles.

Note that the greedy and downdate methods show much better agreement between themselves than with the elevation method. Both greedy and downdate methods agree that PRNs 12 and 92 are the least important satellites. They also both exclude 11, 93, 94, and 104, but not in the same order. Greedy also excludes 103 while downdate also removes 22. Both see relatively small increases in the VPL (three centimeters for greedy and two centimeters for downdate) and somewhat larger increases in HPL (70 centimeters for greedy and 98 centimeters for downdate). Both increases are much smaller than the increases seen by the elevation angle method (3.48 m in VPL and 1.23 m in HPL).

Table 1 shows the HPLs and VPLs for four methods and for the maximum number of channels ranging from 31 down to 20. We also evaluated the optimal brute force method for this table.

The downdate, greedy, and optimal methods are all comparable, even when removing 11 out of 31 satellites. This is particularly impressive because the downdate method only calculates the S and P matrices one time, for the full all-in-view solution. These matrices are not reevaluated after each satellite removal, as is the case for the greedy and optimal methods.

Although these methods may not completely agree on the order in which to remove satellites, we find little difference in performance. They are choosing between roughly equally important satellites; so, the exact ranking is not critical. Contrast this to the elevation method, which is clearly removing satellites that otherwise keep the VPL small.

Which method truly performs better is debatable as it is not obvious how much more important minimizing VPL over HPL is in this case. The last three methods all achieve VPLs below 10 meters and HPLs below 6 meters.

Table 2 shows the order in which satellites are removed when excluding satellites by each method. When the number of channels is reduced by one, a single satellite is excluded from the prior set for the elevation, downdate, and greedy methods. This satellite will not be used for any cases with an even smaller number of channels.

The optimal method, however, completely reevaluates each possible set of satellites. Thus, sometimes satellites that were excluded for a particular number of channels are not excluded for a smaller number of channels. For example, the difference between the satellite set for the optimal method when going from 26 to 25 channels is to reintroduce PRN 11 and remove PRNs 22 and 103.

In the following section we look at statistical performance for the full set of users and time steps.

Simulation Results
Instantaneous availability is determined by whether the VPL and HPL are below their respective alert limits. The example geometry has an all-in-view VPL of 8.88 meters and HPL of 4.34 meters. These are well below the LPV-200 alert limits (VAL = 35 meters and HAL = 40 meters). They are even below the CAT-I autoland alert limits (VAL = 10 meters and HAL = 40 meters).

We could significantly increase the HPL without crossing its threshold; however, the VPL has substantially less margin. This is what motivated the factor of four dividing the horizontal terms in our cost functions. Note that in the example geometry, the elevation angle method does not support CAT-I autoland with fewer than 27 channels, while the other methods support this mode down to at least 20 channels.

Remember that the broadcast sigma values are subject to change, as they depend on future dual-frequency algorithms. If the sigmas were made three times larger, the VPLs and HPLs would also all become three times larger. In that case, the all-in-view solution would still support LPV-200 (but not CAT-I autoland). In such a scenario, the elevation angle method would not support LPV-200 with fewer than 25 channels. The other methods would support it down to at least 20 channels.

Figure 4 shows the maximum observed percentage increase in the protection levels observed for the single-frequency simulation. These values decrease as the number of channels increases.

The elevation method has significantly larger values for the VPL, ranging from a ~5 percent increase at 30 channels, to more than a 100 percent increase for 20 channels. At 24 channels, there was nearly a 50 percent increase.

The downdate and greedy methods show dramatically smaller increases in VPL. They range from less than a 0.2 percent increase at 30 channels to less than 9 percent for 20 channels. These methods saw a ~2 percent maximum increase at 24 channels.

The HPL increases for the three methods are much more similar, but the greedy method has the best performance. For 24 channels, the elevation and downdate methods see up to ~30 percent increase, while the greedy method sees up to ~20 percent. A similar set of curves was obtained for the dual frequency simulation.

Availability is typically specified as an average, which over time requires more than 99 percent of all geometries at a single location to be instantaneously available. We can calculate the effect that having a limited number of channels has on observed availability, but it is harder to generalize the results. They will depend greatly on the assumed constellations and weights. They also will be very dependent on alert limits for the desired operation.

Figure 4 shows the largest observed protection level increases. If such large increases are only rarely observed, they may have little effect on average availability. However, if we evaluate constellations with an even greater number of satellites, the large increases in protection levels will be more common and will have a larger impact on average availability.

Table 3 shows the percent decrease in CAT-I autoland coverage area for the dual-frequency simulation. The coverage region is the area in which a specified availability is met. We determined the coverage region for the all-in-view case corresponding to availabilities, ranging from 95 percent to 100 percent, and then compared them to the corresponding regions for different numbers of channels. No changes were seen by any of the methods that employed 26 or more channels. The elevation method saw some significant decreases when falling below 24 channels.

The downdate and greedy methods saw small decreases below 23 channels. The elevation method results indicate that the maximum increases indeed only affected relatively few geometries for our simulated scenarios. However, these results depend largely on the assumption in the simulation scenario. A scenario with even more satellites or worse weights would see larger impacts at a higher number of channels.

Performance Specification
It is not known how many satellites will ultimately be in orbit, nor how many will be corrected by SBAS. Therefore, we advocate a minimum operational performance standard (MOPS) requirement that will ensure high availability, even if more satellites than anticipated are launched. The elevation method has the very undesirable property that with more satellites in view, the protection levels become progressively worse. This is because adding satellites at higher elevation will cause the receiver to discard lower elevation satellites, raising its effective mask angle. A higher mask angle leads to larger VDOPs and VPLs.

Instead, we would like to encourage the use of downdate or greedy selection methods. These methods are very robust to differing numbers of satellites in view and perform better as more satellites become available. However, we do not wish to mandate a particular algorithm because receiver manufacturers may have even better options available to them.

Instead of a mandate, we propose to specify a reference set of geometries and weights. Each geometry would include the elevation and azimuth angles, the identity of the GNSS constellation to which the satellite belongs, and the variances used to create the weighting matrix. We would also specify a maximum allowed VPL and HPL for each geometry.

This information would be included as part of a Matlab tool that would allow a manufacturer to encode their selection algorithm and evaluate its performance against each geometry. The specified algorithm would be considered acceptable if the tool confirms that the algorithm always returns protection levels below the thresholds. The thresholds would be set such that the downdate algorithm would pass the test, perhaps with some added margin.

We still need to determine an appropriate number as well as which geometries to include. We envision that the tool could easily run hundreds, if not thousands, of simulated cases. We would include geometries that are representative of potential future satellite configurations and that do not perform well with the elevation selection method. These scenarios need to be agreed upon by the wider SBAS community.

Compatibility with ARAIM
Thus far, this article has addressed only satellite-selection methods with which to optimize SBAS performance. However, dual-frequency multi-constellation SBAS receivers will also support ARAIM and will revert to this mode when out of SBAS coverage. Therefore, it is logical to want to optimize SBAS and ARAIM horizontal and vertical services. While the user may only need either SBAS or ARAIM service for any given operation, having both available provides an advantage in case of a failure or an outage of the primary service. However, the best trade-off between the two services is not always obvious.

A cost function that combines the protection levels for both services would simultaneously limit the growth of each term, yet may fail to provide desired service through either. In contrast, a scheme that optimizes either SBAS or ARAIM may provide service through one, at the expense of the other.

ARAIM optimization is a little more difficult than optimizing SBAS because the user will not necessarily know what confidence to place on a specific satellite until after a receiver is already tracking it. In contrast, the SBAS geostationary satellite broadcasts all of the confidence parameters for all of the GNSS satellites, regardless of whether the user is tracking them or not. The SBAS user has full knowledge of the W and G matrices.

In offline ARAIM, the user range accuracy/signal-in-space accuracy (URA/SISA) value is only included in the ephemeris data broadcast from each satellite. The ARAIM user can only guess at the contribution to the W matrix before devoting a channel to track and gather the required data. Currently, GPS constellation broadcasts a URA value of 2.4 meters more than 90 percent of the time; so, this confidence value is not necessarily difficult to predict. However, it remains to be seen how predictable these values will be in the future with new constellations and new messages on GPS that can broadcast a wider range of URA values. Nevertheless, we will assume that the URA/SISA values usually are near to a known constant value.

Example Geometry Revisited
Let’s return to the example geometry used previously: 31 total satellites above five degrees, including two geostationary satellites. For the purposes of this ARAIM analysis, we will discard these two geostationary satellites and only evaluate the remaining 29 satellites. We have further assumed that the probability of satellite failure, P_sat, is 10^-5; the probability of constellation failure, Pc_onst, is 10^-4; the integrity confidence bound, URA/SISA, is 1 meter; the accuracy bound, user range error/signal-in-space error (URE/SISE), is 0.67 meter; and the nominal bias bound, b_nom, is 0.75 m. We have assumed that these values apply identically to each satellite.

The greedy selection method can be very effectively applied to ARAIM as well as SBAS. Two ARAIM specific metrics were evaluated: the ARAIM VPL and the ARAIM vertical accuracy estimate, σ_v. The ARAIM VPL involves evaluation of numerous subsets, its specific formulation can be found in Annex A of the Milestone 3 Report under the EU-U.S. Cooperation on Satellite Navigation referenced in Additional Resources. The vertical accuracy estimate is often very similar to the square root of the SBAS vertical covariance term c3,3 (especially when the ratio of the ARAIM accuracy values is similar to the ratio of the SBAS confidence terms). Therefore, minimizing this term is usually comparable to minimizing the SBAS VPL.

Table 4 shows the results for four different selection algorithms: using the highest elevation angle satellites, the greedy algorithm selecting the best ARAIM VPL at each step, the greedy algorithm selecting the best vertical accuracy estimate at each step, and an optimal method that selects the smallest VPL over all possible combinations. Unlike for SBAS, ARAIM HPLs and VPLs can improve when removing satellites. This is because the least squares weights used for ARAIM do not necessarily minimize the ARAIM VPL, which also includes bias terms.

In the Table 4 results, the last three methods obviously all perform much better at limiting VPL growth than selecting the highest elevation angle satellites. In this example, all subset geometries have VPLs that are slightly below the all-in-view case. This situation is not uncommon when many satellites are available and the protection levels are small.

The SBAS protection levels in Table 1 are all smaller than the corresponding values in Table 4. This is to be expected inside SBAS coverage, where nearly all satellites have access to a good SBAS correction. However, on the edge of coverage, only some satellites will be corrected by SBAS, but all satellites will likely be usable by ARAIM. A possible algorithm would be to compare the all-in-view SBAS with the ARAIM protection levels and then optimize for whichever one performs better. In regions of good SBAS coverage, SBAS would be preferred. At the edges, and outside of SBAS coverage, ARAIM would be preferred.

Operational Considerations
An issue not addressed in this paper is the timing of making and changing selections. When choosing the best satellites to track, it is important to remember that it can take a little while to lock onto a satellite and establish tracking. If the satellite has not been observed recently, the receiver will need to obtain the broadcast ephemeris and confirm it with a second decoding. Thus, it can take more than a minute from deciding to track a satellite to being able to use it in a position solution. So, one should not attempt to change their selected set of satellites too often. Some priority may be given to satellites that are already being tracked.

One may also want to be cautious about selecting too many low-elevation satellites. These satellites typically provide lower received power to user equipment and are more susceptible to unexpected loss of signal lock. Some low-elevation satellites will also be in the process of setting, in which case it may be preferable to select a replacement before the satellite goes below the elevation mask. Having a large number of channels and a large number of satellites in view will hopefully provide sufficient margin such that the loss of any one satellite will not result in a loss of service.

Finally, we should note the time evolution of satellite selection involves many aspects. However, these are beyond the scope of this article.

Conclusions
We have identified a weakness in the traditional elevation angle–based selection algorithm when combined with a limited number of tracking channels. This algorithm also has the potential to perform worse when more satellites are in view of the user. The VDOP and VPL become worse when low-elevation satellites are removed in favor of higher ones.

We have quantified this potential impact for an assumed set of different geometries. Nearly 50 percent increases in VPL and HPL are possible when assuming 24 channels, as compared to the all-in-view solution that contained as many as 31 satellites.

We also presented an algorithm that does a much better job of selecting the satellites to track. This downdate algorithm limited the VPL growth to below two percent when considering 24 tracking channels under the same set of geometries. Furthermore, this algorithm is very efficient and does not require repeated evaluation of subset geometries. It acts on the all-in-view geometry to create a ranked list of which satellites are most important to track.

Finally, we propose a new specification method to evaluate performance, rather than simply state a minimum number of tracking channels. The better the selection algorithm, the fewer required tracking channels. Manufactures would also have the option to use a simpler algorithm, but at the cost of having a larger number of tracking channels.

Acknowledgments
The authors would like to gratefully acknowledge the Federal Aviation Administration’s Satellite Product Team for supporting this work under memorandum of agreement (MOA) contract number DTFAWA-15-A-80019. The opinions expressed in this article are those of the authors, and this article itself does not represent a government position on the future development of WAAS. The article is based on a paper that was presented at the ION GNSS+ 2016 conference in Portland, Oregon.

Additional Resources
[1] Blanch, J., and T. Walter, P. Enge, S. Wallner, F. Fernandez, R. Dellago, R. Ioannides, B. Pervan, I. Hernandez, B. Belabbas, A. Spletter, and M. Rippl, “Critical Elements for Multi-Constellation Advanced RAIM for Vertical Guidance,” NAVIGATION, Vol. 60, No. 1, pp. 53-69, Spring 2013
[2] Blanco-Delgado, N., and Nunes, F.D., “A Convex Geometry Approach to Dynamic GNSS Satellite Selection for a Multi-Constellation System,” Proceedings of the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation, pp. 1351-1360, Savannah, Georgia, September 2009
[3] EU-U.S. Cooperation on Satellite Navigation, Working Group-C ARAIM Technical Subgroup, “Milestone 2 Report,” February 11, 2015 (available online here)
[4] EU-U.S. Cooperation on Satellite Navigation, Working Group-C ARAIM Technical Subgroup, “Milestone 3 Report,” February 26, 2016 (available online here)
[5] Gerbeth, D., and M. Felux, M. Circiu, and M. Caamano, “Optimized Selection of Satellite Subsets for a Multi-constellation GBAS,” Proceedings of the 2016 International Technical Meeting of The Institute of Navigation, pp. 360–367, Monterey, California, January 2016
[6] International Civil Aviation Organization (ICAO), International Standards and Recommended Practices (SARPS), Annex 10 – Aeronautical Telecommunications, Vol. 1I, Radio Navigation Aids, 6th ed., July 2006
[7] Jan, S.S., and W. Chan, T. Walter, and P. Enge, “Matlab Simulation Toolset for SBAS Availability Analysis,” Proceedings of the 14th International Technical Meeting of the Satellite Division of The Institute of Navigation, pp. 2366–2375, Salt Lake City, Utah, September 2001
[8] Kihara, M., and T. Okada, “A Satellite Selection Method And Accuracy For The Global Positioning System,” NAVIGATION, Journal of The Institute of Navigation, Vol. 31, No. 1, pp. 8–20, Spring 1984
[9] Kropp, V., “Reduced All-in-View Satellite Set in ARAIM user algorithm,” in preparation for publication
[10] Miaoyan, Z., and Z. Jun and Q. Yong, , “Satellite Selection for Multi-Constellation,” Proceedings of IEEE/ION PLANS 2008, pp. 1053–1059, Monterey, California, May 2008
[11] Phatak, M. S., “Recursive method for optimum GPS satellite selection,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 37, No. 2, pp. 751-754, doi: 10.1109/7.937488, April 2001
[12] RTCA, Inc., “Minimum Operational Performance Standards for Global Positioning System/Wide Area Augmentation System Airborne Equipment,” RTCA DO229D Change 1, February 2013
[13] Walter, T. and J. Blanch, “Characterization of GNSS Clock and Ephemeris Errors to Support ARAIM,” Proceedings of the ION 2015 Pacific PNT Meeting, pp. 920–931, Honolulu, Hawaii, April 2015

The post Satellite Selection appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Trends for marine accidents show rising numbers and costs that are mainly associated with collisions and groundings. Research indicates that about 60 percent of these accidents are caused by human error. The majority of them could have been avoided by providing suitable input to the navigation decision-making process, according to a 2008 report by the International Maritime Organization (IMO) Marine Safety Committee. (See IMO 2008 in Additional Resources section near the end of this article.).

Trends for marine accidents show rising numbers and costs that are mainly associated with collisions and groundings. Research indicates that about 60 percent of these accidents are caused by human error. The majority of them could have been avoided by providing suitable input to the navigation decision-making process, according to a 2008 report by the International Maritime Organization (IMO) Marine Safety Committee. (See IMO 2008 in Additional Resources section near the end of this article.).

In order to improve safety of navigation and to reduce errors, IMO is promoting the e-Navigation concept. This is defined as the “harmonised collection, integration, exchange, presentation and analysis of marine information on board and ashore by electronic means to enhance berth to berth navigation and related services for safety and security at sea and protection of the marine environment” (IMO 2008).

IMO and the International Association for Lighthouse Authorities (IALA) identify several technological enablers that support the e-Navigation concept. These include, amongst others: accurate electronic navigation charts (ENCs), a robust electronic position, navigation and timing (PNT) system (with redundancy), and an agreed communications infrastructure to link ship and shore.

Further, robust PNT information requires three complementary components: a core GNSS, augmentation systems, such as differential GNSS (DGNSS) or satellite-based augmentation system (SBAS), to ensure that the GNSS performance is able to meet the application requirements, and an adequate backup in the event of a GNSS system failure.

The backup system is based on employing several independent terrestrial schemes that could be made available to users. They include, for example, enhanced Loran (eLoran), ranging mode (R-Mode) navigation using signals independent of GNSS, or enhanced radar positioning techniques. Used in con-junction with GNSS, such independent, redundant systems significantly increase the resilience of the on-board PNT system against possible “feared events” (FEs) affecting a single component of the system. This positioning approach is also known as “resilient PNT” and is currently being supported by the IMO in the development of the e-Navigation concept.

One of the most immediate benefits of resilient PNT is an increased robustness of the GNSS component against local error sources. These include, but are not limited to, multipath events, unintentional interference, and deliberate interference attacks such as jamming or spoofing.

In this context, possible roles for SBAS have been investigated and discussed with the maritime community. This article discusses preliminary results of an analysis of these roles. In it, we will first discuss the operational requirements for radio navigation systems (RNSs) to be used in maritime applications and then outline the current status of GNSS for maritime applications. Next, we will present the main features of DGNSS and SBAS as well as the possibility of offering SBAS as a possible augmentation system complementing DGNSS. Finally, we propose two strategies (“mid-term” and “long-term,”) for the use of SBAS in maritime applications.

GNSS Performance Requirements for Maritime
Two IMO resolutions outline the operational requirements for the use of RNSs in maritime applications. IMO A.1046(27) specifies the requirements of an RNS as a generic component of a World Wide Radio Navigation System (WWRNS). Here, a WWRNS component can be either a satellite-based (GNSS) or a terrestrial-based system. In contrast, IMO A.915(22) focuses on GNSS as a stand-alone system and intro-duces the requirements for a “future,” not yet identified, GNSS.

The two documents express the associated requirements using performance parameters that have a different scope and, therefore, a different approach. As a result, the two resolutions demand different methods to monitor the navigation performance and to verify the level of compliance with the associated requirements. This verification is a key element for safety-of-life (SoL) applications.

IMO A.1046(27). The feasibility of a WWRNS has been investigated since 1983. These studies produced an amendment of Chapter V of the 1974 SOLAS convention that includes “a requirement for ships to carry means of receiving transmissions from suitable radio navigation systems throughout their intended voyage.” The global nature of the WWRNS requires the overall system to be formed by different sub-systems (or components), possibly including GNSS together with the associated augmentation systems (e.g., DGNSS or SBAS), other regional satellite navigation systems, and other (not necessarily satellite-based) radio navigation systems, such as eLoran. WWRNS specifically allows for implementation of the resilient PNT concept, provided that independent RNSs are recognized as a component of the overall system.

Procedures and responsibilities concerning the recognition of an RNS as a component of the WWRNS are detailed in the IMO A.1046(27) resolution. In particular, a generic component (not necessarily satellite-based) of the WWRNS has two main tasks. It must first provide the users with navigation signals and must also constantly monitor the quality of the navigation service provided (the “system integrity monitoring” task). System integrity information is then broadcast to the user together with navigation signals.

Figure 1 (see inset photo, above right) presents a schematic of this type of integrity service, where the “Generic RNS” block indicates the WWRNS component. If a failure is detected in one of the RNS modules, the user is promptly warned that the system (or the information provided by that specific module) should not be used.

This approach for integrity monitoring considers only the “system-related failures” that might prevent the RNS from broadcasting accurate navigation signals. This concept is normally referred to as “integrity at system level” and is not able to promptly warn the user in case of performance degradations due to local error sources (i.e., multipath or interference).

If the user requires additional protection from local error sources, further checks — for example, receiver autonomous integrity monitoring (RAIM) techniques — must be autonomously performed by the user receiver, thereby locally verifying the consistency of the navigation signals provided by the system. Note, however, that these checks are recommended but not mandatory under IMO A.1046(27).

IMO specifies operational requirements in more detail using four parameters: accuracy, system integrity, signal availability, and service continuity. In particular, these requirements define service continuity as the capability of the system to provide its service (navigation signals and broadcast of system integrity information), without interruptions, throughout the duration of a given operation.

Based on these parameters, IMO requirements address two navigation phases: (1) ocean waters and (2) harbor entrance, harbor approaches, and coastal waters. Table 1 presents the parameters of these operational requirements. However, the IMO requirements do not specify the actual navigation performance at the user level.

IMO A.915(22). This IMO resolution expresses operational requirements for future GNSS based on the actual performance at the user level. The parameters include accuracy, integrity, continuity, and availability as defined for civil aviation applications by the RTCA Inc. (see RTCA DO-229D in Additional Resources). In other words, according to the RTCA, the properties of a GNSS system include both the GNSS service (i.e., the properties of the signal in space, or SIS, provided by both the space and the ground segments of the GNSS) and the user receiver.

This is a fundamental difference with the operational requirements needed for a single component of the WWRNS as described in IMO A.1046(27), which only considers system-related aspects. In particular, the underlying integrity concept is now at the user level and includes, for each maritime operation, the specification of three parameters, i.e., the alert limit (AL), the time to alert (TTA), and the integrity risk (IR) (See IMO, Revised Maritime Policy and Requirements for a Future Global Navigation Satellite System, GNSS,” in Additional Resources section for further details.)

Once these parameters are set, the integrity at the user level is monitored by calculating, at the user receiver, a maximum error bound (protection level, PL) for the navigation system error (NSE). This estimation is done at each epoch and normally uses navigation signals (observables), system integrity information, and suitable models for the effects due to local error sources. The definition of these models requires a detailed characterization of the operational environment, including, for example, an analysis of the expected levels for multipath and interference.

Based on the input data provided by the three variables used to estimate user-level integrity, the resulting PL calculation takes into account both system-related failures and possible effects due to local error sources. The PL calculation can be completed either completely autonomously (e.g., via RAIM techniques) or based on additional information provided by the system (e.g., when SBAS is used for aviation applications per the RTCA standards). Figure 2 represents the concept of integrity at the user level for a generic RNS that, in this case, is a future GNSS system. If the user wants to perform a given operation, the current value of the protection level is compared with the AL requirement associated with that operation. If, because of a failure, the PL exceeds the AL specification, the receiver will promptly warn the user that the position estimation cannot be trusted.

In Figure 2, the actual position error (PE), which is normally unknown, is indicated by a green dot. The PL provides a conservative estimation of the PE. In case the PL exceeds the AL specification, a “Do not use the system” warning is communicated to the end-user.

The protection level calculation should always over-bound the position error, thus avoiding critical situations where the PE exceeds the alert limit, while the estimated PL is below the AL (PL < AL < PE). These situations are referred to as “hazardously misleading information” (HMI) events, and their probability of occurrence must be below the integrity risk requirement.

HMI events might be due to unexpected local threats whose effects are not covered by the error models used for PL calculation. Essentially, this may happen for two reasons: the local threat was not considered as a potential feared event when designing the algorithm for PL computation, or the threat was actually taken into account but the adopted error model is shown not to be adequate in that specific situation.

Therefore, even if integrity at the user level is effectively provided by the PL in nominal conditions and for all the foreseen FEs, additional barriers should be put in place against unexpected situations. These barriers are a key element when a given operation is not performed in a “controlled” environment (e.g., an airport), where the actual levels of multipath or interference are kept below acceptable thresholds. A harbor environment is typically not controlled; so, additional protection schemes might be required. One of the most effective barriers is the concurrent use of location information from independent positioning schemes, as actually recommended by the resilient PNT concept.

The IMO A.915(22) resolution indicates the operational requirements for a number of navigation phases and maritime applications. However, several discussions with maritime community representatives confirmed that none of the existing GNSS systems is designed to fulfill this resolution and that no maritime operations currently require compliance with it. The basis for the requirements indicated in the resolution is, therefore, unclear. For example, one of the amendments under discussion is the operation duration, which may be reduced from three hours to 15 minutes to gain consistency with IMO A.1046(27).

As a result, the specifications currently indicated in IMO A.915(22) can only be viewed as a starting point for future evolutions. These are expected to target the harmonization with IMO A.1046(27) and the consolidation of the GNSS role in a resilient PNT system.

Current Status of GNSS for Maritime
At the moment, only GPS, GLONASS, and BeiDou are recognized as part of the WWRNS. The European Commission initiated the recognition process for Galileo, which is currently on-going. Although already accepted as a component of the WWRNS, these systems need proper augmentation systems to perform the most critical operations, i.e., navigation in harbor entrances, approaches, and restricted waters. Nowadays, the most used augmentation system is DGNSS. The possible use of SBAS is also being investigated in a number of different research projects (e.g., see the article by Kvam et alia).

DGNSS. Historically, the predominant use of DGNSS arose from the need for augmentation during the most critical maritime operations, especially when selective availability (SA) was still in place for GPS. DGNSS provides the user with differential corrections (improved accuracy) and system integrity information. Differential corrections, together with the associated user differential range error (UDRE) indicators, are evaluated at a reference station (RS), which is placed at a known location. The corrections, together with the associated UDRE, are then broadcast to the users in the RS coverage area using a dedicated medium frequency (MF) radio data link.

The nominal accuracy of IALA DGNSS beacons is typically between three and five meters. The larger the distance is between the user and the RS, the larger the expected accuracy degradation (0.67 meters per 100 kilometers from the RS, according to the IALA recommendation on DGNSS services).

As mentioned, the DGNSS integrity concept is applied at the system level only and is based on the assessment of the quality of corrections. Integrity monitoring (IM) is performed with the use of a separate DGNSS receiver, which is also placed at a known location, normally a few hundred meters from the reference station to avoid possible correlations of multipath events.

The integrity monitoring evaluates the quality of corrections in both the position and the pseudorange domains. If a pseudorange or a position alarm is generated, the user is promptly warned via a “do not use that SV” or “do not use the system” flag, respectively, which is included in the DGNSS message.

The rationale underlying this IM concept is that the IM receiver will experience the same level of performance as each user receiver in the RS coverage area. Therefore, such a concept is not able to protect the user from possible local error sources. This is an inherent limitation of all differential systems.

The effects due to local error sources can be detected by RAIM algorithms, which are able to identify possible outliers and to estimate the expected accuracy (error ellipse) of the resulting position solution. In general, these techniques can be used with both augmented and un-augmented pseudorange observations and do not depend on the particular augmentation system in use.

In order to monitor the performance of a specific DGNSS service, IALA provides some recommendations, but the scope of these guidelines is limited to service provision. For example, signal availability, A, is evaluated as:

A = MTBO/(MTBO + MTSR) (1)

where MTBO and MTSR indicate the mean time between outages and the mean time to service restoration, respectively. This recommendation covers both scheduled outages (e.g., due to planned maintenance activities) and unscheduled outages (e.g., due to unexpected failures).

Similarly, service continuity, C, is calculated as:

C = e^{(−CTI/MTBF)} (2)

where CTI is the continuity time interval (i.e., the operation duration of 15 minutes), while MTBF is the mean time between (unexpected) failures.

If a user is aware of the lack of DGNSS service or that a scheduled outage is going to take place, the operation will not be executed using DGNSS. For that reason, only unexpected failures are assumed to affect service continuity and are, therefore, considered in the service continuity equation. Note also that, according to the IALA recommendation, all the mean times (MTBO, MTSR, and MTBF) shall be evaluated based on a two-year averaging period.

DGNSS is currently not formally recognized by IMO as a component of the WWRNS, primarily because of not being able to meet the service continuity requirement. Service continuity performance might be improved by increasing the number of reference stations, thus enlarging the areas (multiple coverage areas) where a user can receive differential corrections from more than one RS. If a failure is affecting one RS, the user will still be able to receive the corrections from another; so, the service provision will not be interrupted.

Although service continuity might be a limitation in those areas where the user is able to receive differential corrections from one RS only (single coverage areas), DGNSS is, at the moment, the incumbent GNSS solution for maritime applications.

Some DGNSS Service Providers (SPs), from both public and private sectors, are already anticipating its long-term goal of making the most of different multi-frequency GNSSs. At the same time, in order to reduce the significant maintenance costs of a DGNSS infrastructure, some SPs are also promoting the use of complementary augmentation systems. This is happening, for example, in the United States, where the U.S. Coast Guard is currently authorizing the Coast Guard vessels to use the Wide Area Augmentation System (WAAS) as a complement to DGPS. For that reason, SBASs might be effectively used to complement DGNSS, thus increasing service continuity.

SBAS. WAAS and the European Geostationary Navigation Overlay Service (EGNOS) are examples of SBASs. These systems are able to provide, over a wide (or regional) area, the same type of information offered by a DGNSS service, i.e., differential corrections and system integrity information, through the use of additional, satellite-transmitted messages (SBAS messages).

SBASs are based on a network of ground monitoring stations located at accurately surveyed points that monitor the signals of GNSS satellites. The system integrity is monitored by checking the quality of differential corrections in both the pseudorange and the position domains. The measurements are collected and processed at a central facility where the SBAS messages are created. These data are then uplinked to one or more satellites (e.g., geostationary, GEO, satellites) and finally broadcast to the end users in the SBAS coverage area.

Differential corrections and system integrity information provided by WAAS, EGNOS, and other SBASs can, in principle, always be used to increase the position estimation accuracy and to protect the user from possible system-related failures. Therefore, depending on the application requirements, SBASs can be used to support a range of operations in various transportation domains, including maritime.

SBAS as a Complement of DGNSS
Table 2 presents a summary comparison of SBAS and DGNSS functionalities. Because both systems provide users with similar information, SBAS can be used to effectively complement DGNSS. This option might be particularly attractive in the maritime sector, where DGNSS is currently the most used augmentation system. As a matter of fact, SBASs are potentially able to fill possible gaps in the coverage area of a DGNSS service, thus providing the user with a “seamless,” augmented, navigation solution.

In particular, SBAS nominal performance has the potential to meet the IMO requirements for a component of the WWRNS, as shown in Table 3, where the operational requirements for the most critical operations (navigation in harbor entrances, harbor approaches, and coastal waters) are considered.

As mentioned, the opportunity of using SBAS as a complement for DGNSS has been already taken by the USCG, which has acknowledged the use of WAAS as an official augmentation system for GPS receivers. The USCG clearly states that the “National Differential GPS (NDGPS) system and the WAAS are the only GPS corrections currently authorized for Coast Guard vessel use in high-risk (e.g., restricted waters) navigational zones/areas.”

Based on these considerations, two possible strategies may be identified to promote the use of SBAS in maritime. The “mid-term” strategy consists of promoting the use of SBAS as a complement or a back-up to DGNSS. In this case, the underlying integrity concept is at the system level only. The “long-term” strategy is, on the other hand, based on the development of a novel integrity concept designed in close cooperation with the maritime community that will closely follow the evolution of the resilient PNT concept. The next two sections discuss these strategies.

‘As Is’: The Mid-Term Strategy
The mid-term strategy would use SBAS “as is,” i.e., with no modifications to the SBAS message structure or to the ground segment infrastructure. The strategy is based on SBAS capability of providing differential corrections and system integrity information, as well as navigation performance comparable to that of DGNSS.

Based on these considerations, SBAS could be immediately promoted as a complement or a back-up to DGNSS. As indicated in Table 3, the interoperability between SBAS and DGNSS is expected to be beneficial especially for critical navigation phases of the WWRNS (harbor entrance, harbor approach, and coastal waters).

The use of SBAS should be encouraged through the development of standards that are specifically tailored for maritime. Table 4 describes some of the major issues associated with such an effort. For each aspect, the table lists the underlying problem (open issue) and outlines a possible solution. These (and other) issues will need to be investigated in close cooperation with the maritime community. The objective is to make the best use of the available SBAS performance in compliance with the WWRNS operational requirements.

Maritime Integrity: The Long-Term Strategy
The objective of the long-term strategy is the development of an integrity concept precisely tailored for maritime applications. This concept may benefit from the use of SBAS corrections, together with the relevant system integrity information. Depending on the specific needs expressed by the maritime community for various applications, the concept might eventually allow for protection-level calculations at the user receiver, thereby offering integrity at the user level.

This integrity concept is currently expected to be developed in the framework of a resilient PNT system, where (augmented) GNSS observations are used in conjunction with additional measurements available from other terrestrial-based RNSs and/or from on-board inertial sensors (e.g., inertial measurements units, or IMUs).

In a resilient PNT scheme, the use of differential systems, such as SBAS or DGNSS, is expected to significantly increase the overall resilience level. These systems are actually able to improve the quality of GNSS observations and to provide system integrity information. For that reason, they immediately offer a “certified” protection against possible system-related failures.

If augmented GNSS observations are then combined with additional, non-GNSS measurements, a much more robust navigation solution can be designed in which the user is also protected from the effects of local error sources. As noted, these effects cannot be mitigated by differential systems alone.

At the moment, the high-level architecture of a resilient PNT system is still being defined. However, it already appears clear that the development of a resilient PNT system will take into account future evolutions of SBAS and, in general, of GNSS. Several aspects (and trade-offs) need to be carefully evaluated. These will include, for example, the use of dual-frequency multi-constellation (DFMC) measurements (together with the relevant corrections, if available) and the method (i.e., loosely, tightly, or deeply coupled) used to combine (augmented) GNSS observations and inertial measurements.

Conclusions and Way Forward
The use of SBAS corrections in maritime receivers is currently not regulated. This article discussed two possible strategies for the development of a dedicated standard. These are based on the operational requirements specified by the IMO for a component of the WWRNS and for future GNSS, respectively.

Operational requirements for a component of the WWRNS are indicated in the IMO A.1046(27) resolution. These requirements call for the provision of integrity at the system level only and do not consider possible performance degradation due to local error sources. At the moment, only GPS, GLONASS, and BeiDou are recognized as components of the WWRNS. However, these systems need augmentation to perform the most critical operations, i.e., navigation in harbor entrances, harbor approaches, and coastal waters.

DGNSS is the most used solution for augmentation. It can provide both differential corrections and immediate alerts in the event of a system-related failure. An SBAS is able to effectively provide the same type of information. It could be, therefore, immediately promoted to complement DGNSS in all the situations where augmentation is needed. Although some open issues (e.g., SBAS/DGNSS interoperability) should be unambiguously solved through standardization activities, the use of SBAS as a complementary augmentation system could be promoted in the medium term.

Operational requirements for future GNSS are indicated in the IMO A.915(22) resolution. These requirements call for the provision of integrity at the user level as defined in this article. However, no GNSS system is designed to fulfill IMO A.915(22) in its current version, and there are no maritime operations that require compliance with it.

Depending on the actual needs expressed by the maritime community for different applications, this integrity concept might be developed in the framework of a resilient PNT receiver, where (augmented) GNSS observations will be used in combination with other measurements available from terrestrial-based RNSs and IMUs. In particular, this approach is expected to provide an increased robustness against the effects due to local error sources, which cannot be tackled by differential systems.

The resilient PNT strategy is one of the technological enablers for the e-Navigation concept, which is currently being promoted by the IMO to increase the safety of maritime navigation. The evolution of this strategy is expected to give due consideration to future developments of SBAS and, in general, of GNSS. For that reason, the European Space Agency, in close collaboration with EC and GSA, is also analyzing potential benefits associated with new techniques such as (augmented) DFMC schemes or the horizontal-advanced receiver autonomous integrity monitoring (H-ARAIM) method.

Disclaimer
The views expressed in this article are solely the opinions of the authors and do not reflect those of the European Space Agency.

Additional Resources
1. Grant, A., Williams, P., Hargreaves, C., and Bransby, M., “Demonstrating the Benefits of Resilient PNT,” Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013), Nashville, Tennessee USA, pp. 598-604, September 2013
2. IALA Guideline No. 1112 on Performance and Monitoring of DGNSS Services in the Frequency Band 283.5 – 325 kHz, Edition 1, Saint Germain en Laye, France, May 2015
3. IALA “World Wide Radio Navigation Plan,” Edition 2, Saint Germain en Laye, France, December 2012
4. ICAO, “Aeronautical Information Services,” Annex 15 to the Convention on International Civil Aviation, 14th Edition, Montreal, Quebec, Canada, July 2013
5. International Electrotechnical Commission, “Maritime Navigation and Radio-communication Equipment and Systems – Global Navigation Satellite Systems (GNSS) – Part 4: Ship-borne DGPS and DGLONASS Maritime Radio Beacon Receiver Equipment – Performance Requirements, Methods of Testing and Required Test Results,” Reference number IEC 61108-4:2004(E), First Edition, Geneva, Switzerland, July 2004
6. IMO, “Recognition of Galileo as a component of the WWRNS – Galileo GNSS provision of initial services,” sub-committee on Navigation, Communications and Search and Rescue, 3rd Session, Agenda Item 5, NCSR 3/5, London, UK, December 10, 2015
7. IMO, “Revised Maritime Policy and Requirements for a Future Global Navigation Satellite System (GNSS),” Resolution A.915(22), London, United Kingdom, January 22, 2002
8. IMO, “Report of the Maritime Safety Committee on its eighty-fifth session (MSC 85/26),” London, United Kingdom, December 19, 2008
9. IMO, “Worldwide Radio-navigation System,” Resolution A.1046(27), London, United Kingdom, December 20, 2011
10. International Convention for the Safety of Life at Sea (SOLAS), London, United Kingdom, 1974
11. Kvam, P. E. and Jeannot, M., “The Arctic Test Bed – Providing GNSS Services in the Arctic Region,” Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013), Nashville, Tennessee USA, September 2013
12. RTCA, “Minimum Operational Performance Standards for Global Positioning System/Wide Area Augmentation System Airborne Equipment,” RTCA DO-229D, Washington, D.C., December 13, 2006
13. UK Offshore Operators Association (UKOOA), Surveying and Positioning Committee, “Guidelines for the Use of DGPS in Offshore Surveying,” Issue 1, London, United Kingdom, September 1994
14. USCG, “Coast Guard Navigation Standards Manual,” COMDTINST M3530.2E, Washington, D.C., March 2016
15. Werner, W., Rossbach, U., and Wolf, R., “Algorithms and Performance of the EGNOS CPF Independent Check Set,” Proceedings of ION GPS 2000, Salt Lake City, Utah USA, September 2000

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The Arctic houses an estimated 90 billion barrels of undiscovered, technically recoverable oil and 44 billion barrels of natural gas liquids according to the U.S. Geological Survey. These potential energy reserves represent 13 percent of the untapped oil in the world.

The Arctic houses an estimated 90 billion barrels of undiscovered, technically recoverable oil and 44 billion barrels of natural gas liquids according to the U.S. Geological Survey. These potential energy reserves represent 13 percent of the untapped oil in the world.

Russia, Canada, and the United States plan to explore the Arctic for extensive drilling soon. At the same time, the Arctic is becoming more accessible to normal shipping because of global climate change. New summer sea lanes have already opened up, and projections of sea ice loss suggest that the Arctic Ocean will likely be free of summer sea ice sometime between 2060 and 2080.

The combination of undiscovered oil and climate change are driving a dramatic increase in the demand for navigation in the Arctic. In this article, we examine different approaches to improve accuracy and enable integrity in the Arctic, including the addition of more satellite-based augmentation system (SBAS) reference stations in or near the Arctic, integration of Iridium satellites with GNSS, and use of multi-constellation GNSS.  

More SBAS Reference Stations
The Arctic is a sensitive environment, and thus navigation should have high integrity. For this reason, we are interested in extending SBAS coverage to serve this region.

At present, none of the three operational SBAS provide meaningful service in the far North. In fact, Figure 1 (see inset photo, above right) shows the current SBAS availability coverage with vertical alert limit (VAL) equal to 35 meters, and horizontal alert limit (HAL) equal to 40 meters.

Figure 1 is based on two of the currently operating SBASes: the U.S. Wide Area Augmentation System (WAAS) and the European Geostationary Navigation Overlay Service (EGNOS).

. . .

For the purposes of our analysis, we assume that all these references stations provide the same measurement quality as current WAAS reference stations. We also assume the availability of continuous user connectivity, that is, the user is always able to receive the SBAS corrections.

Although SBAS GEO coverage is limited in the Arctic, other ways exist with which to maintain the connectivity, such as using low earth orbit (LEO) satellites. We will address this topic in more detail in the next section.

. . .

Iridium for SBAS Messages
The second requirement for ensuring integrity in the Arctic is continuous connectivity — in other words, how the SBAS messages are delivered seamlessly to users. Currently, WAAS uses geosynchronous orbit (GEO) satellites to broadcast error corrections. Because the GEO satellites are located directly above the Earth’s equator, WAAS GEO coverage does not include the Arctic.

. . .

The over-the-pole design of Iridium orbits ensures very good high-elevation satellite visibility in the Arctic. Because Iridium satellites already provide voice and data services to satellite phones and integrated transceivers around the globe, Iridium is a strong candidate for enabling SBAS linkage to Arctic users.

. . .

As a bonus, Iridium satellites could improve the vertical dilution of precision (VDOP) if the Iridium satellites also broadcast ranging signals. VDOP is a measure of how well the positions of the satellites are arranged to generate the vertical component of the positioning solution. Higher VDOP values mean less certainty in the solutions and can be caused if the satellites have low elevation angles in relation to users.

. . .

With added Iridium satellites, the VDOP values increase to 1.6 from 2.1 for 24 GPS satellites, and to 1.3 from 1.8 for 31 GPS satellites. Moreover, the VDOP values are more even over the Earth’s surface. For both scenarios of 24 and 31 GPS satellites, adding Iridium satellites improves VDOPs in the Arctic.

Multiple Constellations for High Availability of Integrity
A third issue, although not as critical as the first two, is the VDOP degradation encountered in the Arctic. Because GPS satellites are in an orbital plane of 55 degree inclination, not enough satellites are visible at high elevation angles for users in the Arctic. For this reason, VDOPs in the Arctic are worse (i.e., higher) than those close to the equator.

. . .

If using only two constellations, adding GLONASS to GPS is the most beneficial combination. GLONASS satellites orbit at 19,100 kilometers (11,842 miles) altitude with a 64.8-degree inclination. Compared to the 55-degree inclination of the GPS orbital planes, the GLONASS constellation produces better coverage in high latitudes. The VDOP improvement in the Arctic is more dramatic using three or even all four constellations.

Conclusion
This article identified a need for high-integrity navigation in the Arctic and analyzed techniques to extend SBAS coverage to this important region. We show that the current network of reference stations can be augmented to provide Arctic integrity with high availability. Moreover, Iridium satellites could provide a broadcast channel to the SBAS users. Multiple GNSS constellations significantly improve VDOPs and thus reduce vertical positioning errors in the Arctic.

For the complete story, including figures, graphs, and images, please download the PDF of the article, above.

Acknowledgment
The authors would like to thank the Federal Aviation Administration’s Satellite Navigation Program Office and the Boeing Company for supporting this research.

Additional Resources
[1] Evans, J.V., “Satellite Systems for Personal Communications,” Proceedings of the IEEE, Volume: 86, Issue: 7, 1998
[2] United States Geological Survey, “90 Billion Barrels of Oil and 1,670 Trillion Cubic Feet of Natural Gas Assessed in the Arctic,” USGS, July 2008

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The Raytheon Company has announced that it successfully completed the final system acceptance test to augment standard GPS signals over India.

GAGAN stands for GPS Aided GEO Augmented Navigation-Technology Demonstration System. It monitors GPS satellite signals for errors and then generates correction messages to improve positioning accuracy for users.

The Raytheon Company has announced that it successfully completed the final system acceptance test to augment standard GPS signals over India.

GAGAN stands for GPS Aided GEO Augmented Navigation-Technology Demonstration System. It monitors GPS satellite signals for errors and then generates correction messages to improve positioning accuracy for users.

The system also augments standard GPS signals to support international and domestic flights during approach, at the terminal and on the ground. The process enhances the accuracy and integrity of flight navigation aids.

The latest test, conducted late in 2007, demonstrated that ground elements could successfully integrate with a geosynchronous satellite over India and generate a test signal that conformed to international requirements for the Indian flight information region.

The test also demonstrated that the time from signal generation to transmission to the satellite and reception back on the ground was less than the 6.2-second requirement.

After this test, the company said that the Indian Space Research Organization and Airports Authority of India would begin expanding the existing ground network, add redundancy, and produce the certification analysis and documentation for safety-of-flight commissioning.

Similar satellite-based GPS augmentation systems include the Wide Area Augmentation System (WAAS) in North America, the European Geostationary Navigation Overlay Service (EGNOS) and the MTSAT-based Satellite Augmentation system (MSAS) in Japan.

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