B: Applications

Precise Point Positioning and Its Challenges, Aided-GNSS and Signal Tracking

Q: What is precise point positioning (PPP), and what are its requirements, advantages and challenges?

A: Precise point positioning (PPP) is a method that performs precise position determination using a single GPS receiver.

This positioning approach arose from the advent of widely available precise GPS orbit and clock data products with centimeter accuracy. These data can be applied to substantially reduce the errors in GPS satellite orbits and clocks, two of the most significant error sources in GPS positioning.

Q: What is precise point positioning (PPP), and what are its requirements, advantages and challenges?

A: Precise point positioning (PPP) is a method that performs precise position determination using a single GPS receiver.

This positioning approach arose from the advent of widely available precise GPS orbit and clock data products with centimeter accuracy. These data can be applied to substantially reduce the errors in GPS satellite orbits and clocks, two of the most significant error sources in GPS positioning.

Combining precise satellite positions and clocks with a dual-frequency GPS receiver (to remove the first order effect of the ionosphere), PPP is able to provide position solutions at centimeter to decimeter level, which is appealing to many applications such as airborne mapping. PPP is different from double-difference RTK (real-time kinematic) positioning that requires access to observations from one or more base stations with known coordinates. The word “precise” is also used to distinguish it from the conventional point positioning techniques that use only code or phase-smoothed code as the principal observable for position determination.

(For the rest of Yang Gao’s answer to this question, please download the complete article using the PDF link above.)

Q: Does Aided-GNSS improve signal acquisition, tracking, or both?

A: A-GPS (Aided/Assisted-GPS) and more recently its extensions, A-GNSS, have been introduced to substitute for missing satellite broadcast data when access is intermittent, difficult, or impossible due to signal obstruction. It has expanded the capabilities of the traditional receiver in reducing the time to first fix (TTFF), enabling “high sensitivity” modes, improving the performance in urban canyons and indoors, and incidentally, boosting the receiver’s efficient use of power.

Multiple ways have been developed to deploy an A-GPS server, and to distribute the process flow between the server and the mobile. The mobile station–based method places the position determination in the receiver, while the network-based method relegates it back to the server.

Other notable factors influence the architecture. The assistance can be one-way, where information accessible at the server flows down to the receiver, or in closed loop, where the information is uploaded to the server, processed remotely applying far larger computational resources and extra knowledge not available to the receiver, and then pushed back to the receiver in its final form.

We will now introduce two simple rules that will illustrate the rest of the explanations:

Rule 1: For A-GPS to be practical, the assistance information should not be stale when ready to be used at the mobile. In more technical terms, the assistance information persistence needs to be longer than the sum of network latency plus server and mobile processing times,

Rule 2: In a closed loop architecture, the information collected at the mobile, and processed at the server should be returned fast enough before the internal state of the mobile changes too much. We can reformulate it as the sum of the round trip network delay plus server and mobile processing times have to be shorter than the process time constant to control the mobile.

(For the rest of Lionel J. Garin’s answer to this question, please download the complete article using the PDF link above.)

A New Version of the RTCM SC-106 Standard, the Probability of Solving Integer Ambiguities

Q: The RTCM has announced a new version of its widely used differential GPS (DGPS) standard. Why did the group decide a new standard — Version 3 — was needed, and what are the benefits compared to Version 2?

Q: The RTCM has announced a new version of its widely used differential GPS (DGPS) standard. Why did the group decide a new standard — Version 3 — was needed, and what are the benefits compared to Version 2?

A: Initially, the RTCM (Radio Technical Commission for Maritime Services) Subcommittee (SC104) Version 2 standard was developed with marine DGPS as the target application. One of the design goals for the RTCM SC 104 standard was to have correctional information readily available for user equipment. The outlines of the messages were specifically tailored for low bit rate data links.

Not until the beginning of the 1990s did RTK (real-time kinematic) surveying applications come into focus for RTCM. The committee drafted new RTK messages based on the proven DGPS messages. Although the DGPS messages only contain corrections, the RTK messages also allow transmission of raw observables from the satellite signals. RTCM tentatively published redundant means of disseminating precise RTK information with the aim of gaining practical experience during their implementation.

The first implementations by different manufacturers had diverse interoperability issues. For instance, various manufacturers have different sign conventions for representing the carrier phase observations, which resulted in incompatibilities when mixing receivers of different manufacturers.

(For the rest of Dr. Hans-Jürgen Euler’s answer to this question, please download the complete article using the PDF link above.)

Q: What is the probability of correctly resolving integer ambiguities and how can it be evaluated?

A: Resolving, or “fixing”, carrier phase ambiguities to integer values is ultimately based on statistical assumptions and testing. As such, a probability is associated with resolving any particular ambiguity correctly. Evaluating the probability of correct fix (PCF), that is, the probability that the ambiguities are fixed to the correct integer values, is particularly important for safety-critical applications where an incorrect ambiguity fix would produce hazardously misleading information (HMI).

In fixed-ambiguity carrier phase processing, the usual procedure is to begin by estimating the carrier phase ambiguities as real-valued (“float”) parameters and then to determine their integer values. A difficulty with this method is that, although the least-squares adjustment or Kalman filter used to estimate the real-valued ambiguities provides an estimate of their quality (a covariance matrix), it is not obvious how to obtain an estimate of the quality of the integer ambiguities.

In most carrier phase ambiguity estimation methods, integer quality is validated using some sort of statistical test. These generally involve testing the least-squares sum-squared residuals of the best fitting integer solution against the second best fitting solution. The test statistic is then compared against a threshold value, the idea being that if the best solution is sufficiently better than the second-best solution, then it must be correct.

(For the rest of Kyle O’Keefe and Mark Petovello’s answer to this question, please download the complete article using the PDF link above.)