This paper describes methods for using dual‐frequency GPS data to improve the accuracy of carrier‐smoothed code pseudorange measurements by eliminating the limitations imposed by ionospheric divergence. With these techniques, the steady‐state time constant used in the smoothing filter can be vastly extended as long as continuous carrier tracking is maintained. When used for Differential GPS (DGPS), the smoothing requirements for the reference and remote receivers can be decoupled since there will be no need to contend with ionospheric divergence. A new DGPS architecture that exploits these dual‐frequency smoothing techniques to improve the accuracy of code differential systems is presented. Applications of these techniques to stand‐alone GPS navigation are also discussed.

ABSTRACT

In civil aviation, it is desirable to be able to detect navigation system failures quickly and alert the pilot and crew accordingly. Also, while not absolutely necessary, it certainly would be simpler in the GPS case if the detection could be done autonomously within the cockpit rather than via a communication link with a network of monitoring stations on the ground. There is some redundancy in the GPS system, even if only four satellites are in view. This can be further enhanced with the addition of baro‐altitude information, and it is the system redundancy that makes autonomous failure detection possible. The scheme used for failure detection in this investigation is the Magill adaptive filter, which is also known as the Multiple Model Estimation Algorithm (MMEA) in control theory. Preliminary results indicate that soft ramp‐type satellite clock failures can be detected and identified in a relatively short time for the low dynamic environment that might be encountered in the high‐altitude enroute situation. Failure detection is more difficult in the low‐altitude nonprecision approach scenario, but preliminary results indicate that this can also be done.

The notion of aiding GPS code‐ranging information with carrier phase measurements has been around for quite some time. The integration of these two complementary forms of data can provide highly precise position solutions corrupted by very little high‐frequency error, which is advantageous particularly for high‐dynamical situations. This paper presents two schemes, based on the Kalman filter, for integrating GPS code and carrier measurements efficiently. One of these schemes involves a general combination of the two types of measurements, where the vehicle motion is described as a random process. In the other, the continuous carrier phase measurements provide a dynamically exact reference trajectory in much the same manner as in an inertial system, and the pseudorange data is then used to update this reference trajectory via a Kalman filter. This method of integration looks especially attractive as a means of achieving good dynamical response while still retaining the benefits of filtering the pseudorange data.

In kinematic GPS, the initial integer ambiguity must be resolved either by a static survey over time, or by instant calibration with a known baseline or an antenna exchange. While these standard methods require maintaining a baseline stationary to an earth-fixed reference frame during the initialization, there are situations when at least one of the receivers may be constantly in motion. This paper proposes two ideas for adapting standard kinematic techniques to situations that do not naturally allow for the constraint of a fixed baseline. The first calls for extracting the information needed to resolve the integer ambiguity from the very data collected while the kinematic survey is in progress. The second idea addresses the use of the antenna exchange technique for mobile platforms where the original locations of the antennas are not likely to remain stationary during the physical exchange. Both ideas count on information from additional measurements to augment their respective measurement models.