have also integrated alpha and beta sensors to infer the ambient wind vector in real time. The velocity and angular velocity solutions are inferred from phase rate (doppler) signals extracted from the phase locked loop within the receiver. Phase is the fundamental observable, and the phase rate signal is necessarily more noisy. This in turn leads to velocity and angular velocity solutions that are equivalently more noisy than the analogous position or attitude solution. The noise in all measurements may be reduced at the expense of tracking performance by reducing the bandwidth of the phase state estimator. The mobile receiver must have adequate bandwidth to track the dynamics of the vehicle but a stationary reference receiver can and should be filtered more heavily. The aircraft we controlled requires stability augmentation for phugoid, dutch roll and spiral divergence modes. Dutch roll and phugoid are both lightly damped with periods of approximately 3s and 10s respectively. The fastest mode is short period, which is naturally well damped at a frequency of 1 Hz. We set the phase state estimator bandwidth to 2 Hz. This gives adequate bandwidth to track the aircraft dynamics, while admitting a minimum of noise. In our experience the attitude and angular velocity solution is much more robust than the position solution. The principal reason is that there are many more measurements available. With our 6 channel receivers, there are upto 18 measurements to determine the 3 attitude unknowns versus a maximum of 6 to determine the 4 unknowns required for a position solution. To add robustness in the position solution one may consider using additional ranging signals such as pseudolites. Generally this will require having a receiver with more than 6 channels. It also complicates the surveying and setup and adds non-linearity and near/far issues. An alternative is to use a number of antennae and switch between them to keep the sky in continuous view. These are areas of ongoing research.

ACKNOWLEDGEMENTS

The authors would like to thank the FAA for ongoing funding of this project and Trimble Navigation for discount hardware and access to their receiver code. Particular thanks must go to Dr Andrew Conway of the ARL at Stanford for his collaborative effort in developing software used in this project [6]. We would also like to thank Dr Andreas Nowatzyk for his great help in solving the many radio interference problems that plagued us during systems integration.

REFERENCES

[1] Clark Emerson Cohen. Attitude Determination Using GPS. PhD thesis, Stanford University, Department of Aeronautics and Astronautics, December 1992.

[2] A. E. Bryson. Control of Spacecraft and Aircraft. AA271B Class Notes, Department of Aeronautics and Astronautics 1992, Stanford University.

[3] B Pervan C Cohen and B Parkinson. Integrity Monitoring for Precision Approach using Kinematic GPS and a Ground-Based Pseudolite. Navigation, Volume 41 No.2 Summer 1994.

[4] C E Cohen et al. Preliminary Results of Cat. III Precision Landing with 110 Automatic Landings of a United Boeing 737 Using GNSS Integrity Beacons. ION National Technical Meeting, Anaheim, California, January 1995.

[5] C Cohen B Pervan D Lawrence S Cobb D Powell B Parkinson. Real-Time Flight Testing Using Integrity Beacons for GPS Category III Precision Landing. Navigation, Vol 41 No.2 Summer 1994.

[6] A Conway. Autonomous Control of a model Helicopter using carrier phase GPS. PhD thesis, Stanford University, Department of Electrical Engineering, February 1995.

[7] P Montgomery H Uematsu B Parkinson. Full state estimation and control of a large model aircraft using carrier differential GPS. Proceedings: DSNS April 1995. Norway.

[8] P Montgomery, H Uematsu and B Parkinson. Analysis of angular velocity determination using GPS. Proceedings: ION GPS-94 Sept 1994. Salt Lake City.

[9] A Conway P Montgomery S Rock R Cannon and B Parkinson. A new motion based algorithm for GPS attitude integer resolution. in review: contact paulm@relgyro.stanford.edu



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