A New Navigation Alfgorithm Using Only Information From Measurement

Benlin Xu

Abstract:	This is a new self-learning navigation algorithm based on probability space and non-Newtonian dynamics. This new algorithm relies solely on the measurements on a vehicle: positions, velocities and their error statistics. The basic idea behind this new algorithm is twofold: (1) A cluster of the observed positions contain true kinematic information of the vehicle, (2) A process model associated with the error statistics of the positions is able to squeeze, to a large extent, the information off for use. We base the new algorithm on an analogy. We consider the statistical confidence regions of the position fixes as “sources” tending to “attract” the undetermined trajectory to pass through these regions. With these position fixes and their error statistics, an imaginary real-time potential field is constructed in which an imaginary mass particle is forced to move. To make the new algorithm be flexible to a changing navigation environment, we leave some parameters unfixed and let the algorithm determine them using a sequence of observations and the criterion of least square errors of the observation. By all the above efforts, the trajectory of the imaginary particle can be a good representative path of the vehicle. The new navigation algorithm has been tested with both simulated and real navigation data, as an estimator, predictor, smoother and blunder detector. The test results have demonstrated that the new algorithm has some advantages over Kalman filter, but with a slower processing speed.
Published in:	Proceedings of the 8th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 1995) September 12 - 15, 1995 Palm Springs, CA
Pages:	309 - 318
Cite this article:	Xu, Benlin, "A New Navigation Alfgorithm Using Only Information From Measurement," Proceedings of the 8th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 1995), Palm Springs, CA, September 1995, pp. 309-318.
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