Artificial Intelligence for Optimizing the GNSS Carrier Phase-based Positioning.

H.A. Saleh

Abstract:	In the last two decades model-based on ideas of the Artificial Intelligence (AI) has been an important research area where new methodologies have been proposed, studied and experimented. Today AI is entering its maturity and this is witnessed by the number of applications which have been implemented and deployed, and by those which are currently under investigation. Within the Global Navigation Satellite Systems (GNSS) technology, the ideas of AI have been researched, implemented, investigated and achieved good performance in the designing of the surveying networks. These ideas are dynamically expanded to solve other important applications within the field of GNSS such as optimal orbit determination, modeling of atmospheric effects and ambiguity resolution, etc. In this paper, metaheuristic techniques, which are often based upon ideas from AI, have been proposed and implemented to efficiently provide flexible and computerized procedures for optimizing the resolution of GNSS ambiguity problem. Metaheuristic techniques from the field of Operational Research (OR) are applicable to a wide range of important problems that occur in a variety of disciplines, such as statistics and engineering. OR attempts to provide a systematic and rational approach to the fundamental elements involved in the control of a problem by making decisions which, in some sense, achieves the best results in light of all the information that is available. In most OR applications, researchers are more interested in finding a “good enough” solution than proving the optimality. “Good enough” may of course mean “very good, and much better than would be found by other means”. In discussing the optimality of a solution it is important to realize what is the aim of optimization. Is it seeking the cheapest or the fastest solution, or, that which is most acceptable to use, or, simply any solution that satisfies certain conditions? For example, the aim in GNSS surveying network scheduling is to seek the cheapest schedule that satisfies both GNSS and metaheuristic requirements. In GNSS, the optimal determination of the number of ambiguity candidates to be searched and selected is a crucial factor for improving the computational efficiency of GNSS ambiguity resolution. The criterion of acceptability adopted in this research for the GNSS ambiguity resolution will include notions such as “How good is the obtained solution?” and “How much computational effort (i.e., cutting down the search space transformation of the ambiguity) will it save?”
Published in:	Proceedings of the 2003 National Technical Meeting of The Institute of Navigation January 22 - 24, 2003 Disneyland Paradise Pier Hotel Anaheim, CA
Pages:	407 - 416
Cite this article:	Saleh, H.A., "Artificial Intelligence for Optimizing the GNSS Carrier Phase-based Positioning.," Proceedings of the 2003 National Technical Meeting of The Institute of Navigation, Anaheim, CA, January 2003, pp. 407-416.
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