A Self-adaptive Unscented Kalman Filtering for Underwater Gravity Aided Navigation

L. Wu, J. Ma, J. Tian

Abstract:	In this paper, a self-adaptive unscented Kalman filtering for underwater gravity aided navigation is constructed. It is more accurate and far easier to implement than an extended Kalman filter. Then the novel navigation algorithm based on the self-adaptive unscented Kalman filter is explored. With this method submerged position fixes for autonomous underwater vehicle can be obtained from comparing gravity fields’ measurements with gravity maps. Specifically, simulation results show that navigation errors can be reduced more effectively and efficiently by the presented algorithm.
Published in:	Proceedings of IEEE/ION PLANS 2010 May 4 - 6, 2010 Renaissance Esmeralda Resort & Spa Indian Wells, CA
Pages:	142 - 145
Cite this article:	Wu, L., Ma, J., Tian, J., "A Self-adaptive Unscented Kalman Filtering for Underwater Gravity Aided Navigation," Proceedings of IEEE/ION PLANS 2010, Indian Wells, CA, May 2010, pp. 142-145. https://doi.org/10.1109/PLANS.2010.5507294
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