Mitigating the Effects of GNSS Multipath with a Coded Filter

Shan Mohiuddin, Donald E. Gustafson, and Yaron Rachlin

Abstract:	The Charles Stark Draper Laboratory has developed novel estimation algorithms that bring recent sparse signal reconstruction techniques from compressive sensing, a rapidly developing field in applied mathematics, to bear on traditional navigation problems. The new estimation methods are applied to the problem of detecting, estimating, and removing measurement and process errors that are poorly modeled as Gaussian noise. More specifically, the paper focuses on mitigating the effects of GNSS multipath errors. The two key insights that connect compressive sensing ideas to the problem of multipath mitigation are to recognize that multipath is sparse (or compressible) in some appropriately chosen basis—the canonical basis per time or the wavelet basis over time—and that a traditional Kalman filtering or smoothing problems can be cast as channel coding problems. In this framework, fast convex optimization solvers are employed to solve constrained 1-norm minimization problems to estimate and remove the sparse multipath signals. The results show that the new algorithms outperform standard algorithms by a significant margin in simulated and live-sky multipath scenarios.
Published in:	Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011) September 20 - 23, 2011 Oregon Convention Center, Portland, Oregon Portland, OR
Pages:	2381 - 2394
Cite this article:	Mohiuddin, Shan, Gustafson, Donald E., Rachlin, Yaron, "Mitigating the Effects of GNSS Multipath with a Coded Filter," Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011), Portland, OR, September 2011, pp. 2381-2394.
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