Navigation and Estimation Improvement by Environmental-Driven Noise Mode Detection

Jindrich Dunik, Oliver Kost, Ondrej Straka, Erik Blasch

Abstract: This paper deals with the state estimation of nonlinear stochastic dynamic systems, where the measurement noise is modelled by the multimodal Gaussian sum probability density function. The multimodal density is able to sufficiently capture various environmental features and phenomena affecting sensor readings. The design particularly focuses on the environmental-driven detector of the measurement noise mode for the terrain-aided navigation using a point-mass filter, which allows reducing the overall measurement noise variance used by the estimator and, consequently, decreasing the estimator or navigator error. Throughout the paper, the detector design and validation are illustrated with the help of a terrain-aided navigation system.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 925 - 932
Cite this article: Dunik, Jindrich, Kost, Oliver, Straka, Ondrej, Blasch, Erik, "Navigation and Estimation Improvement by Environmental-Driven Noise Mode Detection," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 925-932.
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