Multi-Sampling-Based Distribution Simulation for Nonlinear Measurement Error Mitigation

Yong Hun Kim, Joo Han Lee, Bo Sung Ko, Kyeong Wook Seo, Eung Ju Kim, and Jin Woo Song

Abstract:	In this study, we propose a technique to mitigate estimation errors caused by nonlinear measurements. The proposed technique considers the well-known measurement noise variance and innovation as individual samples to form their respective distributions. The calculated distribution is changed to a normalized weight to compensate for the observed measurement error, and a pseudo-measurement is calculated and used as the measurement update. Additionally, the noise variance of the pseudo-measurement is newly calculated using likelihood optimization. As a result, it was confirmed that when using the proposed technique, stable performance is achieved even if non-linear measurement errors exist for a certain period of time. In particular, it was confirmed that the proposed technique converges to the true value faster than existing filters when nonlinear errors exist in measured values and return to normal. The proposed method can construct not much change to the conventional filter as only uses predicted value and observed measurement.
Published in:	Proceedings of the 2024 International Technical Meeting of The Institute of Navigation January 23 - 25, 2024 Hyatt Regency Long Beach Long Beach, California
Pages:	1158 - 1168
Cite this article:	Kim, Yong Hun, Lee, Joo Han, Ko, Bo Sung, Seo, Kyeong Wook, Kim, Eung Ju, Song, Jin Woo, "Multi-Sampling-Based Distribution Simulation for Nonlinear Measurement Error Mitigation," Proceedings of the 2024 International Technical Meeting of The Institute of Navigation, Long Beach, California, January 2024, pp. 1158-1168. https://doi.org/10.33012/2024.19569
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