Adaptive square-root unscented particle filtering algorithm for dynamic navigation

Wei, W, Gao, S, Zhong, Y, Gu, C and Hu, G 2018, 'Adaptive square-root unscented particle filtering algorithm for dynamic navigation', Sensors, vol. 18, no. 7, pp. 1-15.

Document type: Journal Article
Collection: Journal Articles

Title Adaptive square-root unscented particle filtering algorithm for dynamic navigation
Author(s) Wei, W
Gao, S
Zhong, Y
Gu, C
Hu, G
Year 2018
Journal name Sensors
Volume number 18
Issue number 7
Start page 1
End page 15
Total pages 15
Publisher MDPIAG
Abstract This paper presents a new adaptive square-root unscented particle filtering algorithm by combining the adaptive filtering and square-root filtering into the unscented particle filter to inhibit the disturbance of kinematic model noise and the instability of filtering data in the process of nonlinear filtering. To prevent particles from degeneracy, the proposed algorithm adaptively adjusts the adaptive factor, which is constructed from predicted residuals, to refrain from the disturbance of abnormal observation and the kinematic model noise. Cholesky factorization is also applied to suppress the negative definiteness of the covariance matrices of the predicted state vector and observation vector. Experiments and comparison analysis were conducted to comprehensively evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm exhibits a strong overall performance for integrated navigation systems.
Subject Automation and Control Engineering
Keyword(s) Adaptive filtering
Cholesky factorization
Integrated navigation
Particle filter
Performance analysis
DOI - identifier 10.3390/s18072337
Copyright notice © 2018 by the authors. Licensee MDPI, Basel, Switzerland. e Creative Commons Attribution (CC BY) license
ISSN 1424-8220
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Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
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