Augmented sigma-point lagrangian splitting method for sparse nonlinear state estimation
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Journal ISSN
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A4 Artikkeli konferenssijulkaisussa
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Author
Date
2020
Major/Subject
Mcode
Degree programme
Language
en
Pages
5
2090-2094
2090-2094
Series
28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
Abstract
Nonlinear state estimation using Bayesian filtering and smoothing is still an active area of research, especially when sparsity-inducing regularization is used. However, even the latest filtering and smoothing methods, such as unscented Kalman filters and smoothers and other sigma-point methods, lack a mechanism to promote sparsity in estimation process. Here, we formulate a sparse nonlinear state estimation problem as a generalized L1-regularized minimization problem. Then, we develop an augmented sigma-point Lagrangian splitting method, which leads to iterated unscented, cubature, and Gauss-Hermite Kalman smoothers for computation in the primal space. The resulting method is demonstrated to outperform conventional methods in numerical experimentals.Description
Keywords
Kalman filter, Nonlinear state estimation, Sigma-point, Sparsity, Variable splitting
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Citation
Gao, R & Särkkä, S 2020, Augmented sigma-point lagrangian splitting method for sparse nonlinear state estimation . in 28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings ., 9287731, European Signal Processing Conference, European Association For Signal and Imag Processing, pp. 2090-2094, European Signal Processing Conference, Amsterdam, Netherlands, 24/08/2020 . https://doi.org/10.23919/Eusipco47968.2020.9287731
