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Augmented sigma-point lagrangian splitting method for sparse nonlinear state estimation
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A4 Artikkeli konferenssijulkaisussa
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en
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5
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28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings, pp. 2090-2094, European Signal Processing Conference
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.
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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 Image Processing, pp. 2090-2094, European Signal Processing Conference, Amsterdam, Netherlands, 24/08/2020. https://doi.org/10.23919/Eusipco47968.2020.9287731