Augmented sigma-point lagrangian splitting method for sparse nonlinear state estimation

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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.

Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
Number of pages5
ISBN (Electronic)9789082797053
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference - Amsterdam, Netherlands
Duration: 24 Aug 202028 Aug 2020


ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO


  • Kalman filter
  • Nonlinear state estimation
  • Sigma-point
  • Sparsity
  • Variable splitting

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