Regularized State Estimation and Parameter Learning Via Augmented Lagrangian Kalman Smoother Method

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Abstract

In this article, we address the problem of estimating the state and learning of the parameters in a linear dynamic system with generalized L1 -regularization. Assuming a sparsity prior on the state, the joint state estimation and parameter learning problem is cast as an unconstrained optimization problem. However, when the dimensionality of state or parameters is large, memory requirements and computation of learning algorithms are generally prohibitive. Here, we develop a new augmented Lagrangian Kalman smoother method for solving this problem, where the primal variable update is reformulated as Kalman smoother. The effectiveness of the proposed method for state estimation and parameter learning is demonstrated in spectro-temporal estimation tasks using both synthetic and real data.

Details

Original languageEnglish
Title of host publicationProceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019
Publication statusPublished - 1 Oct 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Pittsburgh, United States
Duration: 13 Oct 201916 Oct 2019
Conference number: 29

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing
PublisherIEEE
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
CountryUnited States
CityPittsburgh
Period13/10/201916/10/2019

    Research areas

  • Augmented Lagrangian method, Kalman smoother, Parameter learning, Sparsity, State estimation

ID: 40550307