Autonomous Tracking and State Estimation With Generalized Group Lasso

Rui Gao*, Simo Särkkä, Rubén Claveria-Vega, Simon Godsill

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

Abstrakti

We address the problem of autonomous tracking and state estimation for marine vessels, autonomous vehicles, and other dynamic signals under a (structured) sparsity assumption. The aim is to improve the tracking and estimation accuracy with respect to the classical Bayesian filters and smoothers. We formulate the estimation problem as a dynamic generalized group Lasso problem and develop a class of smoothing-and-splitting methods to solve it. The Levenberg-Marquardt iterated extended Kalman smoother-based multiblock alternating direction method of multipliers (LM-IEKS-mADMMs) algorithms are based on the alternating direction method of multipliers (ADMMs) framework. This leads to minimization subproblems with an inherent structure to which three new augmented recursive smoothers are applied. Our methods can deal with large-scale problems without preprocessing for dimensionality reduction. Moreover, the methods allow one to solve nonsmooth nonconvex optimization problems. We then prove that under mild conditions, the proposed methods converge to a stationary point of the optimization problem. By simulated and real-data experiments, including multisensor range measurement problems, marine vessel tracking, autonomous vehicle tracking, and audio signal restoration, we show the practical effectiveness of the proposed methods.
AlkuperäiskieliEnglanti
Sivumäärä15
JulkaisuIEEE Transactions on Cybernetics
DOI - pysyväislinkit
TilaSähköinen julkaisu (e-pub) ennen painettua julkistusta - 24 kesäkuuta 2021
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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