Autonomous Tracking and State Estimation with Generalised Group Lasso

TL;DR

This paper introduces a novel structured sparsity-based estimation framework using generalized group Lasso, improving accuracy in autonomous tracking of marine vessels and vehicles over classical Bayesian methods.

Contribution

It formulates the tracking problem as a dynamic generalized group Lasso and develops efficient smoothing-and-splitting algorithms with convergence guarantees.

Findings

Effective in large-scale problems without pre-processing.

Handles nonsmooth nonconvex optimization.

Proven convergence to stationary points.

Abstract

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 classical Bayesian filters and smoothers. We formulate the estimation problem as a dynamic generalised group Lasso problem and develop a class of smoothing-and-splitting methods to solve it. The Levenberg--Marquardt iterated extended Kalman smoother-based multi-block alternating direction method of multipliers (LM-IEKS-mADMM) algorithms are based on the alternating direction method of multipliers (ADMM) framework. This leads to minimisation subproblems with an inherent structure to which three new augmented recursive smoothers are applied. Our methods can deal with large-scale problems without pre-processing for dimensionality reduction. Moreover, the methods allow one to solve nonsmooth nonconvex optimisation problems. We then prove that under mild conditions, the proposed methods converge to a stationary point of the optimisation problem. By simulated and real-data experiments including multi-sensor range measurement problems, marine vessel tracking, autonomous vehicle tracking, and audio signal restoration, we show the practical effectiveness of the proposed methods.