A Novel Sparsity-Based Approach to Recursive Estimation of Dynamic Parameter Sets

TL;DR

This paper introduces a sparsity-based Bayesian recursive estimation method for dynamic parameter sets, effectively handling unreliable observations and rapid parameter changes, with superior performance in low SNR scenarios.

Contribution

A new Bayesian model and recursive filtering algorithm leveraging sparsity for dynamic parameter estimation, outperforming existing methods in challenging conditions.

Findings

Outperforms state-of-the-art algorithms in low SNR scenarios

Maintains computational efficiency with approximate Bayesian filtering

Effective in tracking moving targets with unreliable observations

Abstract

We consider the problem of estimating a variable number of parameters with a dynamic nature. A familiar example is finding the position of moving targets using sensor array observations. The problem is challenging in cases where either the observations are not reliable or the parameters evolve rapidly. Inspired by the sparsity based techniques, we introduce a novel Bayesian model for the problems of interest and study its associated recursive Bayesian filter. We propose an algorithm approximating the Bayesian filter, maintaining a reasonable amount of calculations. We compare by numerical evaluation the resulting technique to state-of-the-art algorithms in different scenarios. In a scenario with a low SNR, the proposed method outperforms other complex techniques.