Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach

TL;DR

This paper introduces a robust optimization algorithm based on Majorization-Minimization for state-space model estimation, effectively handling heavy-tailed noise and constraints to improve accuracy and efficiency.

Contribution

It proposes a novel MM-based framework that unifies robust and constrained state estimation, overcoming limitations of existing methods.

Findings

High accuracy in noisy environments

Enhanced robustness to outliers

Computational efficiency demonstrated

Abstract

In this paper, we present a novel optimization algorithm designed specifically for estimating state-space models to deal with heavy-tailed measurement noise and constraints. Our algorithm addresses two significant limitations found in existing approaches: susceptibility to measurement noise outliers and difficulties in incorporating constraints into state estimation. By formulating constrained state estimation as an optimization problem and employing the Majorization-Minimization (MM) approach, our framework provides a unified solution that enhances the robustness of the Kalman filter. Experimental results demonstrate high accuracy and computational efficiency achieved by our proposed approach, establishing it as a promising solution for robust and constrained state estimation in real-world applications.