Sensor Selection for Estimation with Correlated Measurement Noise

TL;DR

This paper develops a novel sensor selection framework that accounts for correlated measurement noise, providing optimal activation strategies and extending to sensor scheduling with proven effectiveness through numerical experiments.

Contribution

It derives a closed-form Fisher information matrix valid for any noise correlation and proposes convex and greedy algorithms for sensor selection and scheduling.

Findings

The proposed methods outperform existing approaches in correlated noise scenarios.

Noise correlation significantly impacts sensor selection and estimation accuracy.

Numerical results demonstrate the effectiveness of the algorithms in various settings.

Abstract

In this paper, we consider the problem of sensor selection for parameter estimation with correlated measurement noise. We seek optimal sensor activations by formulating an optimization problem, in which the estimation error, given by the trace of the inverse of the Bayesian Fisher information matrix, is minimized subject to energy constraints. Fisher information has been widely used as an effective sensor selection criterion. However, existing information-based sensor selection methods are limited to the case of uncorrelated noise or weakly correlated noise due to the use of approximate metrics. By contrast, here we derive the closed form of the Fisher information matrix with respect to sensor selection variables that is valid for any arbitrary noise correlation regime, and develop both a convex relaxation approach and a greedy algorithm to find near-optimal solutions. We further extend our framework of sensor selection to solve the problem of sensor scheduling, where a greedy algorithm is proposed to determine non-myopic (multi-time step ahead) sensor schedules. Lastly, numerical results are provided to illustrate the effectiveness of our approach, and to reveal the effect of noise correlation on estimation performance.