Adaptive Noise Covariance Estimation under Colored Noise using Dynamic Expectation Maximization

TL;DR

This paper presents a brain-inspired, adaptive algorithm based on Dynamic Expectation Maximization for accurately estimating noise covariance matrices in systems with colored noise, outperforming existing methods.

Contribution

It extends the Dynamic Expectation Maximization algorithm to jointly estimate noise covariance and states in colored noise environments, with proven convergence and superior performance.

Findings

Outperforms nine baseline methods in estimation accuracy

Proven convergence to the global optimum of the free energy

Excels in high colored noise conditions

Abstract

The accurate estimation of the noise covariance matrix (NCM) in a dynamic system is critical for state estimation and control, as it has a major influence in their optimality. Although a large number of NCM estimation methods have been developed, most of them assume the noises to be white. However, in many real-world applications, the noises are colored (e.g., they exhibit temporal autocorrelations), resulting in suboptimal solutions. Here, we introduce a novel brain-inspired algorithm that accurately and adaptively estimates the NCM for dynamic systems subjected to colored noise. Particularly, we extend the Dynamic Expectation Maximization algorithm to perform both online noise covariance and state estimation by optimizing the free energy objective. We mathematically prove that our NCM estimator converges to the global optimum of this free energy objective. Using randomized numerical simulations, we show that our estimator outperforms nine baseline methods with minimal noise covariance estimation error under colored noise conditions. Notably, we show that our method outperforms the best baseline (Variational Bayes) in joint noise and state estimation for high colored noise. We foresee that the accuracy and the adaptive nature of our estimator make it suitable for online estimation in real-world applications.