Adaptive Noise Covariance Estimation under Colored Noise using Dynamic Expectation Maximization
This work addresses a critical bottleneck in state estimation and control for dynamic systems affected by colored noise, offering an incremental improvement over existing methods by extending the Dynamic Expectation Maximization algorithm.
The paper tackles the problem of accurately estimating the noise covariance matrix in dynamic systems under colored noise, which is common in real-world applications, and shows that their novel algorithm outperforms nine baseline methods with minimal estimation error, particularly excelling in joint noise and state estimation for high colored noise.
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.