Bias‐Covariance Joint H‐Infinity Filtering With Strict Disturbance Attenuation Bounds

ABSTRACT

Most existing H∞ adaptive methods cannot address non‐zero mean drift and bounded covariance at the same time. Conventional maximum likelihood schemes also tend to violate the γ‐robustness constraint. To overcome these issues, this paper proposes bias‐covariance joint H‐infinity filtering (BCJHF) algorithms. Bias‐covariance joint estimators are first introduced to allow for online estimation of both the mean and covariance. These estimators are then incorporated into the H∞ gain design to establish the BCJHF framework. The system is rigorously proven to maintain the γ‐suboptimal performance bound, preserving robustness during adaptive estimation. To further validate the proposed algorithms, the univariate nonstationary growth model is used as a simulation case.