Variable Step-Size General Bias-Compensated M-Estimate Adaptive Filtering for Robust DOA Estimation

Adaptive filters have been successfully applied in uniform linear array system to estimate the direction-of-arrival. However, traditional adaptive filtering algorithms suffer from degraded estimation accuracy against impulsive noise interferences or require a trade-off between the convergence speed and estimation accuracy. This article proposes a variable step-size general bias-compensated M-estimate (VSS-GBCM) adaptive filtering algorithm to address the above issues. This article first derives a bias-compensated M-estimate (BCM) adaptive filtering algorithm from a continuous bias-compensated M-estimate cost function, and then analyze its transient and steady-state mean square performance under common statistical assumptions. Sub-sequently, a variable step-size (VSS) mechanism is obtained by minimizing the mean square deviation at every snapshot, result ing in a VSS-BCM adaptive filtering algorithm. Moreover, the VSS-GBCM is developed by solving a general bias-compensated M-estimate cost function and using the above VSS strategy, where the VSS-BCM is its special case. Finally, simulation results validate that our solutions are superior to competing methods.