Recursive Constrained Maximum Versoria Criterion Algorithm for Adaptive Filtering

This paper proposes a recursive constrained maximum Versoria criterion (RCMVC) algorithm. In comparison with recursive competing methods, our proposed RCMVC can achieve smaller steady-state misalignment in non-Gaussian noisy environments. Specifically, we use the maximum Versoria criterion (MVC) to derive a new robust recursive constrained adaptive filtering within the least-squares framework for solving linearly constrained problems. For RCMVC, we analyze the mean-square stability and characterize the theoretical transient mean square deviation (MSD) performance. Furthermore, we conduct some simulations to validate the consistency between the analytical and simulation results and show the effectiveness of RCMVC in non-Gaussian noisy environments.