Constant modulus hybrid recursive and least mean squared algorithm performance comparable to unscented Kalman filter for blind beamforming

摘要

In this paper, we propose an adaptive filtering algorithm, Hybrid Recursive and Least Mean Square-based Constant Modulus Algorithm (RLS-LMS-CMA) for optimized blind beamforming for a Uniform Linear Array (ULA). We consider that Recursive Least Square-based Constant Modulus Algorithm (RLS-CMA) and Least Mean Square-based Constant Modulus Algorithm (LMS-CMA) algorithms are time tested. Therefore, we investigated a combination of RLS-LMS-CMA algorithm. We achieve similar tracking performance when compared to Unscented Kalman Filter-based Constant Modulus Algorithm (UKF-CMA) with minimal computational complexity. Simulations are carried out to compare the performance of RLS-LMS-CMA with other state-of-the-art algorithms. Results obtained indicate that proposed algorithm leads to an equivalent tracking ability and convergence rate of UKF-CMA algorithm.