Adaptive Beamforming for Vector-Sensor Arrays Based on Reweighted Zero-Attracting Quaternion-Valued LMS Algorithm

In this work, reference signal based adaptive beamforming for vector sensor arrays consisting of crossed dipoles is studied. In particular, we focus on how to reduce the number of sensors involved in the adaptation so that reduced system complexity and energy consumption can be achieved while an acceptable performance can still be maintained, which is especially useful for large array systems. As a solution, a reweighted zero attracting quaternion-valued least mean square algorithm is proposed. Simulation results show that the algorithm can work effectively for beamforming while enforcing a sparse solution for the weight vector where the corresponding sensors with zerovalued coefficients can be removed from the system.