Separable dimension subspace method for joint signal frequencies, DOAs and sensor mutual coupling estimation

Abstract

To extract the frequencies and direction of arrivals (DOAs) of multiple sources from experimental data collected by a sensor array is a multiple parameter estimation problem. Some important algorithms for spatial-temporal processing have been developed in the past decades. A practical problem, not often considered, is that the different sensors in the array affect each other through mutual coupling. This effect varies with frequencies and degrades the performance of algorithms. Thus, a separable dimension subspace method to simultaneously estimate signal frequencies, direction of arrivals (DOAs) and sensor mutual coupling is proposed.