Polarization-beamspace self-initiating MUSIC for azimuth/elevation angle estimation

A novel MUSIC-based (multiple signal classification) direction finding algorithm applicable to arbitrary three-dimensional arrays of electromagnetic vector-sensors is herein proposed. This innovative algorithm: (1) self-generates coarse estimates of the arrival angles to start off its MUSIC-based iterative search without any a priori information on the sources' parameters, (2) exploits the sources' polarization diversity, (3) decouples the estimation of the sources' arrival angles from the estimation of the sources' polarization parameters, and (4) automatically pairs the x-axis direction-cosine estimates with the y-axis direction-cosine estimates and with the polarization estimates. An electromagnetic vector-sensor is composed of six co-located diversely polarized antennas distinctly measuring all six electromagnetic-field components of a multi-source incident wave-field and is commercially available. This proposed algorithm forms polarized beams at each vector-sensor, based on coarse estimates of each source's respective electromagnetic-field components obtained by decoupling the signal-subspace eigenvectors. Simulation results verify this innovative scheme's capability to self-generate initial estimates for its MUSIC-based iterative search and demonstrate the proposed algorithm's superior performance relative to a similarly spaced array of unpolarized scalar sensors. Under one scenario, the proposed method lowers the estimation bias by 98% and the estimation standard deviation by 40%.