Iterative Maximum Likelihood DOA Estimator Aided by Magnitude Measurements
Ningbo Liang, Shengchu Wang · 2022
This paper proposes an iterative maximum-likelihood (IML) direction of arrival (DOA) estimator for the magnitude-assisted polarization-sensitive array (MA-PSA), which incorporates magnitude-only radio frequency (RF) chains into the PSA to obtain magnitude observations. In IML, the multi-signal classification (MUSIC) algorithm is executed to acquire initial estimations of DOAs based on complex observations from the classical RF chains. Secondly, the azimuth and elevation DOAs of multiple sources are alternatively estimated, where only one DOA is updated while fixing the remaining ones. The neighborhood of the selected DOA is uniformly gridded, and probability likelihood values of the grid points are calculated. Finally, the selected DOA is estimated as the one reaching the maximum likelihood value. The above process is named single angle search (SAS), alternatively executed in IML until convergence is reached. Due to the iterative SAS, IML significantly reduces the algorithm complexity compared to the conventional ML, which estimates all the DOAs jointly and becomes rather cumbersome once the number of DOAs is relatively high. Simulation results show that IML outperforms some existing DOA and channel response estimators and does not require a reference source. MA-PSA outperforms PSA on the performance of DOA estimation by introducing additional magnitude measurements.