Spatial Filtering Based on Differential Spectrum for Improving ML DOA Estimation Performance

Abstract

Recently, maximum spatial eigenfiltering allowed significant improvements on the estimation performance of maximum likelihood direction-of-arrival (DOA) estimators for closely spaced sources. However, that eigenfilter may greatly attenuate widely spaced sources as SNR decreases, leading to severe performance degradation. Since the differential spectrum shows prominent spectral peaks around the true DOA angles even at very low SNR, we originally propose using it to derive two eigenvalue-based finite-impulse response spatial filters to overcome that problem. The first one employs the frequency sampling approach, whereas the second one includes moving average modeling, to fit the frequency response to the differential spectrum. Both the amount of spectral samples and the filter orders were carefully chosen to control aliasing in time domain and reduce the overall computational effort. The simulation results showed that the proposed filters preserved signal passbands of DOA sources, even where the maximum spatial eigenfilter failed. Compared with that filter, our propositions significantly reduced the threshold SNR for widely spaced sources while performing very similarly for closely spaced sources, at the expense of an small increase in runtime. To the best of our knowledge, the proposed filters are the first to allow spatial filtering in maximum likelihood DOA estimation independently of source separation.