DOA estimation and tracking for signals with known waveform via symmetric sparse subarrays
In this paper, we present a novel approach to the problem of estimating and tracking the direction-of-arrival (DOA) of signals with known waveforms and unknown gains impinging on symmetric sparse subarrays. Unlike the conventional methods, which estimate the DOA based on the spatial signature of the signal with known waveform, the proposed method partitions the whole least square (LS) problem into multiple linear regression models of which each obtains a pair of DOA and gain. Here, we exploit a simple and efficient QR-decomposition-based recursive least square (QRD-RLS) technique to solve each linear regression model. Thanks to the unitary transformation for symmetric array configuration, we can decouple the pair of DOA and gain easily. Finally, several numerical examples showing the performance of the method are provided.