Enhanced decoupling minimum processor for coherent multi-target direction estimation in sparse arrays

Sparse arrays, compared to uniform linear arrays with the same number of elements, have larger array apertures, thus enhancing spatial resolution and offering more flexible array layouts. However, traditional direction-of-arrival (DOA) estimation algorithms based on sparse arrays may fail to estimate certain targets when dealing with multi-target scenarios due to grating lobes or sidelobes, and their performance drops sharply when targets are coherent. Aiming to address the challenge of handling coherent multi-target situations with sparse arrays, this paper proposes an improved decoupling minimum processor algorithm using nested arrays. The improved minimum processor takes the entire array's spatial spectrum and the results of the multiplication processor as direct inputs, reducing the influence of grating lobes and sidelobes on the main lobe, enabling the detection of multiple targets. Subsequent use of the virtual domain and smoothing algorithms can achieve decoherence of signals on sparse arrays. The improved algorithm resolves the issue of the minimum processor neglecting certain specific directions when estimating the directions of multiple targets, enabling the decoupling of multiple coherent targets based on nested arrays and accurately estimating the direction of each target, with sidelobes lower by 6 to 8 dB compared to the original minimum processor. Finally, the simulation data processing results verify that the proposed method can effectively estimate the directions of multiple targets.