A Novel DOA Estimation of Closely Spaced Sources Using Attention Mechanism With Conformal Arrays

Recent developments in electronic countermeasures have heightened the demand for the direction of arrival (DOA) estimation, and it has become more urgent to improve the resolution of closely spaced sources for conformal arrays. In previous studies, distributed sources incident on linear or planar arrays have been the point of focus, and their performance for closely spaced sources has deteriorated dramatically. To address this issue, we design a multi-head attention (MHA) mechanism-based cascaded estimator, which is composed of a signal-to-noise ratio (SNR) classification network and two DOA estimation subnetworks. The former network uses a fully connected neural network for SNR classification, whose output can activate the corresponding estimation subnetwork. In estimating subnetworks, we adopt multiple self-attention mechanisms for the first time to extract more implied and representative information from raw features for effective estimation. Numerical results verify that the proposed estimator not only exhibits better performance in terms of resolution and recovery accuracy even at lower SNR but also provides great robustness with respect to angle intervals and snapshots, which lays the groundwork for further DOA studies for closely spaced sources.