Robust DOA Estimation Using Complex-Valued Residual Attention Networks With Low-Rank and Sparse Prior

With the widespread application of multisensor systems, direction-of-arrival (DOA) estimation in complex electromagnetic environments is crucial for target detection and localization. Under nonideal conditions, the received signals are easily affected by uncontrollable factors such as coherent signals and variations in signal power. In this letter, a novel DOA estimation method based on the complex-valued residual attention convolutional neural network (CRA-CNN) is proposed. A complex-valued residual network integrated with an attention mechanism is introduced to extract key features from the signal covariance matrix, significantly enhancing feature representation and discrimination. Notably, a novel loss function combining low-rank and sparse prior constraints is designed to enhance sensitivity to essential features while suppressing redundancy and noise. Simulation results demonstrate that CRA-CNN improves both the accuracy and robustness of DOA estimation.