End-to-end DOA estimation via self-supervised cascaded DNNs with array errors mitigation

In this paper, we present a novel cascaded deep neural network architecture for high-precision direction-of-arrival (DOA) estimation in the presence of multiple array errors. The proposed framework addresses complex nonlinear distortions through an end-to-end feedforward computing strategy, eliminating the need for explicit error modeling. It consists of two key components trained in parallel: (1) four-layer adaptive spatial filters optimized using spatial-domain supervision on covariance matrices, and (2) an eight-layer MUSIC-NET that maps synthetic covariance matrices to MUSIC-like spectrum via self-supervised learning. The output of spatial filters serves as the input for MUSIC-NET, forming a two-stage architecture that enables adaptive array error mitigation during DOA estimation. Simulation results demonstrate that the proposed architecture exhibits superior robustness and generalization capabilities across unseen scenarios, outperforming state-of-the-art methods in low signal-to-noise ratio and limited snapshot conditions.