Embedded Mixture-Correntropy Spatial Smoothing for Robust DOA Estimation in Shallow-Water Underwater Acoustics

Direction-of-arrival (DOA) estimation in shallow-water underwater acoustics is challenged by coherent multipath and impulsive disturbances, which jointly cause covariance rank deficiency and outlier-driven subspace distortion. This paper proposes an embedded robust covariance-construction mechanism for coherent-plus-impulsive DOA estimation. The mechanism is implemented as mixture-correntropy-weighted simplified spatial smoothing (SS–MCC), in which snapshot reliability is enforced during subarray covariance accumulation rather than after decorrelation. A two-kernel residual-based weighting rule suppresses strongly contaminated snapshots while retaining moderately perturbed but informative snapshots. Under a controlled narrowband uniform linear array benchmark with fully coherent two-arrival multipath and Bernoulli–Gaussian impulsive noise, SS–MCC yields more stable DOA behavior than MUSIC, SS-MUSIC, and FLOM-MUSIC, especially in low-SNR, high-impulsiveness, and near-threshold regimes, although absolute strict recovery remains limited in the hardest cases. All-trial strict correct-two-peak statistics and ablation results show that the gain mainly comes from embedded covariance cleaning rather than post-processing or parameter tuning. A measured-noise-injected benchmark using NOAA–Navy SanctSound FK01 underwater recordings further confirms the same qualitative robustness trend under real noise waveforms, while remaining a semi-realistic noise-injection check rather than measured-array sea-trial validation. A simplified DOA- assisted MVDR benchmark indicates that improved covariance robustness can also support more favorable beamforming-oriented trends. The results provide controlled benchmark evidence that reliability-aware covariance construction can stabilize subspace extraction under joint coherent multipath and impulsive contamination; validation under wideband propagation, model mismatch, partial coherence, and measured array data remains future work.