Single vector hydrophone DOA estimation via bearing-inner-product accumulation and adaptive bearing threshold

Abstract

Traditional single vector hydrophone direction-finding methods suffer from accuracy limitations under low signal-to-noise ratio (SNR) and strong interference. The Conventional Statistical (CS) method is easily overwhelmed by strong noise, while the Weighted Statistical (WS) method has poor robustness against narrowband interference. This paper proposes a method combining Bearing-Inner-Product Accumulation (BIPA) with adaptive bearing threshold constraints. An inner product matrix of unit bearing vectors is constructed across all frequency points to quantify bearing consistency, and a subset of frequency points with the most concentrated bearing distributions is adaptively selected to form an adaptive spatial filter that removes noise and interference. A signal quality factor derived from the sound field coherence function adaptively adjusts the bearing threshold width, while the limited target motion speed constrains the effective estimation range to prevent abrupt bearing changes. Field tests in the South China Sea show that, for weak broadband targets under time-varying SNR, the proposed method achieves an RMSE of 7.46°, which is 56.1% lower than CS (17.01°) and 87.6% lower than WS (60.15°), while effectively suppressing strong interference and background noise from non-target directions. This research provides a reliable technical solution for high-precision direction-finding using a single vector hydrophone in complex marine environments.