Bayesian Fusion Based Robust Array Shape Estimation for Distorted Towed Hydrophone Array

Towed hydrophone arrays are widely employed for underwater target detection and direction-of-arrival (DOA) estimation. However, array shape distortion induced by ocean currents, internal waves, and platform maneuvers severely degrades beamforming performance and DOA estimation accuracy. In this paper, a novel Bayesian fusion framework is proposed to achieve robust array shape estimation. Specifically, based on the time-delay estimates derived from the phase differences of line-spectrum components in a pre-processing step, the array geometry is first reconstructed via a piecewise straight-line fitting method. Concurrently, an existing hidden Markov model (HMM)-based method is adopted to estimate the inter-segment deviation angles, in which the smoothness of the array shape is enforced through the state-transition probabilities. The proposed framework then treats these two preliminary estimates as observations from distinct sources and incorporates a smoothness prior within a maximum a posteriori (MAP) formulation that admits a non-iterative closed-form solution to enforce physical continuity constraints on the array geometry. By fusing these complementary estimates, the proposed method simultaneously preserves local sensitivity to fine-scale bends and maintains global consistency of the array shape. Both simulation and lake-trial experiments validate the effectiveness of the proposed method, reducing the array shape estimation error by more than 30% relative to representative existing methods. Moreover, by relying solely on the received acoustic data, the method lowers the dependence on auxiliary sensors and the associated system cost.