Joint ternary coprime array and sparse Bayesian learning for underwater acoustic target bearing estimation

To enhance underwater acoustic target bearing estimation, a novel scheme integrating a ternary coprime array (TCA) with sparse Bayesian learning (SBL) is proposed. The TCA architecture, comprising three uniform subarrays, is leveraged for its high degrees of freedom (DOFs) and reduced mutual coupling. Notably, the TCA maintains a closed-form DOFs expression regardless of element variations, facilitating modularity and scalability well-suited for complex and dynamic sea environments. For multi-snapshot scenarios, a vectorized SBL algorithm tailored for the TCA is developed. Comprehensive simulations and validation using sea-trial data confirm its superior estimation accuracy over classical sparse arrays across varying signal-to-noise ratios (SNRs) and snapshot numbers in both narrowband and broadband conditions. To address the challenging few-snapshot case, an innovative compressed-grid SBL strategy confined to specific angular subintervals is derived. An estimate matching framework is devised to merge the high-precision yet aliased estimates from sparse subarrays with the aliasing-free but low-precision estimates from the dense subarray. Finally, simulation results demonstrate that this compressed-grid approach offers significant performance advantages over existing methods at identical grid densities.