确定性采样的矢量水听器阵列舰船目标方位估计

Ship target bearing estimation based on deterministic sampling vector hydrophone array

使用较少的硬件电路和计算量实现高分辨率的目标方位估计一直是海域防御系统具有挑战性的研究工作。将压缩感知理论应用于矢量水听器阵列,利用确定测量矩阵建立了确定压缩采样的矢量水听器阵列(DCV)结构,并将其与改进的多重信号分类算法(MMUSIC)相结合,提出了确定压缩采样的矢量水听器阵列的MMUSIC(DCV-MMUSIC)算法。将该算法用于相关或非相关模拟舰船目标的方位估计,通过对算法的可行性、高分辨能力和改变测量次数、信噪比和快拍数等多种仿真,得出该算法可以在低信噪比和小快拍下对舰船目标进行方向估计,并且具有使用硬件电路少、计算量低和估计偏差小等优点。又将其用于实际商船目标的方位估计,得到了较好的方位估计性能。

Achieving high resolution bearing estimation with less hardware circuit and computational cost is always a challenging task for port defense systems. In this paper, the compressive sensing theory is applied in vector hydrophone array, the deterministic measurement matrix is used to construct the deterministic compressive sampling vector hydrophone array （DCV） structure, which is combined with the modified multiple signal classification （MMUSIC） algorithm and a novel beating estimation algorithm, named as DCV-MMUSIC, is proposed. The algorithm was used in the bearing estimation of correlated or uneorrelated simulative ship targets. Through various simulations on the algorithm feasibility, high resolution, different number of measurements, signal noise ratio （SNR） and number of snaps, it is concluded that this algorithm carl be applied in the bearing estimation of ship target in lower SNR and fewer number of snaps, and has the advantages of less computational cost, less hardware circuit and small estimation deviation. The algorithm was also used in the bearing estimation of actual merchant ship targets, and good performance of bearing estimation is achieved.