中文摘要：

常规的多重信号分类（multiple signal classification, MUSIC）算法计算量庞大，难以应用于多波束测深声纳（multi-beam bathymetry sonar, MBBS），而现有的波束域MUSIC算法仍需要进行协方差矩阵估计和特征值分解而造成系统规模复杂。将基于多级维纳滤波器（multiple stage Wiener filter, MSWF）的快速子空间估计与多子阵波束域MUSIC（multiple subarray beamspace MUSIC, MSB-RMU）算法相结合提出MM—MUSIC算法。和MS13-RMU算法相比，该算法用较小的性能损失换来大大降低的计算量和高度的可并行性，基于Xilinx AccelDSP综合工具的快速子空间估计的实现和实验数据的处理证明了该算法的有效性与实用性。

英文摘要：

Conventional MUSIC algorithm is hard to be applied in MBBS for its great computational load, and most of the beamspace MUSIC algorithms need the estimation of the covariance matrix and eigenvalue decomposition, which leads to complex signal processing platform. To solve this problem, a MM-MUSIC algorithm is proposed on the combination of the fast subspace estimation based on MSWF and a MSB-MUSIC algorithm. Compared with MSB-MUSIC algorithm, the proposed MM-MUSIC algorithm has much fewer computational load and higher parallelism at a cost of a little loss of performance. The realization of fast subspace estimation with AccelDSP and the processing results of experiment data prove the efficiency and practicality of the MM-MUSIC algorithm.