水下声源定位问题是水声信号处理领域的重点和难点，基于计算声场和接收声场相关性辨识目标距离、深度的匹配场定位方法具有广泛应用。针对Bartlett处理器宽容性好但分辨率低、最小方差处理器分辨率高但对失配敏感的问题，将空间谱估计中基于矩阵空间特征分解的目标定向算法引入匹配场定位。对水下单声源定位、双声源定位、环境失配处理等条件，对比3种定位算法的性能。仿真数据表明，与Bartlett处理器相比，基于矩阵空间特征分解的定位方法具有更高的目标定位准确度。与最小方差处理器相比，该方法在双声源定位时能够分辨相近声源，尤其对深度辨识更准确。实验数据表明，基于矩阵空间特征分解的处理器能够实现表面强干扰条件下的水下弱目标定位。
Underwater sound source localization is an important and difficult problem in the field of underwater acoustic signal processing. Matched field processing method which use the correlation between the calculated sound field and the received sound field to localize target range and depth has been widely used. Aiming at the problem that the Bartlett processor has good tolerance but low resolution, and the minimum variance processor has high resolution but sensitive to environment mismatch, the target orientation algorithm based on matrix eigen decomposition in spatial spectrum estimation is introduced into matched field processing. With the conditions of single source, double source and environmental mismatch treatment, the performance of three localization algorithms is compared. Simulation data shows that compared with Bartlett processor, matrix eigen decomposition method has higher localization accuracy. Compared with the minimum variance processor, it can distinguish more adjacent sources, and has more accurate depth identification particularly. Experimental data shows that the processor based on matrix eigen decomposition can localize the underwater week target under the condition of strong surface interference.
2021,43(10): 141-148 收稿日期：2020-10-09
DOI：10.3404/j.issn.1672-7649.2021.10.029
分类号：TN911.7
基金项目：基础加强计划技术领域基金项目(2019-JCJQ-JJ-036)；国家自然科学基金资助项目(11374001)
作者简介：魏尚飞(1996-),男,硕士研究生,研究方向为水下信号处理
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