基于自适应波束形成的鱼雷对潜目标识别技术

通过鱼雷对潜目标准确识别，实现对目标的精确打击。当前的目标识别算法采用时频特征提取算法，随着海洋背景噪声强度的增大，准确识别概率不高。提出一种采用亮点回波信号自适应波束形成的鱼雷对潜目标识别算法，首先进行了鱼雷对潜攻击声探测亮点回波模型构建，采用级联滤波器进行回波信号降噪处理，对滤波后的输出信号进行自适应波束形成处理，实现信号的特征提取和指向性聚焦，提高目标亮点回波信号的检测性能，实现目标准确识别。仿真结果表明，采用该算法进行鱼雷对潜目标检测识别，准确检测概率高于传统算法，在低信混比下仍具有较好的准确识别率，抗干扰性能较好。

Submarine target recognition technology based on adaptive beam forming

The target is accurately identified by torpedo, and the target is hit accurately. The current target recognition algorithm uses the time?frequency feature extraction algorithm, with the increase of the marine background noise intensity, the accurate recognition probability is not high. Put forward a torpedo submarine target recognition algorithm based on highlight echo signal adaptive beamforming, first of all, a torpedo attack submarine acoustic detection echo highlight model was constructed using cascaded filters for echo signal denoising, outputing signal of the filter for self adaptive beam forming process, implementing signal space gain directivity focusing, improve the detection performance of target echo highlight signal, realize precise target identification. Simulation results show that the proposed algorithm is used to detect and identify the potential target, and the accurate detection probability is higher than that of the traditional algorithm. The algorithm has better recognition rate and better anti jamming performance than the traditional algorithm.