一种基于被动声呐宽带空间谱的自动检测算法

doi:10.12382/bgxb.2022.0170

摘要/Abstract

摘要：

被动声呐宽带目标检测中,基于固定门限的检测不能适应波动背景、对强弱目标共存场景检测时易检出旁瓣或漏检弱目标,此外检测结果中目标占据多个波束易使精确测向产生引导波束选取偏差。针对上述问题,基于宽带空间谱,将排序截断平均算法、局部峰值检测和波束内插算法结合,通过理论和蒙特卡洛实验分析对门限估计值有影响的参数,设计参数自动选取方案,形成检测门限自适应方法及自动检测与精确测向一体化方案。仿真与海试实验结果验证了所提算法的有效性,有较强工程应用价值。

关键词: 宽带目标检测, 自动检测, 排序截断平均, 局部峰值检测, 波束内插

Abstract:

In passive sonar boardband target detection, detection based on the fixed threshold cannot adapt to the fluctuating background, and it is easy to detect sidelobes or miss weak targets when detecting strong and weak targets coexisting in the scenario. In addition, the targets occupying multiple beams in the detection results can easily generate beam selection bias for accurate direction finding. To solve these problems, the broadband spatial spectrum is used as the test statistic, and the sorted truncation average algorithm, local peak detection and beam interpolation algorithm are combined. Through theoretical and Monte Carlo experiments, the parameters affecting the estimated threshold value are analyzed, and an automatic parameter selection scheme is designed. A detection threshold adaptive method and an integrated scheme of automatic detection and accurate direction finding are developed. The simulations and sea trials have verified the effectiveness of the proposed algorithm and have strong engineering application value.

Key words: wideband target detection, automatic detection, sorted truncation average, local peak detection, beam interpolation

柳嵩, 姚直象, 陆代强, 袁骏. 一种基于被动声呐宽带空间谱的自动检测算法[J]. 兵工学报, 2023, 44(6): 1764-1774.

LIU Song, YAO Zhixiang, LU Daiqiang, YUAN Jun. An Automatic Detection Algorithm Based on Wideband Spatial Spectrum of Passive Sonar[J]. Acta Armamentarii, 2023, 44(6): 1764-1774.

图/表 15

图1 角度轴滑窗

Fig.1 Angle axis sliding window

图2 BSTAD算法流程

Fig.2 BSTAD algorithm flow

图3 虚警概率随α的变化曲线

Fig.3 Curve of false alarm probability varying with α

图4 检测概率

Fig.4 Detection probability

图5 不同K值的虚警概率

Fig.5 False alarm probability curves for different K values

图6 -20dB信噪比时的检测概率

Fig.6 Detection probability at -20dB

图7 不同α值的检测概率

Fig.7 Detection probability curves for different α values

图8 不同α值的虚警概率

Fig.8 False alarm probability curves for different α values

图9 17个目标的方位历程

Fig.9 Azimuth history map of 17 targets

图10 不同K值下的BSTAD检测结果

Fig.10 BSTAD detection results under different K values

图11 强弱目标共存时的方位历程

Fig.11 Azimuth history map when strong and weak targets coexist

图12 两种不同算法检测结果对比

Fig.12 Comparison of test results of two different detection algorithms

图13 不同情况下角度估计均方根误差

Fig.13 Root mean square error of angle estimation in different cases

图14 海试实验的方位历程

Fig.14 The azimuth history map of the sea trial experiment

图15 不同检测算法结果对比

Fig.15 Comparison of test results of two different detection algorithms

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