A Multi-target Passive Tracking Algorithm Based on Unmanned Underwater Vehicle
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摘要:
被动声呐信号处理中，致力于实现连续且稳定的目标方位跟踪。在复杂的水下环境中，由于干扰和噪声的存在，以及阵列孔径的限制，方位检测结果中不可避免地存在很多轨迹中断、野值、干扰与目标间的方位交叉。该文提出了一种基于水下无人航行器的多目标被动跟踪算法，使用基于航行器运动信息的粒子采样预测方法进行轨迹中断预测补齐，使用基于航行器运动信息的观测门限设置方法自适应设置跟踪门限，使用块关联跟踪方法进行轨迹中断关联和方位交叉关联。仿真和实验结果表明，该算法能够实现正确的多目标跟踪。
Abstract:In the passive tracking using acoustic arrays, continuous and stable tracking of targets is important. In complex underwater environments, there are inevitably many trajectory interruptions, outliers, interference and target azimuth crossings in the bearing detection results, due to interference, noise, and arrays aperture limitations. In this paper, a multi-target passive tracking algorithm based on unmanned underwater vehicle is proposed. The particle sampling prediction method based on the motion information of the vehicle is used to perform the interruption prediction. The observation threshold setting method based on the motion information of the vehicle is used to adaptively set the tracking threshold. The block association tracking method is used for association of trajectory break and azimuth cross. The experimental results show that the proposed algorithm achieves correct multi-target tracking.
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表 1 检测概率和算法性能(%)
方位连续段
检测概率算法正确
跟踪概率方位连续段
检测概率算法正确
跟踪概率100 100.00 40 80.810 80 100.00 35 63.420 50 98.21 30 24.140 45 91.55 25 0.037 
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表 2 检测跟踪概率和平均方位误差统计结果
阈值检测 传统跟踪算法 VI-BOTA VIP-BOTA 检测跟踪概率(%) 64.85 69.67* 84.70* 92.62 平均方位误差(°) 1.54 1.59 3.12(1.47) 1.29
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