Target Association and Tracking Approach Based on Historical Kinematic Characteristics and SVM Spectrum Classification for Passive Sonar
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摘要: 针对航迹交叉条件下被动声呐目标跟踪困难的问题，该文将现有运动特征关联方法和信号特征辅助关联方法进行改进融合，提出一种基于历史运动特征约束和支持向量机(His-SVM)频谱分类的被动声呐目标关联跟踪方法。首先，利用目标的历史航迹点提取历史方位变化率，作为重合条件下点航迹关联的主要特征；其次，将方位靠近目标的点迹关联问题转化为点迹频谱的分类问题，利用目标航迹点频谱训练的SVM模型完成待关联点迹频谱的分类，根据分类结果实现方位靠近目标的点航迹关联；最后，将两种方法有机融合，构建了被动声呐交叉重合目标关联跟踪的算法框架。仿真实验结果表明，该算法能够有效完成靠近目标的点迹分类和交叉重合目标的关联跟踪，其跟踪性能优于传统运动特征关联跟踪算法。Abstract: In order to solve the crossing target tracking problem for passive sonar, a target association and tracking approach based on Historical kinematic characteristics and SVM (His-SVM) spectrum classification is presented, which combines the improved kinematic feature association method with the revised signal feature association method. The historical bearing changing rate is firstly extracted from historical track points to be used as a main feature for the overlapping target association and tracking. Furthermore, the SVM model, which is trained by the spectrum of track points, is utilized to classify the close trace points and each trace points can be assigned to different targets according to the classification results. Finally, the framework of the crossing target tracking algorithm is constructed by integrating historical kinematic characteristics with the SVM spectrum classification. The results of simulation studies verify the effectiveness of the proposed approach for close target association and crossing target tracking, and indicate that the tracking performance of the proposed approach is better than the traditional kinematic feature association method.
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表 1 SVM与相关系数方法分类正确率(%)比较
目标1、2分类 目标2、3分类 目标3、4分类 目标1、2分类
(线谱漂移)目标2、3分类
(线谱漂移)目标1 目标2 目标2 目标3 目标3 目标4 目标1 目标2 目标2 目标3 SVM(60 s训练) 100 100 100 100 99.58 100 100 100 100 100 SVM(30 s训练) 100 100 100 100 98.89 100 97.41 97.41 100 100 相关系数 100 100 100 89.58 98.75 47.50 75 77.92 74.58 90.83 
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表 2 5种算法处理性能比较
His-SVM Tra His Tra-SVM MHT 跟踪误差(°) 0.18 2.93 0.32 2.48 0.33 处理时间(s) 0.015 0.009 0.009 0.011 0.019
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