Tracking Method without Prior Information when Multi-group Targets Appear Successively
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摘要:
针对多编队机动目标先后出现时的跟踪问题，该文提出了一种基于交互式多模型高斯混合概率假设密度滤波(IMM-GM-PHD)算法的无先验信息跟踪方法。首先，在IMM-GM-PHD算法预测过程完成的基础上，引入密度检测机制，利用相关域为所有预测高斯分量挑选有效量测，结合密度聚类(DBSCAN)算法检测是否出现新编队目标。其次，在IMM-GM-PHD算法状态更新完成的基础上，利用更新高斯分量的组成情况完成模型概率的更新。最后，在状态估计优化过程中，结合编队目标的特点，加入相似度判别技术，利用杰森-香农(JS)散度度量高斯分量间的相似度，剔除没有相似分量的高斯分量，进一步优化估计结果。仿真结果表明，该文方法能够快速有效地跟踪非同时出现的多编队机动目标，具有较好的跟踪性能。
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关键词:
- 多编队机动目标 /
- 交互式多模型高斯混合概率假设密度滤波算法 /
- 相关域 /
- 密度聚类算法 /
- 杰森-香农散度
Abstract:Considering the problem of multi-group maneuvering target tracking, a fast tracking method based on Interactive Multiple Maneuvering Gaussian Mixture Probability Hypothesis Density (IMM-GM-PHD) algorithm is proposed. Firstly, based on the completion of the IMM-GM-PHD algorithm prediction process, the density detection mechanism is added, and the correlation domain is used to select effective measurement for all predicted Gaussian components, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is combined to detect whether a new formation target appears. Secondly, based on the completion of the state update of the IMM-GM-PHD algorithm, the update of the model probability is completed by updating the composition of the Gaussian component. Finally, in the process of state estimation optimization, combined with the characteristics of formation targets, the similarity discrimination technique is added, and the Jensen-Shannon (JS) divergence is used to measure the similarity between Gaussian components, and the Gaussian components without similar components are eliminated, and the estimation results are further optimized. The simulation results show that the proposed algorithm can track multi-group maneuvering targets quickly and effectively, and has better tracking performance.
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表 1 不同杂波密度下平均OSPA距离比较
算法 杂波密度 ${\rm{\lambda }} = 1$ ${\rm{\lambda }} = 10$ ${\rm{\lambda }} = 50$ IMM-GM-PHD算法 29.755 32.129 44.609 本文算法 21.821 28.617 43.996 
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