Abstract: In order to solve the problem that the original Extended Target Gaussian Mixture Probability Hypothesis Density（ET-GM-PHD） filter can not track maneuvering target, this paper introduces the Modified Input Estimation（MIE） algorithm into the framework of ET-GM-PHD filter to deal with the tracking of multiple maneuvering extended targets. Moreover, the proposed algorithm which can track multiple maneuvering extended targets with unknown targets’ numbers cannot obtain the trajectories of each target. So, the Gaussian component labeling method is used to effectively identify each extended target and obtain their trajectories. Simulation results show that the proposed algorithm has a good performance in the weak maneuvering target tracking and effectively estimates the trajectories of each extended target.

Key words: multiple extended target, Gaussian Mixture Probability Hypothesis Density（GM-PHD）, Modified Input Estimation （MIE）, track maintenance

摘要： 针对原始扩展目标高斯混合概率假设密度（Extended Target Gaussian Mixture Probability Hypothesis Density，ET-GM-PHD）滤波算法不能解决机动目标跟踪问题，在高斯混合概率假设密度（Gaussian Mixture Probability Hypothesis Density，GM-PHD）滤波框架下，引入修正的输入估计算法（Modified Input Estimation，MIE），可以有效地处理多扩展目标的机动问题。此外，提出的算法虽然可以实现对未知数目的多机动扩展目标进行跟踪，但无法获得各个目标的航迹。针对此问题，进一步引入高斯分量标记方法，有效地将多机动扩展目标的航迹进行准确关联，获取各个目标的航迹。实验结果表明，提出的算法在弱机动扩展目标跟踪中具有较好的跟踪性能，同时能够有效地估计多扩展目标的航迹。

关键词: 多扩展目标, 高斯混合概率假设密度, 输入估计, 航迹维持