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首页> 外文期刊>International Journal of Control, Automation, and Systems >Probability Hypothesis Density Filter Based on Strong Tracking MIE for Multiple Maneuvering Target Tracking
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Probability Hypothesis Density Filter Based on Strong Tracking MIE for Multiple Maneuvering Target Tracking

机译：基于强跟踪MIE的概率假设密度滤波器用于多机动目标跟踪

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摘要

Taking into account the difficulties of multiple maneuvering target tracking due to the unknown target number and the uncertain acceleration, a novel multiple maneuvering target tracking algorithm based on the Probability Hypothesis Density (PHD) filter and Modified Input Estimation (MIE) technique is proposed in this paper. First, the unknown acceleration vector is added to the target state to form a new augmented state vector. Then, strong tracking filter multiple fading factors are introduced to the MIE method which can adjust the prediction covariance and the corresponding filter gain at different rates in real time, so that the MIE method can adaptively track high maneuvering targets well. Finally, we combine this adaptive MIE, method with the PHD filter, which can effectively track multiple maneuvering targets without much prior information. Simulation results show that the proposed algorithm has a higher tracking precision and a better real-time performance than the conventional maneuvering target tracking algorithms.
机译：考虑到目标数目未知和加速度不确定带来的多目标跟踪困难，提出了一种基于概率假设密度（PHD）滤波器和修正输入估计（MIE）技术的多目标跟踪算法。纸。首先，将未知加速度矢量添加到目标状态以形成新的增强状态矢量。然后，将强跟踪滤波器的多个衰落因子引入到MIE方法中，可以实时调整预测协方差和相应的滤波器增益，从而使MIE方法可以很好地自适应跟踪高机动目标。最后，我们将这种自适应MIE方法与PHD滤波器相结合，可以有效地跟踪多个机动目标，而无需太多先验信息。仿真结果表明，与传统的机动目标跟踪算法相比，该算法具有更高的跟踪精度和更好的实时性。

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