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首页> 外文会议>International Conference on Modelling, Simulation and Optimization Technologies and Applications >Application of Cluster Analysis on Gaussian-Mixture Probability Hypothesis Density Filter for Multiple Extended Target Tracking
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Application of Cluster Analysis on Gaussian-Mixture Probability Hypothesis Density Filter for Multiple Extended Target Tracking

机译：集群分析对多延长目标跟踪高斯 - 混合概率假设密度滤波器的应用

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摘要

Based on the multiple Extended Target Gaussian-Mixture Probability Hypothesis Density (GM-PHD) filter, a new algorithm of extended target track initiation and observation partition in the clutter environment are proposed. Firstly the paper take clustering trend of observation into account when carrying track initiation, which make the clustering results more convincing and increase computational efficiency; Then, the improved partition algorithm introduce the concepts of core distance and reached distance to save the sequence of measurement points and extract the measurement cluster. Simulation experiments show that the proposed initiation algorithm has a better computational cost over traditional algorithm when carrying track initiation. In the partition process, the new algorithm is not sensitive to the parameter selection and extended target measurement density, at the same time, the computational cost decreases.
机译：基于多扩展目标高斯 - 混合概率假设密度（GM-PHD）滤波器，提出了一种新的延长目标轨道启动和杂波环境中观察分区的新算法。首先，在携带轨道启动时，纸张将观察的聚类趋势考虑，这使得聚类结果更加令人信服，提高计算效率;然后，改进的分区算法介绍了核心距离的概念并达到距离，以保存测量点序列并提取测量簇。仿真实验表明，当携带轨道启动时，所提出的启动算法具有更好的传统算法计算成本。在分区过程中，新算法对参数选择和扩展目标测量密度不敏感，同时计算成本降低。

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