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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers >Tracking of group targets using multiple models GGIW-PHD algorithm based on best-fitting Gaussian approximation and strong tracking filter
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Tracking of group targets using multiple models GGIW-PHD algorithm based on best-fitting Gaussian approximation and strong tracking filter

机译：基于最佳拟合高斯逼近和强跟踪滤波器的多种模型GGIW-PHD算法跟踪群体目标

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摘要

Gamma Gaussian inverse Wishart probability hypothesis density (GGIW-PHD) filter algorithm is always used for tracking group targets with unknown number and variable measurement rates in the presence of cluttered measurements and missing detections. Aiming at the defect that the tracking error of GGIW-PHD filter algorithm will increase greatly in the maneuvering stage, a multiple model GGIW-PHD (MM-GGIW-PHD) algorithm is proposed in this paper based on the best-fitting Gaussian approximation and strong tracking filter. Firstly, on the basis of measurement set partition, the best-fitting Gaussian approximation method is proposed to implement the fusion of multiple models in the PHD predict stage. And a fading factor of strong tracking filter is proposed to correct the predicted covariance matrix of the GGIW component. Then, the estimation of kinematic state and extension state are deduced in the frame of multiple models. The probability of different tracking models is updated by the modified likelihood functions. The simulation results show that the MM-GGIW-PHD algorithm based on best-fitting Gaussian approximation and strong tracking filter can decrease the tracking error of group targets in the maneuvering stage and treated with the combination/spawning of group effectively.
机译：伽玛高斯逆Wishart概率假设密度（GGIW-PHD）过滤器算法始终用于在测量值混乱和检测缺失的情况下跟踪未知数量和可变测量速率的目标群体。针对GGIW-PHD滤波算法的跟踪误差在机动阶段会大大增加的缺点，本文提出了一种基于最佳拟合高斯逼近的多模型GGIW-PHD（MM-GGIW-PHD）算法。强大的跟踪过滤器。首先，在测量集划分的基础上，提出了最适合的高斯近似方法，以在PHD预测阶段实现多种模型的融合。提出了一种强跟踪滤波器的衰落因子，对GGIW分量的预测协方差矩阵进行校正。然后，在多个模型的框架中推导出运动状态和扩展状态的估计。通过修改的似然函数来更新不同跟踪模型的概率。仿真结果表明，基于最佳拟合高斯逼近和强跟踪滤波器的MM-GGIW-PHD算法可以减少机动目标在机动阶段的目标跟踪误差，并且可以有效地对目标进行组合/生成。

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