In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with ...In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter.Based on our recent work on extended box-particle probability hypothesis density(ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density(ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density(PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targets' tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.展开更多
The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influen...The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.展开更多
In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussi...In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussian surface function is constructed first by the measurements, and it is used to define the GSM via a mapping function. We then integrate the GSM with the probability hypothesis density（PHD） filter, the Bayesian recursion formulas of GSM-PHD are derived and the Gaussian mixture implementation is employed to obtain the closed-form solutions. Moreover, the estimated shapes are designed to guide the measurement set sub-partition, which can cope with the problem of the spatially close target tracking. Simulation results show that the proposed algorithm can effectively estimate irregular target shapes and exhibit good robustness in cross extended target tracking.展开更多
针对杂波环境下多扩展目标跟踪中航迹起始和量测集划分问题,提出了一种基于高斯混合概率假设密度滤波器的扩展目标跟踪算法。在航迹起始阶段利用最近邻指数法对量测集进行聚类趋势分析,接着通过改进OPTICS(ordering points to identify ...针对杂波环境下多扩展目标跟踪中航迹起始和量测集划分问题,提出了一种基于高斯混合概率假设密度滤波器的扩展目标跟踪算法。在航迹起始阶段利用最近邻指数法对量测集进行聚类趋势分析,接着通过改进OPTICS(ordering points to identify the clustering structure)算法,建立一个增广数据集排序来表示量测集的密度结构,该算法对参数选择、初始点选择均不敏感,可以滤除量测集中的杂波。仿真结果表明,在航迹起始阶段本文所提算法在保证起始性能的同时计算代价明显减少,在量测集划分过程中,所提算法能够有效划分不同形状、密度的扩展目标,自适应地确定划分数目,减少算法运行时间。展开更多
针对复杂不确定性环境下具有不规则形状的多扩展目标跟踪问题,提出了一种基于星凸形随机超曲面模型(Starconvex RHM)的多扩展目标多伯努利滤波算法.首先,在有限集统计(Finite set statistics,FISST)理论框架下,采用多伯努利随机有限集(M...针对复杂不确定性环境下具有不规则形状的多扩展目标跟踪问题,提出了一种基于星凸形随机超曲面模型(Starconvex RHM)的多扩展目标多伯努利滤波算法.首先,在有限集统计(Finite set statistics,FISST)理论框架下,采用多伯努利随机有限集(MBer-RFS)和泊松RFS(Possion-RFS)分别描述多扩展目标的状态和观测,并给出扩展目标势均衡多目标多伯努利(ET-CBMeMBer)滤波器.其次,利用RHM去描述任意星凸形扩展目标的量测源分布,提出了容积卡尔曼高斯混合星凸形多扩展目标多伯努利滤波器.此外,本文给出了一种多扩展目标不规则形状估计性能的评价指标.最后,通过多扩展目标和具有形状突变的多群目标的跟踪仿真实验验证了本文方法的有效性.展开更多
文摘In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter.Based on our recent work on extended box-particle probability hypothesis density(ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density(ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density(PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targets' tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.
基金supported by the National Natural Science Foundation of China(61401475)
文摘The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.
基金supported by the National Natural Science Foundation of China(6130501761304264+1 种基金61402203)the Natural Science Foundation of Jiangsu Province(BK20130154)
文摘In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussian surface function is constructed first by the measurements, and it is used to define the GSM via a mapping function. We then integrate the GSM with the probability hypothesis density（PHD） filter, the Bayesian recursion formulas of GSM-PHD are derived and the Gaussian mixture implementation is employed to obtain the closed-form solutions. Moreover, the estimated shapes are designed to guide the measurement set sub-partition, which can cope with the problem of the spatially close target tracking. Simulation results show that the proposed algorithm can effectively estimate irregular target shapes and exhibit good robustness in cross extended target tracking.
文摘针对杂波环境下多扩展目标跟踪中航迹起始和量测集划分问题,提出了一种基于高斯混合概率假设密度滤波器的扩展目标跟踪算法。在航迹起始阶段利用最近邻指数法对量测集进行聚类趋势分析,接着通过改进OPTICS(ordering points to identify the clustering structure)算法,建立一个增广数据集排序来表示量测集的密度结构,该算法对参数选择、初始点选择均不敏感,可以滤除量测集中的杂波。仿真结果表明,在航迹起始阶段本文所提算法在保证起始性能的同时计算代价明显减少,在量测集划分过程中,所提算法能够有效划分不同形状、密度的扩展目标,自适应地确定划分数目,减少算法运行时间。
文摘针对复杂不确定性环境下具有不规则形状的多扩展目标跟踪问题,提出了一种基于星凸形随机超曲面模型(Starconvex RHM)的多扩展目标多伯努利滤波算法.首先,在有限集统计(Finite set statistics,FISST)理论框架下,采用多伯努利随机有限集(MBer-RFS)和泊松RFS(Possion-RFS)分别描述多扩展目标的状态和观测,并给出扩展目标势均衡多目标多伯努利(ET-CBMeMBer)滤波器.其次,利用RHM去描述任意星凸形扩展目标的量测源分布,提出了容积卡尔曼高斯混合星凸形多扩展目标多伯努利滤波器.此外,本文给出了一种多扩展目标不规则形状估计性能的评价指标.最后,通过多扩展目标和具有形状突变的多群目标的跟踪仿真实验验证了本文方法的有效性.
