The finite set statistics provides a mathematically rig- orous single target Bayesian filter （STBF） for tracking a target that generates multiple measurements in a cluttered environment. However, the target maneuver...The finite set statistics provides a mathematically rig- orous single target Bayesian filter （STBF） for tracking a target that generates multiple measurements in a cluttered environment. However, the target maneuvers may lead to the degraded track- ing performance and even track loss when using the STBF. The multiple-model technique has been generally considered as the mainstream approach to maneuvering the target tracking. Moti- vated by the above observations, we propose the multiple-model extension of the original STBF, called MM-STBF, to accommodate the possible target maneuvering behavior. Since the derived MM- STBF involve multiple integrals with no closed form in general, a sequential Monte Carlo implementation （for generic models） and a Gaussian mixture implementation （for linear Gaussian models） are presented. Simulation results show that the proposed MM-STBF outperforms the STBF in terms of root mean squared errors of dynamic state estimates.展开更多
根据有限集统计方法,推导得到了可适用于不可分辨目标跟踪问题的势概率假设密度(cardinalized probability hypothesis density,CPHD)滤波器。类似传统的点目标CPHD滤波器,该不可分辨目标CPHD滤波器不仅可以递推地传递多目标状态集合的...根据有限集统计方法,推导得到了可适用于不可分辨目标跟踪问题的势概率假设密度(cardinalized probability hypothesis density,CPHD)滤波器。类似传统的点目标CPHD滤波器,该不可分辨目标CPHD滤波器不仅可以递推地传递多目标状态集合的一阶统计矩,还可以传递多目标个数(即势)的概率分布。蒙特卡罗仿真实验表明,相比Mahler提出的不可分辨目标PHD滤波器,所提出的不可分辨目标CPHD滤波器具有更加精确和稳定的多目标个数和状态估计,但它的计算量要大于不可分辨目标PHD滤波器。展开更多
针对复杂环境下单传感器多目标跟踪方法效果不佳的问题,基于FISST(Finite set statistics)跟踪理论提出一种多传感器高斯混合PHD(Probability hypothesis density)多目标跟踪方法.首先,分析了FISST下多传感器PHD的形式化滤波器,在此基...针对复杂环境下单传感器多目标跟踪方法效果不佳的问题,基于FISST(Finite set statistics)跟踪理论提出一种多传感器高斯混合PHD(Probability hypothesis density)多目标跟踪方法.首先,分析了FISST下多传感器PHD的形式化滤波器,在此基础上构建一种反馈式多传感器PHD融合跟踪框架;进一步利用高斯混合技术提出多传感器PHD跟踪方法;最后,通过解决多传感器后验PHD粒子匹配与融合问题提出三种算法.仿真实验表明,与常规高斯混合PHD跟踪算法相比,本文所提算法能够有效提高目标跟踪精度和鲁棒性.展开更多
基金supported by the National Natural Science Foundation of China (61101181)
文摘The finite set statistics provides a mathematically rig- orous single target Bayesian filter （STBF） for tracking a target that generates multiple measurements in a cluttered environment. However, the target maneuvers may lead to the degraded track- ing performance and even track loss when using the STBF. The multiple-model technique has been generally considered as the mainstream approach to maneuvering the target tracking. Moti- vated by the above observations, we propose the multiple-model extension of the original STBF, called MM-STBF, to accommodate the possible target maneuvering behavior. Since the derived MM- STBF involve multiple integrals with no closed form in general, a sequential Monte Carlo implementation （for generic models） and a Gaussian mixture implementation （for linear Gaussian models） are presented. Simulation results show that the proposed MM-STBF outperforms the STBF in terms of root mean squared errors of dynamic state estimates.
文摘根据有限集统计方法,推导得到了可适用于不可分辨目标跟踪问题的势概率假设密度(cardinalized probability hypothesis density,CPHD)滤波器。类似传统的点目标CPHD滤波器,该不可分辨目标CPHD滤波器不仅可以递推地传递多目标状态集合的一阶统计矩,还可以传递多目标个数(即势)的概率分布。蒙特卡罗仿真实验表明,相比Mahler提出的不可分辨目标PHD滤波器,所提出的不可分辨目标CPHD滤波器具有更加精确和稳定的多目标个数和状态估计,但它的计算量要大于不可分辨目标PHD滤波器。
文摘针对复杂环境下单传感器多目标跟踪方法效果不佳的问题,基于FISST(Finite set statistics)跟踪理论提出一种多传感器高斯混合PHD(Probability hypothesis density)多目标跟踪方法.首先,分析了FISST下多传感器PHD的形式化滤波器,在此基础上构建一种反馈式多传感器PHD融合跟踪框架;进一步利用高斯混合技术提出多传感器PHD跟踪方法;最后,通过解决多传感器后验PHD粒子匹配与融合问题提出三种算法.仿真实验表明,与常规高斯混合PHD跟踪算法相比,本文所提算法能够有效提高目标跟踪精度和鲁棒性.
文摘本文基于随机有限集的高斯混合多目标滤波器(Gaussian Mixture Multi-Target Filter,GM-MTF)提出几种传感器控制策略.首先,基于容积卡尔曼高斯混合多目标非线性滤波器,借助两个高斯分布之间的巴氏距离,推导GM-MTF的整体信息增益,并以此为基础提出相应的传感器控制策略.另外,设计高斯粒子的联合采样方法对多目标滤波器的预测高斯分量进行采样,用一组带权值的粒子去近似多目标统计特性,利用理想量测集对粒子的权值进行更新,继而研究利用Rényi散度作为评价函数,提出一种适应性更好的传感器控制策略.最后,给出基于目标势的后验期望(Posterior Expected Number of Targets,PENT)评价的高斯混合实现过程.仿真实验验证了提出算法的有效性.
