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基于势概率假设密度滤波的检测前跟踪新算法
引用本文:林再平,周一宇,安玮. 基于势概率假设密度滤波的检测前跟踪新算法[J]. 红外与毫米波学报, 2013, 32(5): 437-443
作者姓名:林再平  周一宇  安玮
作者单位:国防科学技术大学电子科学与工程学院,国防科学技术大学电子科学与工程学院,国防科学技术大学电子科学与工程学院
基金项目:十二五国防预研(113010203);武器装备预研基金(9140A21041110KG0148);
摘    要:基于势概率假设密度滤波(Cardinalized Probability Hypothesis Density, CPHD)检测前跟踪(Track before detect, TBD)算法能有效解决未知目标数的弱小目标检测跟踪.文章深入研究了CPHD算法, 从标准CPHD滤波的粒子权重更新出发, 结合检测前跟踪的实际, 合理地推导出CPHD-TBD算法的粒子权重更新表达式; 分析了CPHD滤波目标势分布的物理意义, 实现了目标势分布更新计算在检测前跟踪的应用.将CPHD滤波和TBD进行有效结合, 提出了基于势概率假设密度滤波的检测前跟踪算法, 并给出其详细实现步骤.仿真实验证明提出的CPHD-TBD算法与现有概率假设密度检测前跟踪(PHD-TBD)算法相比, 能更详细地传递目标分布信息, 从本质上改变了PHD-TBD对目标数估计的方式, 能更准确稳定估计目标数, 实现了对目标的发现和状态准确估计, 性能明显更优.

关 键 词:检测前跟踪  势概率假设密度滤波  粒子更新  势分布更新  
收稿时间:2012-06-06
修稿时间:2012-08-12

Track-Before-Detect algorithm based on cardinalized probability hypothesis density filter
LIN Zai-Ping,ZHOU Yi-Yu and AN Wei. Track-Before-Detect algorithm based on cardinalized probability hypothesis density filter[J]. Journal of Infrared and Millimeter Waves, 2013, 32(5): 437-443
Authors:LIN Zai-Ping  ZHOU Yi-Yu  AN Wei
Affiliation:School of Electronic Science and Engineering,National University of Defense Technology,Changsha,P. R. China,410073,School of Electronic Science and Engineering,National University of Defense Technology,Changsha,P. R. China,410073,School of Electronic Science and Engineering,National University of Defense Technology,Changsha,P. R. China,410073
Abstract:On the basis of the cardinalized probability hypothesis density (CPHD), track-before-detect (TBD) algorithm is able to effectively solve the detection and tracking of weak point target with unknown target number. A detailed study of the CPHD algorithm which starts from the standard CPHD filter to the practicalities of TBD is presented. The updated expression for calculating particle weight of CPHD-TBD algorithm was deduced. Meanwhile, according to the physical means of the target distribution of CPHD, its update calculation in TBD has been implemented. Ultimately the combination of the CPHD and TBD has been achieved. The method to use it was introduced. The CPHD-TBD algorithm changes the way of target number estimation essentially compared with the PHD-TBD, resulting in accurate information of target distributions. Simulation results demonstrated that the proposed algorithm can estimate the number and states of targets more stability and accurately than the existing PHD-TBD algorithm.
Keywords:Track-Before-Detect  Cardinalized Probability Hypothesis Density   Particle update   Cardinalized distribution update   
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