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.
On the basis of the cardinalized probability hypothesis density （CPHD）, track-before-detect （TBD） algo- rithm 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.
出处
《红外与毫米波学报》
SCIE
EI
CAS
CSCD
北大核心
2013年第5期437-443,共7页
Journal of Infrared and Millimeter Waves
基金
十二五国防预研基金项目(113010203)
武器装备预研基金(9140A21041110KG0148)~~
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
track-before-detect
cardinalized probability hypothesis density
particle update
cardinalized probabilitydistribution update
作者简介
林再平（1982-）,男,浙江台州人,博士研究生,主要研究领域为空间红外图像获取与处理.Email：linzaiping@sina.com.
