基于高斯粒子CPHD滤波的多目标检测前跟踪算法  

Track-before-detect algorithm based on Gaussian particle cardinalized probability hypothesis density

作　　者：冯新喜[1] 魏帅[1] 鹿传国 

机构地区：[1]空军工程大学信息与导航学院 [2]95806部队

出　　处：《控制与决策》2017年第11期1991-1996,共6页Control and Decision

基　　金：国家自然科学基金项目(61571458);陕西省自然科学基金项目(2011JM8023)

摘　　要：针对未知目标数条件下多弱小目标检测前跟踪(TBD)算法鲁棒性较低、运算量较大等问题,提出一种基于高斯粒子势概率假设密度(CPHD)滤波的多目标检测前跟踪算法.运用高斯函数近似目标状态的后验概率密度,采取粒子滤波的方法迭代更新CPHD中各高斯项的均值与协方差,无需重采样,避免了粒子退化和采样枯竭等问题;同时结合检测前跟踪算法的实际情况,得出粒子权值的更新表达式.仿真实验表明,与现有算法相比,所提出算法在降低复杂度的同时,可以更为可靠地传递目标势分布信息,从而提高多弱小目标数目和状态估计的准确性和稳定性.In the unknown target number environment, a new track-before-detect（TBD） algorithm based on cardinalized probability hypothesis density（CPHD） filter is proposed for the tracking and detection of multiple dim targets to avoid the low tracking robustness and high computational amount. The Gaussian particle filter（GPF） approximates posterior densities as Gaussians, the mean and covariance of each Gaussian components in CPHD can be operated recursively by using the particle filter, and particle resampling is not required, which can avoid the problems of particle degeneracy and sample impoverishment. Meanwhile, the updated expression for calculating the particle weight is derived according to the actual TBD situation. Simulation results show that the proposed algorithm is able to convey the cardinalized information more reliably and lower computational time with better tracking performance in number and states of multiple dim targets estimation than the conventional algorithm.

关 键 词：检测前跟踪 势概率假设密度 高斯粒子滤波 红外图像 

分 类 号：TN953[电子电信—信号与信息处理]