基于粒子滤波的检测前跟踪改进算法  

Improved track-before-detect algorithm based on particle filter

作　　者：吴孙勇[1,2] 廖桂生[1] 杨志伟[1] 李彩彩[1] 

机构地区：[1]西安电子科技大学雷达信号处理重点实验室,西安710071 [2]桂林电子科技大学数学与计算科学学院,广西桂林541004

出　　处：《控制与决策》2010年第12期1843-1847,共5页Control and Decision

基　　金：国家杰出青年科学基金项目(60825104);国家自然科学基金重点项目(60736009);国家自然科学基金项目(60901066)

摘　　要：针对低信噪比环境下微弱目标的实时检测与跟踪,提出一种基于粒子滤波的检测前跟踪改进算法.该算法在粒子滤波的基础上融合不敏卡尔曼滤波(UKF)算法,融合后的新算法在利用重要性密度函数产生粒子时充分考虑当前时刻的量测,从而引导粒子向高似然区域移动,使得粒子的分布更接近状态的后验概率分布.仿真实验表明,改进算法的检测与跟踪性能优于标准的粒子滤波算法.An improved track-before-detect（TBD） algorithm based on the particle filter is proposed for weak target detection and tracking in low signal to noise radio（SNR） environment.Under the theory framework of particle filter,an algorithm which combines the particle filter with unscented Kalman filter（UKF） algorithm is presented.When the important probability density distribution is calculated by using the algorithm,the sampling particles are most likely to be in the region of high likelihood based on the current measurement,which makes the particles distribution more approach to the posterior distribution of the state.Simulation results show that the proposed algorithm has an improved performance of detection and tracking compared with the standard particle filter.

关 键 词：粒子滤波 检测前跟踪 微弱目标 不敏卡尔曼滤波 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]