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闪烁噪声下的改进粒子滤波跟踪算法 被引量：4

Improved tracking method of particle filter in glint noise environment
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摘要 在实际雷达目标跟踪系统中,雷达量测常受到闪烁噪声干扰,传统的滤波算法在闪烁噪声下,滤波性能急剧下降甚至发散。提出了一种改进的粒子滤波(particle filter,PF)算法,按照高斯牛顿迭代方法对迭代扩展卡尔曼滤波(iterated extended Kal manfilter,IEKF)中的测量更新进行修正,利用修正的IEKF来产生PF的重要性密度函数。进一步,采用马尔科夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)方法来消除重采样引起的粒子贫化问题。在给出的闪烁噪声统计模型基础上,将所提算法与PF及MCMCPF算法进行了仿真比较,结果表明该算法具有更好的跟踪性能。 In real radar target tracking system,the measure data of radar are often distributed by the glint noise.The performances of conventional filters degrade severely in the presence of glint noise.An improved particle filter（PF） is proposed.The iterated extended Kalman filter（IEKF） is modified by providing a new measurement update based on Gauss-Newton iteration,and then the modified IEKF is used to generate the proposal distribution.Additionally,the Markov chain Monte Carlo（MCMC） step is adopted to counteract the problem of particle impoverishment caused by resampling step,and the diversity of the particles is maintained.Based on the glint noise statistical model,the proposed method is compared with the PF and the MCMCPF via simulations.It is shown that the proposed method outperforms both the PF and the MCMCPF.
作者 张俊根 姬红兵
机构地区 西安电子科技大学电子工程学院
出处 《系统工程与电子技术》 EI CSCD 北大核心 2010年第10期2223-2226,共4页 Systems Engineering and Electronics
基金 国家自然科学基金(60871074)资助课题
关键词 目标跟踪 粒子滤波 迭代扩展卡尔曼滤波 马尔科夫链蒙特卡罗 闪烁噪声 target tracking particle filter（PF） iterated extended Kalman filter（IEKF） Markov chain Monte Carlo（MCMC） glint noise
分类号 TN953 [电子电信—信号与信息处理]
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参考文献15

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共引文献8

同被引文献29

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引证文献4

二级引证文献12

系统工程与电子技术
2010年 第10期

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