摘要
对标准粒子滤波在处理非线性系统状态估计中噪声独立假设的局限,该文研究分析了一种噪声相关粒子滤波算法。在常用的系统状态模型基础上,分析了噪声相关时建议分布函数的具体分布形式,并以高斯相关噪声为背景,在重要性权重条件最小方差意义下推导了最优建议分布函数的数值表达式。所设计的滤波器有效弥补了传统粒子滤波算法在噪声相关情况下的缺陷,拓展了PF算法的应用范围。仿真实验表明了该方法的有效性。
The standard particle filter needs to meet the requirement of noise independent. In order to overcome this limitation, this paper proposes a correlative noise particle filter （CN-PF） algorithm. The method analyzes the characteristic of noise time correlation, and derives the joint probability density function of correlative noise based on the given nonlinear system model. The concrete implementation method of noise de-correlation is analyzed based on the Gaussian noise assumption. The optimal proposal distribution function is deduced in the condition of the importance weight variance minimum. The CN-PF algorithm compensates the shortage of the traditional particle filter algorithm effectively, and expands the application range of the PF algorithm. The theoretical analysis and simulation results show the effectiveness of the propose method.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2016年第3期436-441,共6页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(61263031)
甘肃省自然科学基金(1010RJZA046)
甘肃省教育厅研究生导师基金(0914ZTB003)
关键词
噪声相关
非线性系统
粒子滤波
建议分布函数
noise correlation
nonlinear system
particle filter
proposal distribution function
