摘要
针对噪声分布未知环境下的非线性目标跟踪,提出了基于卷积粒子滤波的交互式多模型算法。该算法利用卷积粒子滤波器并行地运行多个模型,对前一时刻每个模型的状态后验概率密度进行交互,从交互后的概率密度中采样作为当前时刻滤波器的初始粒子,对当前时刻每个模型的状态后验概率密度进行加权作为系统输出。与基于粒子滤波的交互式多模型算法相比,算法消除了对量测噪声分布的依赖,提高了效费比,理论分析和仿真结果证明了该算法的有效性。
A new interacting multiple model algorithm based on the convolution particle filter is proposed for non-linear target tracking when the distribution of noise is unknown.The algorithm utilizes the convolution particle filter to run multiple models in parallel.The previous state posterior probabilities of all models interact each other.Samples from the interacted probability density are regarded as the current initial particles.The outputs of all parallel filters are weighted as system outputs.Compared with the interacting multiple model algorithm based on particle filter（IMM-PF）,the new algorithm improves the effectiveness-cost ratio and eliminates the correlation between the algorithm and analytical probability distribution of measurement noises.The theoretical analysis and simulation results show the effectiveness of the proposed algorithm.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2011年第5期992-995,共4页
Systems Engineering and Electronics
基金
国家自然科学基金(60736045
10776003)资助课题
关键词
机动目标跟踪
多模型算法
蒙特卡罗
粒子滤波
噪声分布
maneuvering target tracking
multiple model algorithm
Monte Carlo
particle filter
distribution of noise
作者简介
孙杰（1986-），男，硕士研究生，主要研究方向为机动目标跟踪。E—mail：sj—tbao@126．com
