摘要
对粒子滤波算法的原理和应用进行综述.首先针对非线性非高斯系统的状态滤波问题,阐述粒子滤波的原理;然后在分析采样-重要性-重采样算法基础上,讨论粒子滤波算法存在的主要问题和改进手段;最后从概率密度函数的角度出发,将粒子滤波方法与其他非线性滤波算法进行比较,阐明了粒子滤波的适应性,给出了粒子滤波在一些研究领域中的应用,并展望了其未来发展方向.
The Algorithm and applications related to particle filter are surveyed. Aiming at the nonlinear/non-Gaussian filter problem, the generic ideas of particle filter are given, based on the analysis of standard algorithm of sampling-importance-resampling filter, the problems of particle filter are discussed and some improvement methods are illustrated. From view of probability density function, the comparisons between particle filter and others nonlinear filter algorithms and applicability are introduced, some applications in the developed areas are reviewed, Finally, further research directions are pointed out.
出处
《控制与决策》
EI
CSCD
北大核心
2005年第4期361-365,371,共6页
Control and Decision
基金
国家自然科学基金项目(60375008)
中国博士后科学基金项目(2003034265)
上海市博士后基金项目(SH0325)
河北省博士基金项目(B2004510).
关键词
粒子滤波
概率密度
非线性滤波
算法
Computer aided diagnosis
Computer vision
Kalman filtering
Object recognition
Probability density function
Sampling
