摘要
介绍了粒子滤波的基本思想和具体算法实现步骤,在给出的闪烁噪声统计模型基础上,将粒子滤波算法应用在雷达目标跟踪中,解决了闪烁噪声情况下的雷达目标跟踪问题.仿真结果表明,在满足高斯噪声条件下,扩展卡尔曼算法和粒子滤波算法跟踪性能相近,但若考虑雷达的闪烁噪声,则随着闪烁影响增强,扩展卡尔曼算法跟踪性能严重下降,而粒子滤波算法能继续保持较好的跟踪精度.
Particle filter is a new filtering method based on Bayesian estimation and Monte Carlo method and can effectively cope with complicated nonlinear and/or non-Gaussian problems. The basic idea and algorithm description of particle filter were presented. Then, particle filter was introduced to radar tracking based on the glint noise statistical model. The Monte Carlo simulation results show that in Gaussian environment both extended Kalman filter and particle filter have almost the same tracking accuracy, and that in glint noise environment particle filter has also good accuracy, while the extended Kalman filter's performance degrades severely as the glint effect increasing.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2004年第12期1996-1999,共4页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金资助项目(60375008)
国家教育部科学技术研究重点项目(01072)
上海市科技发展基金重点项目(015115038)
