摘要
针对标准粒子滤波存在的粒子退化现象,提出了一种改进的UPF算法。该算法采用基于高斯Sigma点选取的自适应无味卡尔曼滤波产生建议分布函数,然后利用MetropolisHastings(MH)方法优化粒子,提高了对系统后验概率密度的逼近程度。仿真结果表明:改进算法降低了粒子滤波算法的粒子退化程度,提高了跟踪精度。
An improved Unscented Particle Filter （UPF） algorithm is proposed to overcome the problem of particles degradation in the standard particle filter. The algorithm uses adaptive unscented Kalman filter based on Gaussian Sigma point selection to generate the proposal distribution function. Then it uses the Metropolis-Hastings （Mtt） algorithm to optimize particles, so that the approximation of the posterior probability density of the system is improved. Simulation results show that the improved UPF algorithm reduces particle degradation which exists in the particle filter algorithm, and improves tracking accuracy.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2014年第5期1435-1440,共6页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(61263031)
关键词
计算机应用
粒子滤波
高斯Sigma点
无味卡尔曼滤波
MH方法
computer application
particle filter
Gaussian Sigma points
unscented Kalman filter
Metropolis- Hastings
