| 基于粒子群优化的Unscented粒子滤波算法 |
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| 引用本文： | 李睿,苑柳青,李明.基于粒子群优化的Unscented粒子滤波算法[J].计算机工程,2011,37(13):153-155. |
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| 作者姓名： | 李睿 苑柳青 李明 |
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| 作者单位： | 兰州理工大学计算机与通信学院,兰州,730050 |
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| 基金项目： | 甘肃省财政厅科研基金资助项目，甘肃省教育厅研究生导师基金资助项目 |
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| 摘 要： | 针对Unscented粒子滤波(UPF)算法中的粒子退化及重采样引起的粒子枯竭等问题，利用粒子群优化算法使粒子通过比较其当前值与最优粒子的适应度值调整自身速度，向高似然域移动，寻找最优位置，并对重采样过程进行优化，以缓解粒子的退化及枯竭问题。实验结果证明，该算法提高了UPF算法的状态估计精度。
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| 关 键 词： | Unscented粒子滤波 粒子群优化算法 粒子退化 粒子枯竭 重采样 |
| 收稿时间： | 2010-11-18 |
Unscented Particle Filter Algorithm Based on Particle Swarm Optimization |
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| LI Rui,YUAN Liu-qing,LI Ming.Unscented Particle Filter Algorithm Based on Particle Swarm Optimization[J].Computer Engineering,2011,37(13):153-155. |
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| Authors: | LI Rui YUAN Liu-qing LI Ming |
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| Affiliation: | (School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China) |
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| Abstract: | Aiming at the problem of Unscented Particle Filter（UPF） algorithm such as particles degeneracy and particles impoverishment,by comparing particles’ present values with the fitness value of objective function,it uses Particle Swarm Optimization（PSO） algorithm to make particles of UPF move towards the higher likelihood area,and finds the optimal position,and relieves the problem of particles degeneracy and impoverishment by improving re-sampling process.Experimental result proves that the state estimation precision of the improved algorithm is superior to traditional UPF algorithm |
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| Keywords: | Unscented Particle Filter（UPF） Particle Swarm Optimization（PSO） algorithm particle degeneracy particle impoverishment re-sampling |
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