[1]王尔申,李兴凯,庞涛.基于BP神经网络的粒子滤波算法[J].智能系统学报,2014,9(06):709-713.[doi:10.3969/j.issn.1673-4785.201310057]
 WANG Ershen,LI Xingkai,PANG Tao.A particle filtering algorithm based on the BP neural network[J].CAAI Transactions on Intelligent Systems,2014,9(06):709-713.[doi:10.3969/j.issn.1673-4785.201310057]
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基于BP神经网络的粒子滤波算法
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第9卷
期数:
2014年06期
页码:
709-713
栏目:
出版日期:
2014-12-25

文章信息/Info

Title:
A particle filtering algorithm based on the BP neural network
作者:
王尔申 李兴凯 庞涛
沈阳航空航天大学 电子信息工程学院, 辽宁 沈阳 110136
Author(s):
WANG Ershen LI Xingkai PANG Tao
School of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China
关键词:
粒子滤波粒子退化重采样BP神经网络
Keywords:
particle filterparticle degeneracyresamplingBP neural network
分类号:
TP391
DOI:
10.3969/j.issn.1673-4785.201310057
文献标志码:
A
摘要:
针对粒子滤波算法中的粒子退化问题,提出了一种基于BP神经网络的提高粒子滤波多样性的算法。利用BP神经网络的非线性映射功能,通过对权值进行分裂、选择,将粒子中的小权值粒子状态作为神经网络的输入,粒子的权值作为神经网络的权值,以观测值作为神经网络的目标信号,通过多次训练增大小权值粒子的权值,从而提高粒子滤波算法粒子的多样性,改善算法的滤波性能。仿真结果表明:基于BP神经网络的粒子滤波算法的性能在有效粒子数和均方根误差参数方面优于基本粒子滤波算法,在改善滤波精度方面得到了较好的效果,验证了BP神经网络在改进粒子滤波算法中的有效性。
Abstract:
Aiming at the particle degeneracy phenomena in particle filtering algorithms, a particle filtering algorithm based on the BP neural network is presented for improving the diversity of particles. This algorithm utilizes the nonlinear mapping function of the BP neural network. First of all, to sample particles from the importance density function of particle weight division, the weighted particle is split into two small weight particles. Next, the weight of very small particles is abandoned and the particles with smaller weight are adjusted using the neural network. The state of the remaining small weight particles is used as the input of neural network. The weights of particles are treated as the weights of neural network by using the observed value as the target signal of the neural network. The weights of many small particles can be increased through many times’ trainings, thereby increasing the diversity of particle samples in the particle filter algorithm. Simulation results showed that the particle filter algorithm based on BP neural network can increase the number of effective particles, reduce the mean square error, and the filtering precision performance is improved. It is proven that this particle filter algorithm based on BP neural network is reliable and effective.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-10-22;改回日期:。
基金项目:国家自然科学基金资助项目(61101161);航空科学基金资助项目(2011ZC54010);辽宁省自然科学基金(联合基金)资助项目(2013024003).
作者简介:王尔申,男,1980年生,副教授,博士,主要研究方向为卫星导航信号处理算法以及航空电子系统。主持多项纵向项目,发表学术论文20余篇;李兴凯,男,1988年生,硕士研究生,主要研究方向为卫星导航信息处理;庞涛,女,1976年生,讲师,主要研究方向为机器人学、人工智能,发表学术论文10篇。
通讯作者:王尔申.E-mail:wes2016@sau.edu.cn.
更新日期/Last Update: 2015-06-16