[1]WANG Ershen,LI Xingkai,PANG Tao.A particle filtering algorithm based on the BP neural network[J].CAAI Transactions on Intelligent Systems,2014,9(6):709-713.[doi:10.3969/j.issn.1673-4785.201310057]
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A particle filtering algorithm based on the BP neural network
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
9
Number of periods:
2014 6
Page number:
709-713
Column:
学术论文—机器学习
Public date:
2014-12-25
- Title:
- A particle filtering algorithm based on the BP neural network
- Keywords:
- particle filter; particle degeneracy; resampling; BP neural network
- CLC:
- TP391
- DOI:
- 10.3969/j.issn.1673-4785.201310057
- 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.
- References:
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Last Update:
2015-06-16