A Monte Carlo Particle Model Associated with Neural Networks for Tracking Problem
文献类型：期刊论文
| 作者 | Pang, Zhonyu1; Liu, Derong1,2; Jin, Ning1; Wang, Zhuo1
|
| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
 |
| 出版日期 | 2008-12-01 |
| 卷号 | 55期号:11页码:3421-3429 |
| 关键词 | Backpropagation neural networks particle degeneracy particle filters resample-move tracking |
| 英文摘要 | Sequential Monte Carlo (SMC) methods, namely, particle filters, are powerful simulation techniques for Sampling sequentially from a complex probability distribution. SMC can be used to solve some problems associated with nonlinear non-Gatissian probability distribution. Sampling is a key step for these methods and has vital effects on simulation results. Various sampling strategies have been proposed to improve the simulation results of SMC methods, but degeneracy of particles sometimes is very severe so that there are only a few particles having significant weights. Diversity of particle samples is reduced significantly so that only a few particles are used to represent the corresponding probability distribution. This kind of sampling is not reasonable to approximate probability distribution. This paper addresses a new method which can avoid the phenomenon of particle degeneracy. We split particles with very big weights into two small ones and use the strategy of neural network to adjust positions of tail particles in order to increase their weights. Another advantage is that this method can efficiently make simulation results approach the actual object. Our simulation results of the typical tracking problem show that not only the phenomenon of particle degeneracy is effectively avoided but also tracking results are much better titan those of the traditional particle filters. Compared with the move-resample method, our method shows better results under the same conditions. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Engineering, Electrical & Electronic |
| 研究领域[WOS] | Engineering |
| 关键词[WOS] | STATE ESTIMATION ; SEQUENTIAL IMPUTATIONS ; BAYESIAN-INFERENCE ; DYNAMIC-SYSTEMS ; FILTERS ; SIMULATION ; ALGORITHM ; CHANNELS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000261847400006 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9583]  |
| 专题 | 自动化研究所_09年以前成果 |
| 作者单位 | 1.Univ Illinois, Dept Elect & Comp Engn, Chicago, IL 60607 USA 2.Chinese Acad Sci, Key Lab Complex Syst & Intelligence Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pang, Zhonyu,Liu, Derong,Jin, Ning,et al. A Monte Carlo Particle Model Associated with Neural Networks for Tracking Problem[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS,2008,55(11):3421-3429. |
| APA | Pang, Zhonyu,Liu, Derong,Jin, Ning,&Wang, Zhuo.(2008).A Monte Carlo Particle Model Associated with Neural Networks for Tracking Problem.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS,55(11),3421-3429. |
| MLA | Pang, Zhonyu,et al."A Monte Carlo Particle Model Associated with Neural Networks for Tracking Problem".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 55.11(2008):3421-3429. |
入库方式： OAI收割
来源：自动化研究所
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