Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking

YANG Feng; ZHANG Wanying

doi:10.11999/JEIT160467

Volume 39 Issue 3

Mar. 2017

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YANG Feng, ZHANG Wanying. Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467

Citation:

YANG Feng, ZHANG Wanying. Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467

YANG Feng, ZHANG Wanying. Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467

Citation:

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Multiple Model Bernoulli Particle Filter for Maneuvering Target Tracking

doi: 10.11999/JEIT160467

YANG Feng¹,
ZHANG Wanying¹

Funds:

The National Natural Science Foundation of China (61135001, 61374159, 61374023), Seed Foundation of Innovation and Creation of Graduate Students in Northwestern Polytechnical University (Z2016149)

Received Date: 2016-05-09
Rev Recd Date: 2016-11-28
Publish Date: 2017-03-19

Abstract

Abstract

Interacting Multiple Model Bernoulli Particle Filter (IMMBPF) is suitable for maneuvering target tracking under cluttered environment. However, when model information is introduced into particle sampling process in IMMBPF, it will lead to the number decline of particles which are applied to approaching the real state and model, and the computation load is heavy because of the interacting stage of particles in the recursion. An enhanced Multiple Model Bernoulli Particle Filter (MMBPF) is proposed to improve the effectiveness of single particle to approximate the real target state and model. The number of particles of each model is given in advance, and the posterior probability of each model is updated with the associate likelihood function, which avoids particle degeneracy without distorting the Markov property. Simulation results show that the proposed MMBPF achieves better tracking performance with fewer particles than IMMBPF.
- Maneuvering target tracking,
- Bernoulli filter,
- Particle filter,
- Multiple model

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References(16)

References

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