A Multi-Bernoulli Filtering Algorithm Using Amplitude Information

YUAN Changshun; WANG Jun; SUN Jinping; SUN Zhongsheng; BI Yanxian

doi:10.11999/JEIT150683

Volume 38 Issue 2

Feb. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(2): 464-471

YUAN Changshun, WANG Jun, SUN Jinping, SUN Zhongsheng, BI Yanxian. A Multi-Bernoulli Filtering Algorithm Using Amplitude Information[J]. Journal of Electronics & Information Technology, 2016, 38(2): 464-471. doi: 10.11999/JEIT150683

Citation:

YUAN Changshun, WANG Jun, SUN Jinping, SUN Zhongsheng, BI Yanxian. A Multi-Bernoulli Filtering Algorithm Using Amplitude Information[J]. Journal of Electronics & Information Technology, 2016, 38(2): 464-471. doi: 10.11999/JEIT150683

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A Multi-Bernoulli Filtering Algorithm Using Amplitude Information

doi: 10.11999/JEIT150683

Funds:

The National Natural Science Foundation of China (61171122, 61201318, 61471019, 61501011), The Fundamental Research Funds for the Central Universities (YWF- 15-GJSYS-068)

Received Date: 2015-06-08
Rev Recd Date: 2015-11-11
Publish Date: 2016-02-19

Abstract

Abstract

In many multi-target tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This amplitude information can be used to improve the multi-target state estimation by obtaining more accurate target and false-alarm likelihoods. In this paper, a novel multi-Bernoulli filtering algorithm is proposed, which is based on the random finite set and incorporate the amplitude information. The amplitude likelihood functions are derived to incorporate the amplitude information into the multi-Bernoulli filter in the update step. In addition, a Gaussian Mixture (GM) implementation for the linear model and a Sequential Monte Carlo (SMC) implementation for the non-linear model are proposed. Simulation results for Gaussian Mixture and Sequential Monte Carlo implementations show that the proposed filter demonstrates a significant improvement than conventional multi-Bernoulli filter in the estimation accuracy of both the number of targets and their states.
- Multi-target tracking,
- Random finite set,
- Amplitude information,
- Multi-Bernoulli filter

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References(24)

References

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