A Multiple Extended Target Generalized Labeled Multi-Bernoulli Filter Based on Joint Likelihood Function

LIU Yiduo; JI Hongbing; ZHANG Yongquan

doi:10.11999/JEIT220213

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1303-1312

LIU Yiduo, JI Hongbing, ZHANG Yongquan. A Multiple Extended Target Generalized Labeled Multi-Bernoulli Filter Based on Joint Likelihood Function[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1303-1312. doi: 10.11999/JEIT220213

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LIU Yiduo, JI Hongbing, ZHANG Yongquan. A Multiple Extended Target Generalized Labeled Multi-Bernoulli Filter Based on Joint Likelihood Function[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1303-1312. doi: 10.11999/JEIT220213

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A Multiple Extended Target Generalized Labeled Multi-Bernoulli Filter Based on Joint Likelihood Function

doi: 10.11999/JEIT220213

School of Electronic Engineering, Xidian University, Xi’an 710077, China

Funds: The National Natural Science Foundation of China (61871301), China Postdoctoral Science Foundation (2020T130494, 2018M633470), The Fundamental Research Funds for the Central Universities (XJS210211)

Received Date: 2022-03-01
Rev Recd Date: 2022-07-08

Available Online: 2022-07-15

Publish Date: 2023-04-10

Abstract

Abstract

High-resolution radar systems monitor multiple extended targets with different shapes in a surveillance area. Reliable shapes estimation can effectively improve tracking performance and are crucial to battle-field situation evaluations. In this paper, a Joint Likelihood based Generalized Labeled Multi-Bernoulli (JL-GLMB) filter is proposed to estimate accurately the number of targets, target tracks, and target shapes. Firstly, the extended target is modeled as a star-convex set, and Gaussian components in the GLMB density are updated by the measurement transformation filter to improve the accuracy of state estimation. Then, a joint likelihood function is constructed by log-weighted fusion strategy to measure comprehensively the similarity between extended target and measurement cell. Finally, a fast approximation method for posterior probability density is proposed based on Gibbs sampling, which improves the accuracy and efficiency of the data association. Simulation results show that the proposed algorithm can effectively estimate multiple extended target states of different shapes, and provide stable cardinality estimation in the clutter environment compared to traditional multiple extended target tracking.
- Multiple extended target tracking,
- Random finite set,
- Star-convex set model,
- Nonlinear estimation

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References(22)

References

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