Multi-GMTI decentralized tracking via consensus LMB density fusion

Wu, Weihua; Sun, Hongyu; Huang, Wenzhun; Zheng, Mao; Feng, Xinxi

doi:10.1109/iccais52680.2021.9624574

2021 International Conference on Control, Automation and Information Sciences (ICCAIS) 2021

DOI: 10.1109/iccais52680.2021.9624574

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Multi-GMTI decentralized tracking via consensus LMB density fusion

Weihua Wu¹,

Hongyu Sun²,

Wenzhun Huang³

et al.

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“…Compared to the two algorithms mentioned above, the MB filter has an advantage in terms of accuracy and computational complexity. It is worth noting that the above filters cannot provide identity information for multi-object estimation, and the existence of this problem drives the development of label RFS [22][23][24].…”

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confidence: 99%

Multiple fluctuating targets track-before-detect using multi-Bernoulli filter in radar sensor

Deng³

et al. 2022

EURASIP J. Adv. Signal Process.

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This paper addresses the detection and tracking of multiple fluctuating targets for a track-before-detect algorithm based on the Multi-Bernoulli (MB-TBD) filter in surveillance radar systems. MB-TBD usually considers target amplitude information and ignores the fact that radar measurements are complex-valued. In this paper, we first propose to utilize phase information to improve the discrimination of targets from noise. More precisely, complex likelihood ratios are used instead of squared modulus measurements likelihood ratios for fluctuations of types Swerling 0, 1, 3. Secondly, the traditional MB-TBD filter cannot solve the problem of coexistence between targets with stronger amplitude and weaker amplitude when multiple fluctuating targets are moving. To address this limitation, an adaptive birth distribution based on joint successive target cancellation and measurement likelihood ratio driven is proposed. Moreover, in order to reduce computational complexity, the Bernoulli components of the same targets are merged after the MB-TBD updating. Finally, the proposed algorithm is implemented using Sequential Monte Carlo technology. The simulation results show that in challenging scenarios, the performance of the improved algorithm is better than the traditional algorithm, and it has a good application prospect.

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confidence: 99%

Multiple fluctuating targets track-before-detect using multi-Bernoulli filter in radar sensor

Deng³

et al. 2022

EURASIP J. Adv. Signal Process.

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A multi-sensor-system cooperative scheduling method for ground area detection and target tracking

Zhang

Shan

2023

Front Inform Technol Electron Eng

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Multi-GMTI decentralized tracking via consensus LMB density fusion

Cited by 2 publications

References 26 publications

Multiple fluctuating targets track-before-detect using multi-Bernoulli filter in radar sensor

Multiple fluctuating targets track-before-detect using multi-Bernoulli filter in radar sensor

A multi-sensor-system cooperative scheduling method for ground area detection and target tracking

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