Tracking Multiple Maneuvering Targets Hidden in the DBZ Based on the MM-GLMB Filter

Wu, Weihua; Sun, Hongyu; Cai, Yonghua; Jiang, Surong; Jia-jun, Xiong

doi:10.1109/tsp.2020.2988635

“…Although the H-GLMB and H-LMB densities are in the same form as the standard GLMB and LMB ones (Vo BT and Vo, 2013) (Vo BT and Vo, 2013), H-LMB is also a special case of H-GLMB with one term (Wu et al, 2020).…”

Section: Remarkmentioning

confidence: 99%

Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems

Wu¹,

Cai²,

Jin³

et al. 2021

Front Inform Technol Electron Eng

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In this study, we extend traditional (single-target) hybrid systems to multi-target hybrid systems with a focus on the multi-maneuvering-target tracking system. This system consists of a continuous state, a discrete and switchable state, and a discrete, time-constant, and unique state. By defining a new generalized labeled multi-Bernoulli density, we prove that it is closed under the Chapman-Kolmogorov prediction and Bayes update for multi-target hybrid systems. In other words, we provide the exact derivation of a solution to this system, i.e., the multi-model generalized labeled multi-Bernoulli filter, which has been developed without strict proof.

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“…This work is an attempt to track multiple maneuvering targets hidden in the DBZ. The basic idea here has been presented at a conference in [56], and has been applied to the MM-GLMB filter [57].…”

Section: B Related Workmentioning

confidence: 99%

Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

Wu¹

2020

Preprint

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<a></a><a></a><a>For a ground moving target indication (GMTI) radar, the presence of </a><a></a><a></a><a></a><a>Doppler blind zone (DBZ)</a> results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.

show abstract

“…This work is an attempt to track multiple maneuvering targets hidden in the DBZ. The basic idea here has been presented at a conference in [56], and has been applied to the MM-GLMB filter [57].…”

Section: B Related Workmentioning

confidence: 99%

Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

Wu¹

2020

Preprint

Self Cite

3

0

View full text Add to dashboard Cite

<a></a><a></a><a>For a ground moving target indication (GMTI) radar, the presence of </a><a></a><a></a><a></a><a>Doppler blind zone (DBZ)</a> results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.

show abstract

Tracking Multiple Maneuvering Targets Hidden in the DBZ Based on the MM-GLMB Filter

Cited by 33 publications

References 58 publications

Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems

Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems

Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

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