An efficient implementation of multiple weak targets tracking filter with labeled random finite sets for marine radar

Cao, Chenghu; Zhao, Yongbo; Pang, Xiaojiao; Suo, Zhiling; Chen, Sheng

doi:10.1016/j.dsp.2020.102710

Cited by 21 publications

(9 citation statements)

References 33 publications

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“…One group [24,25] involves running an additional association method based on the estimates outputted by the filter. The other solution [26][27][28][29] extends a label to the state estimate. However, when targets are in close proximity to each other, in a dense clutter, or misdetected, maintaining the correct multitarget trajectories is difficult, although the accuracy of the estimates of the target number has been increased significantly by improving multitarget state extraction strategies [33][34][35][36][37].…”

Section: Brief Survey Of Related Workmentioning

confidence: 99%

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A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets

Gao

Jiang

Zhang

et al. 2021

Sensors

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In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity.

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Section: Brief Survey Of Related Workmentioning

confidence: 99%

“…Furthermore, the PHD filter is known to produce highly uncertain estimates of the target number [ 21 , 23 ]. In recent years, several modifications have been proposed for the trajectories of targets [ 22 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets

Gao

Jiang

Zhang

et al. 2021

Sensors

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“…Subsequently, a few improvements of GLMB have appeared in refs. [20][21][22][23][24]. In this paper, an approach based on Gibbs sampling of δ-GLMB filter eliminates the low efficiency in the truncation process of prediction and update [25].…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive δ‐generalised labelled multi‐Bernoulli filter for multi‐target tracking with heavy‐tailed noise

Jing

2022

IET Radar Sonar & Navi

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Conventional δ‐generalised labelled multi‐Bernoulli filter (δ‐GLMB) cannot deal with the problem of the heavy‐tailed process noise and measurement noise. In order to solve this problem, an adaptive δ‐GLMB approach based on minimising Kullback‐Leibler Divergence (KLD) is proposed in this study. The inverse wishart and Student‐t mixture is used to approximate the joint posterior distribution of process noise covariance and measurement noise covariance together with multi‐target state, and the multi‐target state and noise parameters are jointly estimated by minimising the KLD. The simulation results show that the proposed approach is robust to multi‐target tracking for condition with heavy‐tailed process noise covariance and measurement noise covariance. Accurate target number and target state estimation are obtained by effectively estimating the process noise covariance and measurement noise covariance.

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“…Recently, the δ-generalized labeled multi-Bernoulli (δ-GLMB) filter was proposed in [10] and [11]. Its efficient implementation called the rapid GLMB (R-GLMB) filter [12] and efficient approximation called the labeled multi-Bernoulli (LMB) filter [13] were developed to reduce the high computational complexity of the δ-GLMB filter.A number of extensions of the δ-GLMB filter for diverse appli-cations have been reported in [14]- [21] to track the space debris [16], spawning object [17], multiple maneuvering targets [18], targets under glint noise [19], extended targets or group targets [20], and multiple weak targets [21]. The main advantages of the R-GLMB filter over the PHD filter and CBMeMBer filter are that it may provide object tracks and that it is applicable to the case of high clutter density and low detection probability [12]- [13], [22].…”

Section: Introductionmentioning

confidence: 99%