Multitarget Tracking Using One Time Step Lagged Delta-Generalized Labeled Multi-Bernoulli Smoothing

Liang, Guolong; Li, Quanrui; Qi, Bin; Qiu, Liqing

doi:10.1109/access.2020.2971624

Cited by 5 publications

(8 citation statements)

References 42 publications

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“…On this basis, this paper compared the proposed method with the OL-δ-GLMB smoother (One Time Step Lagged δ-GLMB Smoother [14]) and the FB-LMB smoother (Forward Backward LMB [17]). The authors of [14] provided a detailed comparison of the proposed OL-δ-GLMB smoother, LMB smoother [16], δ-GLMB-A smoother [27] smoother, and δ-GLMB filter [28] and verified its great performance.…”

Section: Multi-target Scene Simulationmentioning

confidence: 99%

“…Compared with the original method, the accuracy of target cardinality and state estimations was improved to a certain extent, and the processing efficiency of the algorithm was further improved due to the reduction of the track pruning threshold. Finally, this paper compares the proposed method with the forward-backward LMB smoother [17] and the One Time Step Lagged δ-GLMB smoother [14] under the same experimental conditions to verify that the proposed method has better tracking performance than existing methods.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Backward Smoothing Method Based on Label Iterative Processing

et al. 2023

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Effective target detection and tracking has always been a research hotspot in the field of radar, and multi-target tracking is the focus of radar target tracking at present. In order to effectively deal with the issue of outlier removal and track initiation determination in the process of multi-target tracking, this paper proposes an improved backward smoothing method based on label iterative processing. This method corrects the loophole in the original backward smoothing method, which can cause estimated target values to be erroneously removed due to missing detection, so that it correctly removes outliers in target tracking. In addition, the proposed method also combines label iterative processing with track initiation determination to effectively eliminate invalid target short-lived tracks. The results of simulation experiments and actual data verification showed that the proposed method correctly removed outliers and invalid short-lived tracks. Compared with the original method, it improved the accuracy of target cardinality estimation and tracking performance to a certain extent. Moreover, without affecting the algorithm performance, the method’s processing efficiency could be improved by increasing the track pruning threshold. Finally, the proposed method was compared with existing methods, verifying that its tracking performance was better than that of existing methods.

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Section: Multi-target Scene Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Backward Smoothing Method Based on Label Iterative Processing

et al. 2023

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“…Moreover, the proposed forward-backward smoother does not specify the form of the multi-object filtering densities. This is in contrast to multi-object forward-backward smoothers based on labelled RFSs [53]- [55], which cannot incorporate the Poisson birth model and require that the multi-object filtering densities must be labelled. The outcome of this work is a method for efficiently sampling multi-object trajectories from the posterior distribution of sets of trajectories, based on operations involving only the single time step multiobject state distributions constructed during forward filtering.…”

Section: Introductionmentioning

confidence: 99%

Multiple Object Trajectory Estimation Using Backward Simulation

Xia

Svensson

García-Fernández

et al. 2022

IEEE Trans. Signal Process.

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This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate trajectories. In this paper, we first derive a general multi-trajectory backward smoothing equation based on random finite sets of trajectories. Then we show how to sample sets of trajectories using backward simulation for Poisson multi-Bernoulli filtering densities, and develop a tractable implementation based on ranked assignment. The performance of the resulting multi-trajectory particle smoothers is evaluated in a simulation study, and the results demonstrate that they have superior performance in comparison to several state-of-the-art multi-object filters and smoothers.

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“…The generalized labeled multi-Bernoulli (GLMB) filter [ 14 , 15 ] leads to an analytic solution to the Bayes multitarget tracker, which significantly improves the accuracy of multitarget state extraction and explicitly accommodates the estimation of target trajectories. Recently, labeled RFS-based filters and smoothers [ 10 , 16 , 17 , 18 , 19 ] have been further developed, for generating track estimates, which is also the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

“…The trajectory Poisson multi-Bernoulli filter and the trajectory Poisson multi-Bernoulli mixture filter [ 18 ] have better filtering accuracy and real-time performance than those of the Gibbs-GLMB filter. A onetime step lagged Bayes multitarget smoother using the

density [ 19 ], which also inherently outputs targets trajectories, outperforms the Gibbs-GLMB filter on both the estimates and target number and state at the cost of higher computational complexity. However, when targets are in a dense clutter, or misdetected, generating the correct multitarget trajectories is difficult in [ 16 , 17 , 18 , 19 ], although the computational complexity problem has been improved in [ 17 , 18 ].…”

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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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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Multitarget Tracking Using One Time Step Lagged Delta-Generalized Labeled Multi-Bernoulli Smoothing

Cited by 5 publications

References 42 publications

An Improved Backward Smoothing Method Based on Label Iterative Processing

An Improved Backward Smoothing Method Based on Label Iterative Processing

Multiple Object Trajectory Estimation Using Backward Simulation

A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets

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