Joint tracking and classification of extended object based on support functions

Sun, Lei; Lan, Jian; Li, Xuewei

doi:10.1049/iet-rsn.2017.0499

Cited by 15 publications

(6 citation statements)

References 36 publications

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“…In (20), W refers to a measurement cell obtained currently after the distance partition [6], and |W| is the cardinal number of the set W.…”

Section: Updatementioning

confidence: 99%

“…Recently, non-ellipsoidal extended object models received considerable attention [18,19]. To classify these extended objects by their size and shape, joint tracking and classification algorithms of extended objects have been reported [19,20]. Since the performance of the conventional extended objects tracking degrades significantly under the condition of unknown measurement noise covariance, a new multiple extended object-tracking algorithm based on variational Bayesian (VB) cardinality-balanced multi-object multi-Bernoulli was proposed in [21] without taking object extension into consideration.…”

Section: Introductionmentioning

confidence: 99%

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Cardinalised probability hypothesis density tracking algorithm for extended objects with glint noise

Wang

et al. 2016

IET Science, Measurement & Technology

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“…In (20), W refers to a measurement cell obtained currently after the distance partition [6], and |W| is the cardinal number of the set W.…”

Section: Updatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cardinalised probability hypothesis density tracking algorithm for extended objects with glint noise

Wang

et al. 2016

IET Science, Measurement & Technology

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“…For example, Lan and Li [1] proposed JTC method of extended targets using RMM and the prior information of target size. Based on the support function, Sun et al [17] proposed the JTC method of extended targets using RMM. Magnant et al [18] took advantage of target extent measurements and proposed a JTC algorithm for extended targets.…”

Section: Introductionmentioning

confidence: 99%

Joint Tracking and Classification of Extended Targets Using Random Matrix and Bernoulli Filter for Time-Varying Scenarios

et al. 2019

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In surveillance applications, the extent states and measurements of extended targets received by sensors are time-varying. In this paper, we propose a joint tracking and classification (JTC) method for single extended target under the presence of clutter and detection uncertainty. The extent state is modeled as elliptic shape via random matrix model (RMM), and is used as the feature for target classification. To adapt to the time-varying conditions of an extended target, the RMM proposed by Lan et al. is used. Besides, the RMM is integrated into Bernoulli filter to detect an extended target with clutter and detection uncertainty. The resulting method is called joint tracking and classification Gaussian inverse Wishart Bernoulli (JTC-GIW-Ber) filter, and the closed expressions for JTC-GIW-Ber filter recursions are derived under the necessary assumptions and approximations. Comprehensive simulations are carried out to test the performance, and the results demonstrate that the proposed JTC-GIW-Ber filter not only outperforms the JTC-GIW probability hypothesis density (JTC-GIW-PHD) filter and the GIW-Ber filter in extent state estimation, but also outperforms the JTC-GIW-PHD filter in target classification. INDEX TERMS Bernoulli filter, joint tracking and classification, random matrix model, single extended target.

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“…Therefore, this method cannot extract the shape of the target. Nevertheless, the estimation of the target extent is important because it can be used to classify target and improve tracking accuracy [18,19,20].…”

Section: Introductionmentioning

confidence: 99%

Anti-Clutter Gaussian Inverse Wishart PHD Filter for Extended Target Tracking

Huang

Wang

2019

Sensors

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The extended target Gaussian inverse Wishart probability hypothesis density (ET-GIW-PHD) filter overestimates the number of targets under high clutter density. The reason for this is that the source of measurements cannot be determined correctly if only the number of measurements is used. To address this problem, we proposed an anti-clutter filter with hypothesis testing, we take into account the number of measurements in cells, the target state and spatial distribution of clutter to decide whether the measurements in cell are clutter. Specifically, the hypothesis testing method is adopted to determine the origination of the measurements. Then, the likelihood functions of targets and clutter are deduced based on the information mentioned above, resulting in the likelihood ratio test statistic. Next, the likelihood ratio test statistic is proved to be subject to a chi-square distribution and a threshold corresponding to the confidence coefficient is introduced and the measurements below this threshold are considered as clutter. Then the correction step of ET-GIW-PHD is revised based on hypothesis testing results. Extensive experiments have demonstrated the significant performance improvement of our proposed method.

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Joint tracking and classification of extended object based on support functions

Cited by 15 publications

References 36 publications

Cardinalised probability hypothesis density tracking algorithm for extended objects with glint noise

Cardinalised probability hypothesis density tracking algorithm for extended objects with glint noise

Joint Tracking and Classification of Extended Targets Using Random Matrix and Bernoulli Filter for Time-Varying Scenarios

Anti-Clutter Gaussian Inverse Wishart PHD Filter for Extended Target Tracking

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