A Generalized Labelled Multi-Bernoulli Filter for Extended Targets With Unknown Clutter Rate and Detection Profile

Li, Cuiyun; Fan, Zehao; Shi, Renzheng

doi:10.1109/access.2020.3036900

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…At the opposite of coarse PHD filters, PHD trackers are implemented to manage track labels (identifications) through time ( [8]). GLMB is the most advanced one [10].…”

Section: Current Tracking Techniques and Clutter Suppression Methodsmentioning

confidence: 99%

Adaptive PHD-track oriented tracker: applications in infrared detection contexts

Maltese

2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

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Nowadays, multi target trackers have to be more self-adaptive to face pluralities of detection and clutter contexts. For instance, current naval surveillance systems have to deal with different kinds of threats including asymmetric targets for suicide actions or harassing missions. Moreover, time and space fluctuating heavy clutters have to be considered (solar reflection on sea, ground…). Usually, dedicated clutter maps techniques are implemented to adapt tracker parameters to local detection statistics and false alarms rates.In this paper, a self-adaptive tracking concept is proposed. It combines a PHD (Probabilistic Hypothesis Density) filter for clutter suppression, with a current track-oriented tracker. The first module adapts the functioning point of the terminal tracker to low/medium clutter situations.Regarding new algorithms under study, results are provided with infrared registered maritime scenes. They put forward performances that are achieved.

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“…At the opposite of coarse PHD filters, PHD trackers are implemented to manage track labels (identifications) through time ( [8]). GLMB is the most advanced one [10].…”

Section: Current Tracking Techniques and Clutter Suppression Methodsmentioning

confidence: 99%

Adaptive PHD-track oriented tracker: applications in infrared detection contexts

Maltese

2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

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“…Reference [28] proposed a resolvable group targets tracking algorithm based on graph theory and GLMB filter. In addition, some other group targets tracking algorithms based on GLMB filter have been proposed in recent years, such as literature [29] proposed a finite mixture modeling and tracking learning algorithm, literature [30] proposed a multi-extended targets tracking algorithm, and literature [31] proposed a multiple extended targets-based GLMB spline (ET-GLMB-S) filter.…”

mentioning

confidence: 99%

Parameter estimation and tracking algorithm for resolvable group targets based on GLMB filter and RLS algorithm

Liu

Zhao

Liu

et al. 2022

EURASIP J. Adv. Signal Process.

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Current resolvable group targets tracking algorithms can only track a small number of group targets assuming the standard deviation vectors (SDVs) are known. Based on generalized labeled multi-Bernoulli filter, this paper first proposes a serial tracking algorithm, in order to save the computational cost of filter tracking large-batch and multi-structure group targets. Then, the least squares and the k-means algorithms are used to jointly estimate the SDVs and the group target states. The experimental simulation results verify the effectiveness of the proposed methods.

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Robust Poisson multi-Bernoulli mixture filter using adaptive birth distributions for extended targets

Zhou

Xie

et al. 2022

Digital Signal Processing

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A Generalized Labelled Multi-Bernoulli Filter for Extended Targets With Unknown Clutter Rate and Detection Profile

Cited by 4 publications

References 29 publications

Adaptive PHD-track oriented tracker: applications in infrared detection contexts

Adaptive PHD-track oriented tracker: applications in infrared detection contexts

Parameter estimation and tracking algorithm for resolvable group targets based on GLMB filter and RLS algorithm

Robust Poisson multi-Bernoulli mixture filter using adaptive birth distributions for extended targets

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