Track-before-detect labeled multi-bernoulli particle filter with label switching

García-Fernández, Ángel F.

doi:10.1109/taes.2016.150343

Cited by 36 publications

(19 citation statements)

References 36 publications

(89 reference statements)

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“…The marginal density of π k|k−1 (·) is denoted as b τ (·). In the following, we show that it is equal to π k|k−1 τ (·), see (38). Using Theorem 11, b τ (y) = δ τ k (X) (y) π k|k−1 (X) δX…”

Section: B Updatementioning

confidence: 89%

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Multiple Target Tracking Based on Sets of Trajectories

García-Fernández

Svensson

Morelande

2020

IEEE Trans. Aerosp. Electron. Syst.

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101

112

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We propose a solution of the multiple target tracking (MTT) problem based on sets of trajectories and the random finite set framework. A full Bayesian approach to MTT should characterise the distribution of the trajectories given the measurements, as it contains all information about the trajectories. We attain this by considering multi-object density functions in which objects are trajectories. For the standard tracking models, we also describe a conjugate family of multitrajectory density functions.

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Section: B Updatementioning

confidence: 89%

“…Classical MHT algorithms [5], [6], [10] were developed for the standard measurement model but not for general trackbefore-detect models [38]. They rely on enumerating multiple target/measurement association hypotheses, calculating their probabilities and the density of the current target state given a hypothesis.…”

Section: B Relation With Mht Algorithmsmentioning

confidence: 99%

Multiple Target Tracking Based on Sets of Trajectories

García-Fernández

Svensson

Morelande

2020

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

101

112

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“…Since the traditional algorithms require complex data association and high computational complexity, the tracking algorithms based on the random finite set (RFS) have attracted extensive attention recently. Many RFS tracking algorithms have been proposed, such as the Bernoulli filter [5], the probability hypothesis density (PHD) filter [6,7], the cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter [8,9], and the labeled multi-Bernoulli (LMB) filter [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Box-particle Cardinality Balanced Multitarget Multi-Bernoulli Filter for Multipath Multitarget Tracking in OTHR

Qin¹

2019

Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

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This paper proposes a novel box-particle multipath cardinality balanced multitarget multi-Bernoulli (BP-MPCBMeMBer) filter for the problem of multitarget tracking in Over-the-horizon radar (OTHR). The proposed algorithm combines the multipath cardinality balanced multitarget multi-Bernoulli (MPCBMeMBer) filter with interval analysis to solve the problems of high computational complexity and nonlinear measurement models. First, establishment of the OTHR measurement model based on interval analysis, and then the box-particle filter is used to derive the BP-MPCBMeMBer based on the original MPCBMeMBer filter. Eventually, the simulation results are presented to verify the effectiveness of the BP-MPCBMeMBer filter.

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“…Since the random finite set (RFS) algorithms can avoid the data association, which have attracted extensive attention recently. Many target tracking algorithms based on RFS have been proposed, such as the Bernoulli filter [5], the probability hypothesis density (PHD) filter [6][7][8], the cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter [9,10], and the labeled multi-Bernoulli (LMB) filter [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

PHD Filter for Multipath Target Tracking Using Box Particle

Qin¹

2019

Proceedings of the 3rd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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This paper presents a novel box-particle multipath probability hypothesis density (BP-MPPHD) filter for the Over-the-horizon radar (OTHR) target tracking. The proposed filter combines the multipath probability hypothesis density (MPPHD) filter with box particle based on the interval analysis method to solve the problems of high computational complexity and non-linear measurement models in OTHR system. First, the OTHR measurement model is established based on the random finite set, and then the box-particle filter is used to derive the BP-MPPHD filter based on the original MPPHD filter. The simulation results show that the proposed BP-MPPHD filter can track the multipath target effectively with less computational time, compared to the particle implementation of the MPPHD filter for multipath target in OTHR system.

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Track-before-detect labeled multi-bernoulli particle filter with label switching

Cited by 36 publications

References 36 publications

Multiple Target Tracking Based on Sets of Trajectories

Multiple Target Tracking Based on Sets of Trajectories

Box-particle Cardinality Balanced Multitarget Multi-Bernoulli Filter for Multipath Multitarget Tracking in OTHR

PHD Filter for Multipath Target Tracking Using Box Particle

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