Multiple hypothesis track maintenance with possibly unresolved measurements

Koch, Wolfgang; Keuk, G. van

doi:10.1109/7.599263

Cited by 154 publications

(78 citation statements)

References 9 publications

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“…The standard multitarget observation model is the most widely used. Other models include the merged or unresolved measurement model (57)(58)(59)(60)(61)(62), where two or more targets can share a detection; extended target/group measurement model, where each target/group can generate multiple detections (63-71); track-before-detect/image measurement model (72)(73)(74)(75)(76)(77), and the superpositional measurement model (78,79), where the observed signal is a superposition of observations generated by each of the targets present. This article considers only the standard multitarget measurement model.…”

Section: Multitarget Systemsmentioning

confidence: 99%

Multitarget Tracking

Mallick²,

Bar‐Shalom

et al. 2015

Wiley Encyclopedia of Electrical and Electronics Engineering

214

150

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Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets and their states or trajectories from noisy sensor measurements. MTT has a long history spanning over 50 years, with a plethora of applications in many fields of study. While numerous techniques have been developed, the three most widely used approaches to MTT are the joint probabilistic data association filter (JPDAF), multiple hypothesis tracking (MHT), and random finite set (RFS). The JPDAF and MHT have been widely used for more than two decades, while the RFS‐based MTT algorithms have received a great deal of attention during the last decade. In this article, we provide an overview of MTT and succinct summaries of popular state‐of‐the‐art MTT algorithms.

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Section: Multitarget Systemsmentioning

confidence: 99%

Multitarget Tracking

Mallick²,

Bar‐Shalom

et al. 2015

Wiley Encyclopedia of Electrical and Electronics Engineering

214

150

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“…to point targets [7][8][9]. But in recent years, the JDE of multiple unresolved target-groups has attracted extensive attention and many approaches have been proposed [10][11][12][13][14][15][16][17][18][19][20][21][22][23] for it.…”

Section: Introductionmentioning

confidence: 99%

Error bounds for joint detection and estimation of multiple unresolved target-groups

Liu

Duan

Yuan

et al. 2016

Digital Signal Processing

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“…For this task, trajectory ÿltering considering the dynamic behaviour of the target is employed for the reÿnement of the trajectory [10,11]. There have been active works for the robust track maintenance under the cluttered or frequent false alarmed environment [12][13][14][15][16]. Prediction, ÿltering and retro-diction activities are involved at this stage [15].…”

Section: Introductionmentioning

confidence: 99%

Automatic detection and tracking of moving image target with CNN‐UM via target probability fusion of multiple features

Kim¹,

Roska²,

Chua³

et al. 2003

Circuit Theory & Apps

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SUMMARYA high speed target detection and tracking algorithm for a CNN-UM chip is presented in this paper. The target conÿdence value is computed based on the fusion of target existence probabilities of features using products of weighted sums. The target decision is done with such a conÿdence value and target initiation is done through the temporal accumulation of the conÿdence. The probability of the target existence for each feature is created in the region of in uence depending on the reliability and the strength of the feature. By virtue of the analogic parallel processing structure of the CNN-UM (Roska T, Chua LO. The CNN universal machine: an analogic array computer. IEEE Trans. Circuits Systems II 1993; CAS-40:163-173), real time tracking can be achieved with presently available technologies with the speed of several kilo-frames per second. Due to the utilization of multiple features of target, robust target detection is possible via the proposed algorithm. On-chip experiments of the proposed targettracking algorithm have been done and properties of the proposed approach are disclosed through the various experiments.

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Multiple hypothesis track maintenance with possibly unresolved measurements

Cited by 154 publications

References 9 publications

Multitarget Tracking

Multitarget Tracking

Error bounds for joint detection and estimation of multiple unresolved target-groups

Automatic detection and tracking of moving image target with CNN‐UM via target probability fusion of multiple features

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