Controlling track coalescence with scaled Joint Probabilistic Data Association

Kennedy, Hugh L.

doi:10.1109/radar.2008.4653963

Cited by 18 publications

(11 citation statements)

References 12 publications

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“…The dramatic pruning used for ENNPDA, however, leads to an undesired sensitivity to clutter and missed detections [10]. Kennedy’s paper [11] proposed a new scaled JPDA algorithm which introduced an arbitrary positive scaling factor. When the scale factor value is infinity, the algorithm is equivalent to the ENNPDA algorithm, and when the scale factor value is one, the algorithm is equivalent to the JPDA algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Xiao¹,

Li²,

Li³

et al. 2016

Sensors

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The problem of data association for target tracking in a cluttered environment is discussed. In order to improve the real-time processing and accuracy of target tracking, based on a probabilistic data association algorithm, a novel data association algorithm using distance weighting was proposed, which can enhance the association probability of measurement originated from target, and then using a Kalman filter to estimate the target state more accurately. Thus, the tracking performance of the proposed algorithm when tracking non-maneuvering targets in a densely cluttered environment has improved, and also does better when two targets are parallel to each other, or at a small-angle crossing in a densely cluttered environment. As for maneuvering target issues, usually with an interactive multi-model framework, combined with the improved probabilistic data association method, we propose an improved algorithm using a combined interactive multiple model probabilistic data association algorithm to track a maneuvering target in a densely cluttered environment. Through Monte Carlo simulation, the results show that the proposed algorithm can be more effective and reliable for different scenarios of target tracking in a densely cluttered environment.

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Section: Introductionmentioning

confidence: 99%

A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Xiao¹,

Li²,

Li³

et al. 2016

Sensors

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“…the factor nodes will send messages to variable nodes according to (6) and (7). The messages passed through each edge at 0 = t can be obtained as, …”

Section: A Message Passing Schedulementioning

confidence: 99%

“…The goal was to exclude those hypotheses that lead to track coalescence and the method is extended to include integrated track maintenance in [5]. Avoiding track coalescence with appropriate scaling of hypotheses is discussed in [6]. The method described in this paper provides a graphical model framework to obtain refined likelihood values that match the correct association configuration.…”

Section: Introductionmentioning

confidence: 99%

Effective data association scheme for tracking closely moving targets using factor graphs

Panakkal

Velmurugan

2011

2011 National Conference on Communications (NCC)

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Effectiveness of tracking closely moving targets depends on the capability to resolve the ambiguity in associating measurements-to-tracks. Joint probabilistic data association (JPDA) has been shown to be very effective in tracking closely moving objects, but the approach is susceptible to track coalescence. The factor graph (FG) based association scheme developed in this paper circumvents the track coalescence by avoiding multiple hypothesis equivalence with recursive updation of likelihood values. The improvement in association using factor graph based data association scheme over JPDA has been demonstrated using a simulated scenario of closely moving targets. The steady state likelihood values obtained at the end of recursive process are shown to match the likelihoods obtained from measurements.

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“…But this algorithm will lead to track over commitment and an increased incidence of track divergence in the presence of clutter and absent target detections. The paper [6] proposed a Scaled JPDA algorithm which can avoid track coalescence. And I have presented other three modified algorithms avoiding track coalescence: the modified JPDA algorithm based on exclusive measurement and Entropy Value Method (EEJPDA) [7], K-Nearest Neighbor JPDA algorithm (KNNJPDA) [8] and Scaled EEJPDA algorithm (SEEJPDA) [9].…”

Section: Introductionmentioning

confidence: 99%

Comparing of several modified joint probabilistic data association algorithms

Wen

et al. 2013

SPIE Proceedings

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The Joint Probabilistic Data Association algorithm is one of the most widely used Data Association algorithm which can effectively finish multi-target tracking in clutter environment. But it will cause track coalescence phenomenon in parallel neighboring or small-angle crossing scene. For avoiding track coalescence, four modified Joint Probabilistic Data Association algorithms are introduced in this paper. Through Monte Carlo simulations, it is confirmed that these algorithms all can avoid this problem, but the tracking performances of these algorithms are different. So tracking performances of them in tracking precision, computation and anti-jamming ability are compared through simulation test, which can provide the basis for applying these new algorithms in practical.

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Controlling track coalescence with scaled Joint Probabilistic Data Association

Cited by 18 publications

References 12 publications

A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Effective data association scheme for tracking closely moving targets using factor graphs

Comparing of several modified joint probabilistic data association algorithms

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