On using nearest neighbours with the probabilistic data association filter

Colegrove, S.B.; Davey, Samuel J.

doi:10.1109/radar.2000.851804

Cited by 26 publications

(33 citation statements)

References 1 publication

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“…This prevents the candidate tracker from running a candidate track on the same target as is represented by an established track. This is the approach used by the integrated probabilistic data association filter (IPDAF) which augments the target state to include a track quality variable, variously referred to as observability [16], existence [17], perceivability [18], and visibility [19]. The probability of this quality variable (which is binary) is used as a test statistic for track maintenance decisions, e.g.…”

Section: Track Maintenancementioning

confidence: 99%

“…Substituting (4) and (11), the logfP(K j D)g term in (16) can be written as shown in (17) where w mtr (D t ) is defined as …”

Section: Hysteresis As Missing Datamentioning

confidence: 99%

“…When a candidate track is valid, then it is detected with probability Pd and if it is detected, then it forms one measurement. Thus, for a valid candidate, the true a priori probability that the rth measurement at scan t is due to model m, i.e., ¼ This special case, with a binary assignment state model, is the same as the visibility/existence/perceivability model used for initiation with the PDAF and other filters [16][17][18]23].…”

Section: Maintenance Using the Hysteresis Modelmentioning

confidence: 99%

“…In earlier work [15], it was shown that the existence model used for track maintenance with probabilistic data association based trackers such as [16,17,18,19] can be incorporated into the PMHT through the assignment prior. A track that corresponds to a real target has a non-zero probability of producing a measurement, whereas a track that does not correspond to a target cannot produce a measurement.…”

Section: Introductionmentioning

confidence: 99%

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Integrated track maintenance for the PMHT via the hysteresis model

Davey

Gray

2007

IEEE Trans. Aerosp. Electron. Syst.

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Section: Track Maintenancementioning

confidence: 99%

“…Substituting (4) and (11), the logfP(K j D)g term in (16) can be written as shown in (17) where w mtr (D t ) is defined as …”

Section: Hysteresis As Missing Datamentioning

confidence: 99%

Section: Maintenance Using the Hysteresis Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated track maintenance for the PMHT via the hysteresis model

Davey

Gray

2007

IEEE Trans. Aerosp. Electron. Syst.

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“…In OTHR, a significant problem is the effect of multipath propagation, which causes multiple detections via different propagation paths for a target with missed detections and false alarms at the receiver [1][2][3][4][5][6]. Nevertheless, the conventional tracking algorithms, such as probabilistic data association (PDA) [7][8][9], presume that a single-measurement per target, it may consider the other measurements of the same target as clutter, and multiple tracks are produced when a single target is present. Therefore, these methods cannot effectively solve the multipath propagation problem.…”

Section: Introductionmentioning

confidence: 99%

Gaussian mixture probability hypothesis density filter for multipath multitarget tracking in over-the-horizon radar

Qin

Chen

et al. 2015

EURASIP J. Adv. Signal Process.

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Conventional multitarget tracking systems presume that each target can produce at most one measurement per scan. Due to the multiple ionospheric propagation paths in over-the-horizon radar (OTHR), this assumption is not valid. To solve this problem, this paper proposes a novel tracking algorithm based on the theory of finite set statistics (FISST) called the multipath probability hypothesis density (MP-PHD) filter in cluttered environments. First, the FISST is used to derive the update equation, and then Gaussian mixture (GM) is introduced to derive the closed-form solution of the MP-PHD filter. Moreover, the extended Kalman filter (EKF) is presented to deal with the nonlinear problem of the measurement model in OTHR. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.

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A message passing approach for multiple maneuvering target tracking

Lan

Wang

et al. 2020

Signal Processing

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This paper considers the problem of detecting and tracking multiple maneuvering targets, which suffers from the intractable inference of high-dimensional latent variables that include target kinematic state, target visibility state, motion mode-model association, and data association. A unified message passing algorithm that combines belief propagation (BP) and mean-field (MF) approximation is proposed for simplifying the intractable inference. By assuming conjugate-exponential priors for target kinematic state, target visibility state, and motion mode-model association, the MF approximation decouples the joint inference of target kinematic state, target visibility state, motion mode-model association into individual low-dimensional inference, yielding simple message passing update equations. The BP is exploited to approximate the probabilities of data association events since it is compatible with hard constraints. Finally, the approximate posterior probability distributions are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue between the estimations of target kinematic state and target visibility state and decisions on motion mode-model association and data association. The performance of the proposed algorithm is demonstrated by comparing with the well-known multiple maneuvering target tracking algorithms, including interacting multiple model joint probabilistic data association, interacting multiple model hypothesis-oriented multiple hypothesis tracker and multiple model generalized labeled multi-Bernoulli.

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On using nearest neighbours with the probabilistic data association filter

Cited by 26 publications

References 1 publication

Integrated track maintenance for the PMHT via the hysteresis model

Integrated track maintenance for the PMHT via the hysteresis model

Gaussian mixture probability hypothesis density filter for multipath multitarget tracking in over-the-horizon radar

A message passing approach for multiple maneuvering target tracking

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