Integrated track maintenance for the PMHT via the hysteresis model

Davey, Samuel J.; Gray, Douglas A.

doi:10.1109/taes.2007.357157

Cited by 30 publications

(17 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Roughly speaking, the existence-based model, which is used in JIPDA [19], random finite sets based algorithms [42], etc., is more appropriate for recursive processing whereas the joint PDFs of target kinematic state and target existence state are updated with time. The visibility-based model is often used for batch processing algorithms, such as PMHT-v [20], variational Bayes tracker (VBT) [39], etc. In this paper, we adopt the visibility-based model.…”

Section: Modeling Of Target and Measurementmentioning

confidence: 99%

“…By observing Eqs. (20), (21), (61), (62) in [29], the convergence of the LBP in this part is assured by leveraging the techniques in [29].…”

Section: Derivation Of Belief B a (A)mentioning

confidence: 99%

“…↑ (↓) indicates the higher (lower) value the metric, the better (worse) the performance is. [11,20], and Target 3 and Target 4 maneuver during the time interval [21,30], respectively. In this scenario, P d = 0.95 and λ = 10 −4 .…”

Section: Simulation and Analysismentioning

confidence: 99%

See 2 more Smart Citations

A message passing approach for multiple maneuvering target tracking

Lan

Wang

et al. 2020

Signal Processing

View full text Add to dashboard Cite

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.

show abstract

Section: Modeling Of Target and Measurementmentioning

confidence: 99%

“…By observing Eqs. (20), (21), (61), (62) in [29], the convergence of the LBP in this part is assured by leveraging the techniques in [29].…”

Section: Derivation Of Belief B a (A)mentioning

confidence: 99%

See 1 more Smart Citation

A message passing approach for multiple maneuvering target tracking

Lan

Wang

et al. 2020

Signal Processing

View full text Add to dashboard Cite

show abstract

“…Another closely related concept is to use a separate Markov chain to describe the target presence state as originally introduced for PDA in [16]. This approach has been used for the particle filter [10] and a generalized version was applied to the PMHT [17]. Comparison of detection performance for TBD algorithms mentioned above has been done by Davey, et al [18].…”

Section: Signals Transfer and Processing Systemsmentioning

confidence: 99%

Local trajectory parameters estimation and detection of moving targets in rayleigh noise

et al. 2014

View full text Add to dashboard Cite

The problem of detection of moving targets and estimation of local trajectory parameters based on the analysis of sensor data in the form of two-dimensional image is considered. In accordance with the target and sensor models, probability distribution of noise at the output of the detector is Rayleigh distribution, while probability distribution of signal is Rice distribution. Two trajectory parameters estimation techniques are considered: ordinary least squares and Hough transform. A detection stage based on the integration of an input signal along estimated trajectory is proposed. Statistical modeling was performed and detection characteristics were obtained.

show abstract

“…However, the data-dependent prior is itself an ad-hoc fix, and while its proportionality behavior as a function of the number of counts is certainly necessary, its exact form is arbitrarily chosen. There does not seem to be a rigorously track-managed version of the HPMHT, although the hysteresis idea of Davey and Gray [4] seems appropriate and may have been developed further.…”

Section: Introductionmentioning

confidence: 99%

Multiple target tracking from images using the maximum likelihood HPMHT

Willett¹,

Balachandran²,

Dunham

et al. 2013

SPIE Proceedings

View full text Add to dashboard Cite

In this paper, we address the problem of passive tracking of multiple targets with the help of images obtained from passive infrared (IR) platforms. Conventional approaches to this problem, which involve thresholding, measurement detection, data association and filtering, encounter problems due to target energy being spread across multiple cells of the IR imagery. A histogram based probabilistic multi-hypothesis tracking (H-PMHT) approach provides an automatic means of modeling targets that are spread in multiple cells in the imaging sensor(s) by relaxing the need for hard decisions on measurement detection and data association. Further, we generalize the conventional HPMHT by adding an extra layer of EM iteration that yields the maximum likelihood (ML) estimate of the number of targets. With the help of simulated focal plane array (FPA) images, we demonstrate the applicability of MLHPMHT for enumerating and tracking multiple targets.

show abstract

Integrated track maintenance for the PMHT via the hysteresis model

Cited by 30 publications

References 28 publications

A message passing approach for multiple maneuvering target tracking

A message passing approach for multiple maneuvering target tracking

Local trajectory parameters estimation and detection of moving targets in rayleigh noise

Multiple target tracking from images using the maximum likelihood HPMHT

Contact Info

Product

Resources

About