A Multiple-Detection Probability Hypothesis Density Filter

Tang, Xu; Chen, Xin; McDonald, Michael; Mahler, Ronald; Kirubarajan, Thiagalingam

doi:10.1109/tsp.2015.2407322

Cited by 57 publications

(53 citation statements)

References 51 publications

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“…We here use the hypothesis-oriented framework together with Murty's approximation method [44], [45], which is called MD-HMHT. MD-PHD [9], which is limited to track a few high-value targets due to its high computational cost [9], is not considered here.…”

Section: Simulationmentioning

confidence: 99%

Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach

Lan¹,

Sun

Wang

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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We consider multitarget detection and tracking problem for a class of multipath detection system where one target may generate multiple measurements via multiple propagation paths, and the association relationship among targets, measurements and propagation paths is unknown. In order to effectively utilize multipath measurements from one target to improve detection and tracking performance, a tracker has to handle high-dimensional estimation of latent variables including target active/dormant meta-state, target kinematic state, and multipath data association. Based on variational Bayesian inference, we propose a novel joint detection and tracking algorithm that incorporates multipath data association, target detection and target state estimation in a unified Bayesian framework. The posterior probabilities of these latent variables are derived in a closed-form iterative manner, which is effective for reducing the performance deterioration caused by the coupling between estimation errors and identification errors.Loopy belief propagation is exploited to approximately calculate the probability of multipath data association, saving the computational cost significantly. Simulation results of over-the-horizon radar multitarget tracking show that the proposed algorithm outperforms multihypothesis multipath track fusion and multi-detection (hypothesis-oriented) multiple hypothesis tracker, especially under low signalto-noise ratio circumstance.

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Section: Simulationmentioning

confidence: 99%

Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach

Lan¹,

Sun

Wang

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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“…However, the multi-path tracks are generated as it lacks a means of discrimination. In the OTHR tracking system [25][26][27][28] which is widely used in remote sensing applications, transmission and receiving signals can be scattered by different ionospheric layers which results in different measurement paths (models). The multi-path approach leads to multiple detections generated by the same target, and the relationship between measurements and paths is not prior known which results in the measurement path model uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, due to the incorrect model and measurement combination, multi-path tracks are generated and need to be identified. In [25,27], the multiple detection multiple hypothesis tracking (MD-MHT) algorithm and the multi-detection probability hypothesis density (MD-PHD) algorithm are derived by combining the multiple hypothesis tracking and the random finite set framework. In [29], the Gaussian mixture probability hypothesis density (GM-PHD) filter is extended for multi-path multitarget tracking with the over-the-horizon radar system.…”

Section: Introductionmentioning

confidence: 99%

“…If multiple measurements can be considered in one date association event, the tracking performance can be improved significantly due to the target information being more efficiently extracted. The measurement partition method is introduced to generate the possible target-oriented measurement cells and was embedded in the JPDA and PHD frameworks [27,31]. The measurement partition method assumes that more than one measurement can be generated by one target, forming all the possible target-oriented measurement combinations based both on the validated measurements and on the restriction of the maximum number of target detections.…”

Section: Introductionmentioning

confidence: 99%

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Joint integrated track splitting for multi-path multi-target tracking using OTHR detections

Huang

Song

Lee

2018

EURASIP J. Adv. Signal Process.

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In target tracking applications where an over-the-horizon radar (OTHR) is used to gather measurements, one target may generate more than one measurement at each scan due to the multi-path propagation effect. However, traditional tracking methods obtain the data association probabilities based on track-to-measurement data association events under the assumption that each target can generate at most one detection at each scan, leading to poor performances if these methods are applied to multiple detection applications such as OTHR applications. In this paper, we develop a multi-path multi-target tracking algorithm entitled multiple detection joint integrated track splitting (MD-JITS). This novel algorithm jointly solves the measurement origin uncertainty and measurement path model uncertainty. The probability of target existence (PTE) is utilized in the OTHR application as a track quality measure for true track confirmation and false track discrimination. The data association algorithm of MD-JITS the proposed algorithm is realized based on measurement cells that each measurement cell consists of one or more of the validated measurements, while considering the measurement path model. The proposed algorithm is compared with the multiple detection joint integrated probabilistic data association (MD-JIPDA) algorithm in a multi-target crossing scenario, implementing the OTHR system in the presence of clutter and failed target detections, to demonstrate the desired effectiveness.

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“…More recently, the concept of labeled RFSs has been introduced to cope with a multi-target tracking problem, and its implementations include labeled multi-Bernoulli [7] and generalized labeled multi-Bernoulli [8], [9] approximations. These analytic approximations of a multi-target Bayes filter through an RFS and labeled RFSs have a various scope of applications including radar target tracking [10], [11], computer vision [12], [13], and sensor networks [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Target Birth Intensity Estimation Using Measurement-Driven PHD Filter

Zhang¹,

Ge²,

Yang³

2016

ETRI J

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The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target‐tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement‐driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre‐filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement‐driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

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A Multiple-Detection Probability Hypothesis Density Filter

Cited by 57 publications

References 51 publications

Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach

Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach

Joint integrated track splitting for multi-path multi-target tracking using OTHR detections

Target Birth Intensity Estimation Using Measurement-Driven PHD Filter

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