2015
DOI: 10.1109/tsp.2015.2407322
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A Multiple-Detection Probability Hypothesis Density Filter

Abstract: Most conventional target tracking algorithms assume that one target can generate at most one detection per scan. However, in many practical target tracking applications, one target may generate multiple detections in one scan, because of multipath propagation, or high sensor resolution or some other reason. If the multiple detections from the same target can be effectively utilized, the performance of the multitarget tracking system can be improved. However, the challenge is that the uncertainty in the number … Show more

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Cited by 57 publications
(53 citation statements)
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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%
“…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%
“…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%
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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%