Gaussian mixtures in multi-target tracking: a look at Gaussian mixture probability hypothesis density and integrated track splitting

Song, Taek Lyul; Icki, D. Mus; Kim, D.S.; Radosavljević, Zvonko

doi:10.1049/iet-rsn.2011.0263

Cited by 17 publications

(14 citation statements)

References 14 publications

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“…In order to increase accuracy of tracks (track position and velocity) we proposed the Interacting Multiple Model [7]. If any new tracks are found, the initial parameter estimates for these tracks are extracted from the curves corresponding to the points detected in the normalized set of measurements [8,9].…”

Section: List Of Acronymsmentioning

confidence: 99%

An approach of track managements system in software defined radar

Radosavljević¹,

Ivković²

2018

Sci Tech Rev

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Each multi target tracking system must have a track management procedure. It is an integral part of tracking systems and often used together with data association, state estimation and other parts of systems. On the other hand, the family of C-band frequency agile, low to medium altitude pulse Doppler air search radars, are designed to detect low-altitude, low cross-section aircraft targets in conditions of clutter and electronic countermeasures. Within the framework of its modernization, the receiver is conceived on the basis of software defined radar. The implementation of target tracking system within software defined radar, is also suggested. The paper describes the procedure of track management system procedure in the framework of multi target tracking system. It includes complete procedure of initialization, maintenance and deletion of tracks. Part of the numerical experiments is given at the end of the paper, in order to describe a good property functional of the TMS system

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Section: List Of Acronymsmentioning

confidence: 99%

An approach of track managements system in software defined radar

Radosavljević¹,

Ivković²

2018

Sci Tech Rev

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“…Each track component propagates linearly due to (3): (13) where denotes the Gaussian pdf of random variable with mean and covariance and (14) where denotes the standard Kalman filter propagation. As the measurement archives do not change in propagation, neither do the relative component probabilities and (15) 4) fITS Measurement Selection: At scan each track component selects , where, if is true and the target is detected, target measurement with gating probability .…”

Section: A Forward or Standard Its (Fits)mentioning

confidence: 99%

“…The probability of target existence is used as the track quality measure in the ITS [7]. The Probability Hypothesis Density (PHD) based on the finite set statistics provides estimates of the number of targets and the target trajectories without a track to measurement association [12], [13].…”

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confidence: 99%

Smoothing Multi-Scan Target Tracking in Clutter

Mušicki

Song

Kim

2013

IEEE Trans. Signal Process.

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This paper presents a fixed interval smoothing multiscan algorithm for target tracking in clutter. Both the probability of target existence and the target trajectory probability density function are calculated using all available measurements. This improves both the false track discrimination and the target trajectory estimate. The fixed interval smoothing fuses the forward and the backward multi-scan predictions, to obtain the smoothing predictions and smoothing innovations. Both trajectory estimates and the data association probabilities are calculated using the smoothing innovations. An overlapping batch procedure is described which limits the smoothing delay.

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“…The multitarget tracking algorithms also consider targets followed by other tracks as possible measurement sources [1,2,4,9]. Other notable approaches to multitarget tracking include the probability hypothesis density based on the random set statistics [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Multitarget tracking with state dependent detection

Song

Mušicki

Kim³

2015

IET Radar, Sonar & Navigation

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Target tracking determines the number and the trajectories of targets in the surveillance area. The sensors provide an imperfect target detection and measurement environment, complicated by clutter measurements. The vast majority of published target tracking algorithms assume that the probability of target detection is independent of the trajectory state. This assumption is often incorrect, with subsequent degradation in the target tracking performance. The authors present two multitarget trackers which incorporate the state-dependent probability of target detection, each of which improves upon the state of the art capabilities of target tracking in such environments.

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Gaussian mixtures in multi-target tracking: a look at Gaussian mixture probability hypothesis density and integrated track splitting

Cited by 17 publications

References 14 publications

An approach of track managements system in software defined radar

An approach of track managements system in software defined radar

Smoothing Multi-Scan Target Tracking in Clutter

Multitarget tracking with state dependent detection

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