Sequential track extraction

Keuk, G. van

doi:10.1109/7.722699

Cited by 57 publications

(23 citation statements)

References 8 publications

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“…The second model generalises this to allow multiple detections at each time, with at most one detection corresponding to a target. Note that the single observation model could alternatively be interpreted as potentially erroneous output from a fast-time tracker, such as a Kalman filter (see [19] for further details), while the second model is more typical of general tracking scenarios, and similar to that of [1].…”

Section: Hidden Reciprocal Chains (Hrcs)mentioning

confidence: 99%

“…The simulated scenarios are designed to highlight the benefit that can be achieved in both state estimation and detection when source-destination awareness is incorporated into the target model. We compare the HRC tracker (estimator and detector) to existing Hidden Markov chain (HMC) trackers similar to the track extraction models of [1], [2], and also compare to a tracker based on a Schrödinger bridge, which we call a Hidden Schrödinger Chain (HSC) tracker.…”

Section: Numerical Examplesmentioning

confidence: 99%

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Track Extraction With Hidden Reciprocal Chains

Stamatescu

White

Bruce-Doust

2018

IEEE Trans. Automat. Contr.

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This paper develops Bayesian track extraction algorithms for targets modelled as hidden reciprocal chains (HRC). HRC are a class of finite-state random process models that generalise the familiar hidden Markov chains (HMC). HRC are able to model the "intention" of a target to proceed from a given origin to a destination, behaviour which cannot be properly captured by a HMC. While Bayesian estimation problems for HRC have previously been studied, this paper focusses principally on the problem of track extraction, of which the primary task is confirming target existence in a set of detections obtained from thresholding sensor measurements. Simulation examples are presented which show that the additional model information contained in a HRC improves detection performance when compared to HMC models.

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Section: Hidden Reciprocal Chains (Hrcs)mentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Track Extraction With Hidden Reciprocal Chains

Stamatescu

White

Bruce-Doust

2018

IEEE Trans. Automat. Contr.

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“…For the single-target MHT, the likelihood ratio can be shown to be equal to the sum of the unnormalized hypothesis weightsw (j) k , provided that the initial hypothesis weights add to unity [21]:…”

Section: A Track Confirmationmentioning

confidence: 99%

“…In the update step each component (i, j) is assigned to each detection s and updated according to (21), (22), and (25). The weight update factor depends on road segment i, PHD component j, and detection s, and is denoted by g k (i, j, s).…”

Section: Road Map Integrationmentioning

confidence: 99%

GMTI Tracking via the Gaussian Mixture Cardinalized Probability Hypothesis Density Filter

Ulmke¹,

Erdinc²,

Willett

2010

IEEE Trans. Aerosp. Electron. Syst.

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The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with a varying target number in clutter. In particular the Gaussian mixture variant (GMCPHD), which provides closed-form prediction and update equations for the filter in the case of linear Gaussian systems, is a candidate for real time multi-target tracking. The following three issues are addressed. First we show the equivalence between the GMCPHD filter and the standard multi hypothesis tracker (MHT) in the case of a single target. Second by using a Gaussian sum approach, we extend the GMCPHD filter to incorporate digital road maps for road constrained targets. The use of such external information leads to more precise tracks and faster and more reliable target number estimates. Third we model the effect of Doppler blindness by a target state-dependent detection probability, which leads to a more stable target-number estimation in the case of low-Doppler targets.

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“…Paper [9] proposed a detection algorithm of the target trajectory tracking loss while using the multihypothesis tracking algorithm, which employs the sequential criterion of likelihood ratio and takes into account amplitude data. A great computational complexity is a disadvantage of the specified algorithm.…”

Section: Introductionmentioning

confidence: 99%

Sequential detection of target trajectory tracking loss using the decision statistics of pips

Neuimin

Zhuk

2014

Radioelectron.Commun.Syst.

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A sequential algorithm of detection of the tracking loss of target trajectory based on the cumulative sums method has been obtained using the values of decision statistics of pips obtained during the primary processing of radar data. In this case the probabilistic data combining method is used for the estimation of moving target parameters at small values of the signal-to-noise ratio. The obtained algorithm was analyzed by using the statistical simulation of a model case of the target tracking with the data of surveillance radar measuring the range and radial velocity of target. The description of decision statistics of target and noise pips involved the use of the Rice and exponential distribution laws, respectively.

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Sequential track extraction

Cited by 57 publications

References 8 publications

Track Extraction With Hidden Reciprocal Chains

Track Extraction With Hidden Reciprocal Chains

GMTI Tracking via the Gaussian Mixture Cardinalized Probability Hypothesis Density Filter

Sequential detection of target trajectory tracking loss using the decision statistics of pips

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