Tracking in a cluttered environment with probabilistic data association

Bar‐Shalom, Yaakov; Tse, Edison

doi:10.1016/0005-1098(75)90021-7

Cited by 795 publications

(376 citation statements)

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“…By accepting such false alarm measurements as correct the uncertainty ellipse decreases more rapidly than when the true measurements are used. Figure 5 shows the result obtained with the Probabilistic Data Association (PDA) filter described in [7,35] using a Poisson clutter model with the clutter density parameters described previously. In performing the estimate of the target tracks for time k using measurements from the three sensors and three sensor pairs, the PDA approach is used for measurements from each sensor (or sensor pair) individually.…”

Section: Resultsmentioning

confidence: 99%

“…This is investigated in [30], where an energy functional is used to impose constraints upon the association matrix in order to estimate measurement-to-target association values. This energy functional is minimized by a Hopfield analog network [31] in order to generate the association values which are then used in a PDA tracker [7].…”

Section: Calculation Of E-stepmentioning

confidence: 99%

“…A number of suboptimal approaches that do not make a hard decision in the measurement-to-track assignment problem are described in [7,8,9]. Given a specified number of existing targets, these recursive approaches keep a single state estimate for each track at any time.…”

Section: Introductionmentioning

confidence: 99%

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Application of the EM algorithm for the multitarget/multisensor tracking problem

Molnar

Modestino

1998

IEEE Trans. Signal Process.

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An important problem in surveillance and reconnaissance systems is the tracking of multiple moving targets in cluttered noise environments using outputs from a number of sensors possessing wide variations in individual characteristics and accuracies. A number of approaches have been proposed for this multitarget/multisensor tracking problem ranging from reasonably simple, though ad-hoc, schemes to fairly complex, but theoretically optimum, approaches. In this paper we describe a new iterative procedure for multitarget/multisensor tracking based upon use of the expectation-maximization (EM) algorithm. More specifically, we pose the multitarget/multisensor tracking problem as an incomplete data problem with the observable sensor outputs representing the incomplete data while the target-associated sensor outputs constitute the complete data. This formulation then allows a straightforward application of the EM algorithm which provides an iterative solution to the simultaneous maximum-likelihood (ML) and/or maximum a posteriori (MAP) estimate of the target states, under the assumption of appropriate motion models, based upon the outputs of disparate sensors. The advantage of this EM-based approach is that it provides a computationally efficient means for approaching the performance offered by theoretically optimum, but computationally infeasible, simultaneous ML estimator. We provide selected results illustrating the performance/complexity characteristics of this EM-based approach compared to competing schemes.This work supported in part by DAAH04-95-1-0103

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Section: Resultsmentioning

confidence: 99%

Section: Calculation Of E-stepmentioning

confidence: 99%

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Application of the EM algorithm for the multitarget/multisensor tracking problem

Molnar

Modestino

1998

IEEE Trans. Signal Process.

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“…As the EKF inherits the Gaussian posterior representation from the KF, it provides only an approximate solution to the filtering task and becomes less accurate at stronger nonlinearities and larger uncertainties [247]. Its main idea is to approximate the nonlinear functions by their first-order 19 Taylor series expansions. This requires knowledge of the Jacobian matrix of the system function…”

Section: Extended Kalman Filtermentioning

confidence: 99%

“…• Probabilistic Data Association Filters (PDAF) [15,19] are singletarget tracking filters that perform a weighted update with possibly several measurements (soft assignment) in the vicinity of a predicted measurement for each target individually. They are target-oriented 31 Note that single-target filters can also be run in multi-target scenarios but with degraded performance.…”

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confidence: 99%

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Schreier

2017

At - Automatisierungstechnik

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The dissertation approaches the questions i) how to represent the driving environment in an environment model, ii) how to obtain such a representation, and iii) how to predict the traffic scene for criticality assessment. Bayesian inference provides the common framework of all designed methods. First, Parametric Free Space (PFS) maps are introduced, which compactly represent the vehicle environment in form of relevant, drivable free space. They are obtained by a novel method for grid mapping and tracking in dynamic environments. In addition, a maneuver-based, long-term trajectory prediction and criticality assessment system is introduced and the application of all methods within the advanced driver assistance system PRORETA 3 is described.

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