&lt;title&gt;Survey of maneuvering target tracking: dynamic models&lt;/title&gt;

Li, X. Rong; Jilkov, Vesselin P.

doi:10.1117/12.391979

Cited by 602 publications

(947 citation statements)

References 37 publications

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“…However from the above equation 2N unknown parameters can not be evaluated. Some researchers advocate to select the quadrature points arbitrarily and calculate the corresponding weights by using moment equations [12], while others prefer to choose the quadrature points as the zeros of the Hermite polynomial [30], and determine the weights. …”

Section: Appendix-a: Moment Matching Methods For Generating Quadraturementioning

confidence: 99%

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Quadrature filters for one-step randomly delayed measurements

Singh

Bhaumik

Date

2016

Applied Mathematical Modelling

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In this paper, two existing quadrature filters, viz., the Gauss-Hermite filter (GHF) and the sparse-grid Gauss-Hermite filter (SGHF) are extended to solve nonlinear filtering problems with one step randomly delayed measurements. The developed filters are applied to solve a maneuvering target tracking problem with one step randomly delayed measurements.Simulation results demonstrate the enhanced accuracy of the proposed delayed filters compared to the delayed cubature Kalman filter and delayed unscented Kalman filter.

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Section: Appendix-a: Moment Matching Methods For Generating Quadraturementioning

confidence: 99%

“…Such problems are popularly known as coordinated turn in avionics vocabulary [6]. The coordinated turn model, adopted for target motion is summarized in [30] and well described in [6].…”

Section: Simulationmentioning

confidence: 99%

Quadrature filters for one-step randomly delayed measurements

Singh

Bhaumik

Date

2016

Applied Mathematical Modelling

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“…The target motion is generated without a process noise. The target trajectories were generated with a model ( [31] Eqs. (57)- (60)) differing from the models used in the filters.…”

Section: Simulation Resultsmentioning

confidence: 99%

Joint target tracking and classification with particle filtering and mixture Kalman filtering using kinematic radar information

Angelova

Mihaylova

2006

Digital Signal Processing

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This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar measurements only, no class (feature) measurements are used. A speed likelihood function for each class is defined using a prior information about speed constraints. Class-dependent speed likelihoods are calculated through the state estimates of each class-dependent tracker. They are combined with the kinematic measurement likelihoods in order to improve the classification process. The two designed estimators are compared and evaluated over rather complex target scenarios. The results demonstrate the usefulness of the proposed scheme for the incorporation of additional speed information. Both filters illustrate the opportunity of the particle filtering and mixture Kalman filtering to incorporate constraints in a natural way, providing reliable tracking and correct classification. Future observations contain valuable information about the current state of the dynamic systems. In the framework of the MKF, an algorithm for delayed estimation is designed for improving the current modal state estimate. It is used as an additional, more reliable information in resolving complicated classification situations.

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“…Let x=[p, v, a] T , then the process dynamics for the position, velocity, and acceleration states defined in the world coordinate system are given by [13]:…”

Section: A State Updatementioning

confidence: 99%