Maneuvering target tracking using extended Kalman filter

Cortina, Elsa; Otero, Daniel; D’Attellis, C.E.

doi:10.1109/7.68158

Cited by 26 publications

(12 citation statements)

References 15 publications

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“…The most widely used tracking "lter is the extended Kalman "lter (EKF) [3] which employs the "rst-order Taylor series approximation to adapt the linear Kalman "lter to the nonlinear system described by Eqs. (1) and (2). Since the state is in Cartesian coordinates and the measurements are in polar coordinates.…”

Section: Sub-optimal Nonlinear Xlters For Trackingmentioning

confidence: 99%

“…One such technique is the iterated extended Kalman "lter (IKF) [3] which improves the accuracy of the EKF by repeatedly updating the estimates x( LL and the Kalman gain K L based on "rst-order Taylor series expansion about the most recent estimate. There is also a &quasi-extended' Kalman "lter [2] which shows improvements when tracking maneuvering targets at close ranges as well as the modi"ed gain extended Kalman "lter [9] which it has been shown to guarantee stability…”

Section: Sub-optimal Nonlinear Xlters For Trackingmentioning

confidence: 99%

See 1 more Smart Citation

An adaptive Gaussian sum algorithm for radar tracking

1999

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In this paper, we propose a new radar tracking algorithm based on the Gaussian sum "lter. To alleviate the computational burden associated with the Gaussian sum "lter, we have developed a new systematic and e$cient way to approximate a non-Gaussian and measurement-dependent function by a weighted sum of Gaussian density functions and we have also suggested a way to alleviate the growing memory problem inherited in the Gaussian sum "lter. Our method is compared with the extended Kalman "lter (EKF) and the converted measurement Kalman "lter (CMKF) and it is shown to be more accurate in term of position and velocity errors.1999 Elsevier Science B.V. All rights reserved. ZusammenfassungIn diesem Artikel schlagen wir einen neuen Radar-Nachfu K hralgorithmus vor, der auf dem Gau{schen Summen"lter beruht. Zur Verringerung des mit dem Gau{schen Summen"lter verbundenen Rechenaufwands entwickelten wir eine neue systematische und e$ziente Methode zur Approximation einer nicht-Gau{schen und me{wert-abhaK ngigen Funktion durch eine gewichtete Summe Gau{scher Dichtefunktionen. Wir schlugen weiters eine Vorgangsweise zur Verringerung des beim Gau{schen Summen"lter auftretenden Problems eines anwachsenden Speicherbedarfs vor. Wir vergleichen unsere Methode mit dem erweiterten Kalman"lter (EKF) und dem`converted measurementa-Kalman"lter (CMKF) und zeigen, da{ sie hinsichtlich Positions-und Geschwindigkeitsfehlern genauer arbeitet.1999 Elsevier Science B.V. All rights reserved. Re2 sume2Dans cet article, nous proposons un nouvel algorithme de suivi de radars reposant sur un "ltre a`sommes gaussiennes. Pour alleH ger la charge de calcul associeH e aux "ltres a`sommes gaussiennes, nous avons deH veloppeH un nouveau moyen systeH matique et e$cace pour approximer une fonction non gaussienne et deH pendante des mesures par une somme pondeH reH e de fonctions de densiteH s gaussiennes et nous avons eH galement suggeH reH une manie`re d'alleH ger le proble`me de l'occupation de meH moire croissante inheH rente au "ltrage a`sommes gaussiennes. Nous comparons notre meH thode avec le "ltrage de Kalman eH tendu et le "ltrage de Kalman a`mesures converties, et nous montrons que nous sommes plus preH cis en terme d'erreur de position et de vitesse.

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Section: Sub-optimal Nonlinear Xlters For Trackingmentioning

confidence: 99%

Section: Sub-optimal Nonlinear Xlters For Trackingmentioning

confidence: 99%

An adaptive Gaussian sum algorithm for radar tracking

1999

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“…Finite-dimensional filters for linear Gaussian state-space models derived in [5] can be used with the expectation maximization algorithm to yield maximum likelihood estimates of the model parameters with a possibility of parallel implementation on a multiprocessor system. Many authors (e.g., [6], [7]) applying EKFs to tracking problems (and one of the first Moura et al [6]) have come to the conclusion that some problems of numerical ill-conditioning may arise in this approach if the ratio between the maximum and minimum eigenvalues of the covariance matrix is not enough small. To overcome this problem the use of an EKF with the square root algorithm combined with the a posteriori probability maximum techniques was proposed in [7].…”

Section: Introductionmentioning

confidence: 99%

“…Many authors (e.g., [6], [7]) applying EKFs to tracking problems (and one of the first Moura et al [6]) have come to the conclusion that some problems of numerical ill-conditioning may arise in this approach if the ratio between the maximum and minimum eigenvalues of the covariance matrix is not enough small. To overcome this problem the use of an EKF with the square root algorithm combined with the a posteriori probability maximum techniques was proposed in [7].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear spatial-time-varying filtering and identification algorithms for correlation-extremum dynamic systems with random structure and their linearization

Kolosovskaya¹

2018

Vib. proced.

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The correlation-extremum systems theory is extended to stochastic systems with random structure or with switching parameters and the new suboptimal (due to the nonlinear system state and measurement equations) filtering and parameter identification algorithms and their linearized form are derived, which provide adaptive features and reliable operation for the proposed combined correlation-extremum dynamic systems with random structure under environment influences, and represent new solutions of the linearization problem for the case of great estimation errors. The obtained linearized solution allows the simplification of the filter a priori performance investigation at the signal processing system design stage.

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“…[8][9][10][11][12][13]) have dealt with nonlinear tracking problems, but they have generally made use of either kinematic models of second order (acceleration models) or measurements of orientations of the target vehicle. The main contribution of the current work is to consider a higher order kinematic model (jerk model) for target tracking using only position measurements as obtained from normal radars.…”

Section: Introductionmentioning

confidence: 99%

Mixed coordinate tracking of generalized maneuvering targets using acceleration and jerk models

Mahapatra

Mehrotra²

2000

IEEE Trans. Aerosp. Electron. Syst.

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In a recent paper the authors introduced an all-Cartesianformulation of a jerk model for tracking highly maneuvering targets. Here a more complex but realistic case is considered, where target motion modeling and tracking are carried out in the 3-D Cartesian frame using measurements obtained in a spherical system. The transformation of the measurements into the Cartesian system results in nonlinear measurement equations.We solve the problem using an extended Kalman filter (EKF) approach, and also treat the earlier acceleration model similarly for comparison of results. CORRESPONDENCE993

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Maneuvering target tracking using extended Kalman filter

Cited by 26 publications

References 15 publications

An adaptive Gaussian sum algorithm for radar tracking

An adaptive Gaussian sum algorithm for radar tracking

Nonlinear spatial-time-varying filtering and identification algorithms for correlation-extremum dynamic systems with random structure and their linearization

Mixed coordinate tracking of generalized maneuvering targets using acceleration and jerk models

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