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Continuous-Discrete Filtering using EKF, UKF, and PF

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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Standard

Continuous-Discrete Filtering using EKF, UKF, and PF. / Mallick, Mahendra; Morelande, Mark; Mihaylova, Lyudmila.

Information Fusion (FUSION), 2012 15th International Conference on. IEEE, 2012. p. 1087-1094.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Mallick, M, Morelande, M & Mihaylova, L 2012, Continuous-Discrete Filtering using EKF, UKF, and PF. in Information Fusion (FUSION), 2012 15th International Conference on. IEEE, pp. 1087-1094, The 15th International Conference on Information Fusion, Singapore, 9/07/12.

APA

Mallick, M., Morelande, M., & Mihaylova, L. (2012). Continuous-Discrete Filtering using EKF, UKF, and PF. In Information Fusion (FUSION), 2012 15th International Conference on (pp. 1087-1094). IEEE.

Vancouver

Mallick M, Morelande M, Mihaylova L. Continuous-Discrete Filtering using EKF, UKF, and PF. In Information Fusion (FUSION), 2012 15th International Conference on. IEEE. 2012. p. 1087-1094

Author

Mallick, Mahendra ; Morelande, Mark ; Mihaylova, Lyudmila. / Continuous-Discrete Filtering using EKF, UKF, and PF. Information Fusion (FUSION), 2012 15th International Conference on. IEEE, 2012. pp. 1087-1094

Bibtex

@inproceedings{47915b4aba2d4326875879c42380941f,
title = "Continuous-Discrete Filtering using EKF, UKF, and PF",
abstract = "Continuous-discrete filtering (CDF) arises in many real-world problems such as ballistic projectile tracking, ballistic missile tracking, bearing-only tracking in 2D, angle-only tracking in 3D, and satellite orbit determination. We develop CDF algorithms using the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) with applications to the angle-only tracking in 3D. The modified spherical coordinates are used to represent the target state. Monte Carlo simulations are performed to compare the performance and computational complexity of the proposed filtering algorithms. Our results show that the CDF algorithms based on the EKF and UKF have the best state estimation accuracy and nearly the same computational cost.",
keywords = "continuous-discrete filtering, unscented Kalman filter, particle filter, Angle-only filtering in 3D , Continuous-discrete Extended Kalman filter , Continuous-discrete Particle filter , Continuous-discrete Unscented Kalman filter , Continuous-discrete filtering (CDF) , Modified spherical coordinates (MSC)",
author = "Mahendra Mallick and Mark Morelande and Lyudmila Mihaylova",
year = "2012",
month = "7",
day = "1",
language = "English",
isbn = "978-1-4673-0417-7",
pages = "1087--1094",
booktitle = "Information Fusion (FUSION), 2012 15th International Conference on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Continuous-Discrete Filtering using EKF, UKF, and PF

AU - Mallick, Mahendra

AU - Morelande, Mark

AU - Mihaylova, Lyudmila

PY - 2012/7/1

Y1 - 2012/7/1

N2 - Continuous-discrete filtering (CDF) arises in many real-world problems such as ballistic projectile tracking, ballistic missile tracking, bearing-only tracking in 2D, angle-only tracking in 3D, and satellite orbit determination. We develop CDF algorithms using the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) with applications to the angle-only tracking in 3D. The modified spherical coordinates are used to represent the target state. Monte Carlo simulations are performed to compare the performance and computational complexity of the proposed filtering algorithms. Our results show that the CDF algorithms based on the EKF and UKF have the best state estimation accuracy and nearly the same computational cost.

AB - Continuous-discrete filtering (CDF) arises in many real-world problems such as ballistic projectile tracking, ballistic missile tracking, bearing-only tracking in 2D, angle-only tracking in 3D, and satellite orbit determination. We develop CDF algorithms using the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) with applications to the angle-only tracking in 3D. The modified spherical coordinates are used to represent the target state. Monte Carlo simulations are performed to compare the performance and computational complexity of the proposed filtering algorithms. Our results show that the CDF algorithms based on the EKF and UKF have the best state estimation accuracy and nearly the same computational cost.

KW - continuous-discrete filtering

KW - unscented Kalman filter

KW - particle filter

KW - Angle-only filtering in 3D

KW - Continuous-discrete Extended Kalman filter

KW - Continuous-discrete Particle filter

KW - Continuous-discrete Unscented Kalman filter

KW - Continuous-discrete filtering (CDF)

KW - Modified spherical coordinates (MSC)

M3 - Conference contribution/Paper

SN - 978-1-4673-0417-7

SP - 1087

EP - 1094

BT - Information Fusion (FUSION), 2012 15th International Conference on

PB - IEEE

ER -