The Evaluation of the Performance of Different Filtering Approaches in Tracking Problem and the Effect of Noise Variance
Performance of different filtering approaches depends
on modeling of dynamical system and algorithm structure. For
modeling and smoothing the data the evaluation of posterior
distribution in different filtering approach should be chosen carefully.
In this paper different filtering approaches like filter KALMAN,
EKF, UKF, EKS and smoother RTS is simulated in some trajectory
tracking of path and accuracy and limitation of these approaches are
explained. Then probability of model with different filters is
compered and finally the effect of the noise variance to estimation is
described with simulations results.
[1] R. E. Kalman, “A New Approach to Linear Filtering and Prediction
Problems,” Transactions of the ASME–Journal of Basic Engineering, D,
vol. 82, pp. 35–45, 1960.
[2] E. A. Wan and R. van der Merwe, Kalman Filtering and Neural
Networks”, ch. 7, The Unscented Kalman Filter. Wiley, September
2001.
[3] S. J. Julier and J. K. Uhlmann, “A New Extension of the Kalman Filter to
Nonlinear Systems,” in Proceedings of the SPIE AeroSense International
Symposium on Aerospace/Defense Sensing, Simulation and Controls,
(Orlando, Florida), April 20–25, 1997.
[4] S. Särkkä and J. Sarmavuori, “Gaussian Filtering and Smoothing for
Continuous-Discrete Dynamic Systems.”, Signal Processing, Volume 93.
Issue 2, Pages 500-510.2013.
[5] S. Särkkä and J. Hartikainen, “On Gaussian Optimal Smoothing of Non-
Linear State Space Models”, IEEE Transactions on Automatic Control,
Volume 55, Issue 8, Pages 1938-1941.
[6] S. Särkkä, “Unscented Rauch-Tung-Striebel Smoother”. IEEE
Transactions on Automatic Control, Volume 53, Issue 3, Pages 845-849,
[7] S. Särkkä, A. Vehtari, and J. Lampinen, “Rao-Blackwellized Particle
Filter for Multiple Target Tracking”. Information Fusion Journal,
Volume 8, Issue 1, Pages 2-15.
[8] E. A. Wan, R. van der Merwe, “The unscented Kalman filter”, in: S.
Haykin (Ed.), Kalman Filtering and Neural Networks, Wiley
[9] S. J. Julier, J. K. Uhlmann, Unscented filtering and nonlinear estimation,
Proceedings of the IEEE 92 (3) (2004) 401–422.
[10] Farhad Asadi, Mohammad javad Mollakazemi, “Investigation of
fluctuation locations and effect of data distribution in time series
dynamical regimes”.. Accepted and oral presentation in ICBCBBE
2014: XII International Conference on Bioinformatics, Computational Biology and Biomedical Engineering, October, 27-28, 2014, Istanbul,
turkey.
[11] Farhad Asadi, Mohammad javad Mollakazemi, Aref Ghafouri, “The
influence of parameters of modeling and data distribution for optimal
condition on locally weighted projection regression method”.. Accepted
and oral presentation in ICMSE 2014: XII International Conference on
Mathematics and Statistical Engineering, October, 27-28, 2014, Istanbul,
turkey.
[12] Mohammad javad Mollakazemi, Farhad Asadi, “Real-time adaptive
obstacle avoidance in dynamic environments with different D-S”..
Accepted and oral presentation in ICARM 2014: XII International
Conference on Automation, Robotics and Mechatronics, October, 27-28,
2014, Istanbul, turkey.
[1] R. E. Kalman, “A New Approach to Linear Filtering and Prediction
Problems,” Transactions of the ASME–Journal of Basic Engineering, D,
vol. 82, pp. 35–45, 1960.
[2] E. A. Wan and R. van der Merwe, Kalman Filtering and Neural
Networks”, ch. 7, The Unscented Kalman Filter. Wiley, September
2001.
[3] S. J. Julier and J. K. Uhlmann, “A New Extension of the Kalman Filter to
Nonlinear Systems,” in Proceedings of the SPIE AeroSense International
Symposium on Aerospace/Defense Sensing, Simulation and Controls,
(Orlando, Florida), April 20–25, 1997.
[4] S. Särkkä and J. Sarmavuori, “Gaussian Filtering and Smoothing for
Continuous-Discrete Dynamic Systems.”, Signal Processing, Volume 93.
Issue 2, Pages 500-510.2013.
[5] S. Särkkä and J. Hartikainen, “On Gaussian Optimal Smoothing of Non-
Linear State Space Models”, IEEE Transactions on Automatic Control,
Volume 55, Issue 8, Pages 1938-1941.
[6] S. Särkkä, “Unscented Rauch-Tung-Striebel Smoother”. IEEE
Transactions on Automatic Control, Volume 53, Issue 3, Pages 845-849,
[7] S. Särkkä, A. Vehtari, and J. Lampinen, “Rao-Blackwellized Particle
Filter for Multiple Target Tracking”. Information Fusion Journal,
Volume 8, Issue 1, Pages 2-15.
[8] E. A. Wan, R. van der Merwe, “The unscented Kalman filter”, in: S.
Haykin (Ed.), Kalman Filtering and Neural Networks, Wiley
[9] S. J. Julier, J. K. Uhlmann, Unscented filtering and nonlinear estimation,
Proceedings of the IEEE 92 (3) (2004) 401–422.
[10] Farhad Asadi, Mohammad javad Mollakazemi, “Investigation of
fluctuation locations and effect of data distribution in time series
dynamical regimes”.. Accepted and oral presentation in ICBCBBE
2014: XII International Conference on Bioinformatics, Computational Biology and Biomedical Engineering, October, 27-28, 2014, Istanbul,
turkey.
[11] Farhad Asadi, Mohammad javad Mollakazemi, Aref Ghafouri, “The
influence of parameters of modeling and data distribution for optimal
condition on locally weighted projection regression method”.. Accepted
and oral presentation in ICMSE 2014: XII International Conference on
Mathematics and Statistical Engineering, October, 27-28, 2014, Istanbul,
turkey.
[12] Mohammad javad Mollakazemi, Farhad Asadi, “Real-time adaptive
obstacle avoidance in dynamic environments with different D-S”..
Accepted and oral presentation in ICARM 2014: XII International
Conference on Automation, Robotics and Mechatronics, October, 27-28,
2014, Istanbul, turkey.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:68994", author = "Mohammad Javad Mollakazemi and Farhad Asadi and Aref Ghafouri", title = "The Evaluation of the Performance of Different Filtering Approaches in Tracking Problem and the Effect of Noise Variance", abstract = "Performance of different filtering approaches depends
on modeling of dynamical system and algorithm structure. For
modeling and smoothing the data the evaluation of posterior
distribution in different filtering approach should be chosen carefully.
In this paper different filtering approaches like filter KALMAN,
EKF, UKF, EKS and smoother RTS is simulated in some trajectory
tracking of path and accuracy and limitation of these approaches are
explained. Then probability of model with different filters is
compered and finally the effect of the noise variance to estimation is
described with simulations results.
", keywords = "Gaussian approximation, KALMAN smoother,
Parameter estimation.", volume = "8", number = "10", pages = "1369-6", }
