The week's pick
Reseach Article
State Estimation for Target Tracking Problems with Nonlinear Kalman Filter Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 98 - Number 17 |
| Year of Publication: 2014 |
| Authors: Alireza Toloei, Saeid Niazi |
10.5120/17277-7708
|
Alireza Toloei, Saeid Niazi . State Estimation for Target Tracking Problems with Nonlinear Kalman Filter Algorithms. International Journal of Computer Applications. 98, 17 ( July 2014), 30-36. DOI=10.5120/17277-7708
Abstract
One the most important problems in target tracking are state estimation. This paper deals on estimation of states from noisy sensor measurements. Due to important of exact estimation in tracking problems must evader position and Line Of Sight angles estimated with least error rather than actual position. In this paper extended Kalman filter (EKF) and unscented Kalman filter (UKF) and Cubature Kalman Filter (CKF) are presented for bearing only Tracking problem in 3D using bearing and elevation measurements from tows sensors. The algorithms and model of system simulated using MATLAB and many tests were carried out. Simulation experiments show that the efficiency of EKF due to least RMSE have better performance on compared with the UKF algorithm. Also, the performance of EKF algorithm has been dramatically decreased when initialization (initial state assumption) is not good, which in this condition CKF method provides a more accurate approximation. Numerical results from Monte Carlo simulations show that the CKF have the best state estimation accuracy among all nonlinear filters considered. The proposed approach is interesting for the design of optimization algorithms that can run on target tracking systems.
References
- Goutam Chalasani,, Shovan Bhaumik 2011 "Bearing Only Tracking Using Gauss-Hermite Filter" 978-1-4577-2119- IEEE
- S. Sadhu, S. Bhaumik, and T. K. Ghoshal, Dec 18-21, 2004 "Evolving homing guidance configuration with Cramer Rao bound," Proceedings 4th IEEE International Symposium on Signal Processing and Information Technology, Rome,
- T. L. Song, and J. L. Speyer, 1985 "A stochastic analysis of a modified gainextended Kalman filter with applications to estimation with bearings only measurements," IEEE Transactions on Automatic Control, vol 30, no. 10, pp. 940-
- S. Koteswara Rao, K. S. Linga Murthy, and K. Raja Rajeswari, 2010. "Data fusion for underwater target tracking", IET Radar Sonar Navigation, vol. 4, no. 4, pp. 576-585
- K. Dogancay, 2005 "Bearings-only target localization using total least squares," Signal Process, vol. 85, pp. 1695-1710.
- R. E. Kalman, 1960 "A new approach to linear ?ltering and prediction problems," ASME J. Basic Eng. ,
- Jouni Hartikainen, Arno Solin, and Simo Särkkä, August 2011 "Optimal filtering with Kalman filters and smoothers" Aalto-Finland,.
- R. Karlsson and F. Gustafsson, , 2001 "Range estimation using angle-only target tracking with particle filters", Proc. American Control Conference, pp. 3743 – 3748.
- B. L. Scala, M. Morelande, , 2008 "An Analysis of the Single Sensor Bearings-Only Tracking Problem" Radar- Con
- Mallick, M. , Morelande, M. R. , Mihaylova, L, Arulampalam, S. , Yan, 2012 "Comparison of Angle-only Filtering Algorithms in 3D using Cartesian and Modified Spherical Coordinates" 15th International Conference on. IEEE, p. 1392-1399.
- Aidala, V. J. Jan. 1979, Kalman filter behavior in bearings-only tracking applications. IEEE Transactions on Aerospace and Electronic Systems, AES-15, 29—39.
- Nardone, S. C. , Lindgren, A. G. , and Gong, K. F. Sept. 1984 Fundamental properties and performance of conventional bearings-only target motion analysis. IEEE Transactions on Automatic Control, AC-29, 9, 775—787.
- Wan, E. A. and van der Merwe, R. 2001 "The unscented Kalman filter. In S. Haykin (Ed. ), Kalman Filtering and Neural Networks" Hoboken, NJ: Wiley, ch. 7, pp. 221—280.
- K. Radhakrishnan, A. Unnikrishnan, K. G Balakrishnan, 2010 "Bearing only Tracking of Maneuvering Targets using a Single Coordinated Turn Model" International Journal of Computer Applications.
- Julier, S. J. , and Uhlmann, J. K, 1997 "A New Extension of the Kalman Filter to Nonlinear Systems," Proceedings of AeroSense: The 11th Int. Symposium. On Aerospace/Defense Sensing, Simulation and Controls
- Bar-Shalom, Y. , Li, X. -R. , and Kirubarajan, T, 2001. Estimation with Applications to Tracking and Navigation. Wiley Interscience.
- S. J. Julier and J. K. Uhlmann,2004 "Unscented filtering and nonlinear estimation," Proceedings of the IEEE, vol. 92, no. 3, pp. 401–422, 200
- Julier, S. J. , and Uhlmann, J. K, 1997, A New Extension of the Kalman Filter to Nonlinear Systems, Proceedings of AeroSense: The 11th Int. Symposium. On Aerospace/Defense Sensing, Simulation and Controls.
- Daum, F. , 2005, Nonlinear Filters: Beyond the Kalman Filter, IEEE A&E Systems Magazine, Vol. 20. , No. 8.
- Arasaratnam, I. and Haykin, S. June 2009, Cubature Kalman filters. IEEE Transactions on Automatic Control, 54, 6 , 1254—1269.
- Arasaratnam, I. and Haykin, S. Oct 2009. Cubature Kalman filtering: A powerful tool for aerospace applications. Presented at the International Radar Conference, Bordeaux, France. Wu, Y. , et al.
- Aug. 2006A numerical-integration perspective on Gaussian filters. IEEE Transactions on Signal Processing, 54, 8 , 2910—2921
- Pei H. Leong, S Arulampalam, T A. D. Abhayapala Gaussian-Sum : May 2014, Cubature Kalman Filter with Improved Robustness for Bearings-only Tracking, IEEE Signal Processing Letters, Vol. 21, NO. 5
Index Terms
Keywords