CFP last date
20 December 2024
Reseach Article

Kalman Filter Tracking

by Nasser H. Ali, Ghassan M. Hassan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 9
Year of Publication: 2014
Authors: Nasser H. Ali, Ghassan M. Hassan
10.5120/15530-4315

Nasser H. Ali, Ghassan M. Hassan . Kalman Filter Tracking. International Journal of Computer Applications. 89, 9 ( March 2014), 15-18. DOI=10.5120/15530-4315

@article{ 10.5120/15530-4315,
author = { Nasser H. Ali, Ghassan M. Hassan },
title = { Kalman Filter Tracking },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 9 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number9/15530-4315/ },
doi = { 10.5120/15530-4315 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:47.851600+05:30
%A Nasser H. Ali
%A Ghassan M. Hassan
%T Kalman Filter Tracking
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 9
%P 15-18
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Kalman filter estimates the state of a dynamic system, even if the precise form of the system is unknown. The filter is very powerful in the sense that it supports estimations of past and even future states. The description of the standard Kalman filter and its algorithms with the two main steps, the prediction step and the correction step. Furthermore the extended Kalman filter is discussed, which represents the conversion of the Kalman filter to nonlinear systems. Finally these filter was tested on aircraft tracking, and sinus wave using MATLAB.

References
  1. B. Hoffmann-Wellenhof, H. Lichtenegger and J. Collins, 1997. GPS Theory and Practice, Springer-Verlag Vienna.
  2. Mohinder S. Grewal, Angus P. Andrews, 2001. Kalman Filtering Theory and Practice Using Matlab,John Wiley & Sons Inc 2001.
  3. Sorenson, Harold W. ,1995. Kalman filtering theory and application ,New York
  4. Rachel Kleinbauer, Universität Stuttgart, Helsinki, Nov. 2004, Kalman Filtering Implementation with Matlab.
  5. D. Bouvet and G. Garcia. Improving the Accuracy of Dynamic Localization Systems using RTK GPS by Identifying the GPS Latency, IEEE.
  6. Qi. Honghui and J. B. Moore. Direct Kalman Filtering Approach for GPS/INS Integration. IEEE Transactions on Aerospace and Electronic Systems, 38(2):687-693, 2002.
  7. Shaik Sami, P. Padmaja, International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064, Vol. 2 Issue 5, May 2013, Speech Enhancement Using Fast Adaptive Kalman Filtering Algorithm Along With Weighting Filter
  8. R. Sharaf, A. Noureldin, A. Osman, and N. El-Sheimy. Online INS/GPS Integration with A Radial Basis Function Neural Network. IEEE Aerospace and Electronic Systems Magazine, 20(3):8-14, 2005.
  9. M. Grewal, L. Weill, and A. Andrews, Global Positioning Systems, Inertial Navigation and Integration, 2ed, Hoboken, NJ, Wiley, 2007.
  10. L. Zhao, W. Ochieng, M. Quddus, and R. Noland, An extended Kalman filter algorithm for integrating GPS and low cost dead reckoning system data for vehicle performance and emission monitoring, J. Navig, vol. 56, no. 2, pp. 257–275, May 2003.
  11. Pamadi, B. N. , Performance, Stability, Dynamics and Control of Airplanes, AIAA Education Series, Virginia, 1998.
  12. S. Rezaei and R. Sengupta, Kalman filter-based integration of DGPS and vehicle sensors for localization, IEEE Trans. Control Syst. Technol. , vol. 15, no. 6, pp. 1080–1088, Nov. 2007.
Index Terms

Computer Science
Information Sciences

Keywords

KF EKF Prediction dynamic model state vector