Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(156.61KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
New Data Association Technique for Target Tracking in Dense Clutter Environment Using Filtered Gate Structure
El Said Mostafa Saad, El. Bardawiny, H. I. Ali, N. M. Shawky
Pages - 338 - 351     |    Revised - 31-01-2011     |    Published - 08-02-2011
Volume - 4   Issue - 6    |    Publication Date - January / February  Table of Contents
MORE INFORMATION
KEYWORDS
Target Tracking, Data Association, Probabilistic Data Association Algorithm, Kalman Filter
ABSTRACT
Improving data association process by increasing the probability of detecting valid data points (measurements obtained from radar/sonar system) in the presence of noise for target tracking are discussed in manuscript. We develop a novel algorithm by filtering gate for target tracking in dense clutter environment. This algorithm is less sensitive to false alarm (clutter) in gate size than conventional approaches as probabilistic data association filter (PDAF) which has data association algorithm that begin to fail due to the increase in the false alarm rate or low probability of target detection. This new selection filtered gate method combines a conventional threshold based algorithm with geometric metric measure based on one type of the filtering methods that depends on the idea of adaptive clutter suppression methods. An adaptive search based on the distance threshold measure is then used to detect valid filtered data point for target tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm.
CITED BY (1)  
1 Saad, E. M., Bardawiny, E. L., Ali, H. I., & Shawky, N. M. (2011). Improving data association based on finding optimum innovation applied to nearest neighbor for multi-target tracking in dense clutter environment. International Journal of Computer Science Issues.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Socol@r  
5 Scribd 
6 WorldCat 
7 SlideShare 
8 PdfSR 
1 Y. Bar-Shalom and W. D. Blair.” Multitarget Multisensor Tracking:Applications and Advances”,volume III. Archtech House, Norwood, MS, 2000.
2 Y. Bar-Shalom and T. E. Fortmann. “Tracking and Data Association”. Academic Press, 1988.
3 R. A. Singer, R. G. Sea, and K. B. Housewright.” Derivation and evaluation of improved tracking filters for use in dense multitarget environment”. IEEE Transactions on Information Theory, 20:423–432, 1974.
4 Y. Bar-Shalom and E. Tse.” Tracking in a cluttered environment with probabilistic dataassociation”.Automatica, 1975.
5 Y. Bar-Shalom. “Tracking methods in a multitarget environment”. IEEE Transactions on Automated control, 23:618–626, 1978.
6 D. B. Reid. “An algorithm for tracking multiple targets”. IEEE Transactions on Automatic Control, 24:843–854, 1979.
7 J. B. Collins and J. K. Uhlmann. “Efficient gating in data association with multivariate Gaussian distribution states”. IEEE Transactions on Aerospace and Electronic Systems,28(3):909–916, July 1992.
8 S. S. Blackman and R. Popoli. “Design and Analysis of Modern Tracking Systems”. Artech House, 1999.
9 F. J. Breidt and A. L. Carriquiry.” Highest density gates for target tracking”. IEEE Transactions on Aerospace and Electronic Systems,36(1):47–55, Jan. 2000.
10 X. Wang, S. Challa, and R. Evans.” Gating techniques for maneuvering target tracking in clutter”. IEEE Transactions on Aerospace and Electronic Systems, 38(3):1087–1097, July 2002.
11 Y. Kosuge and T. Matsuzaki. “The optimum gate shape and threshold for target tracking”. In SICE Annual Conference, 2003.
12 D. Musicki and M. R. Morelande. “Gate Volume Estimation for Target Tracking”. In International Conference on Information Fusion, 2004.
13 M. Wang, Q. Wan, and Z. You.” A gate size estimation algorithm for data association filters”.Science in China, 51(4):425–432, April 2008.
14 Ji Won Yoon and Stephen J .Roberts “Robust Measurement Validation in Target Tracking using Geometric Structure” IEEE Signal Pocessing Letters,17(5):493-496,May 2010
15 Simon Haykin. “Radar Signal Processing”, . IEEE ASSP MAGAZINE, April 1985.
16 G.Richard Curry. ‘Radar System Performance Modeling”. Artich House, 2rd ed.Edition,2005.
17 R. F. Stengel. “Optimal Control and Estimation”. Dover Publications, 1994.
18 G. W. Pulford and R. J. Evans. “Probabilistic data association for systems with multiple simultaneous measurements”. Automatica, 32(9):1311– 1316, Set. 1996.
19 X. R. Li and Y. Bar-Shalom. “Stability evaluation and track life of the PDAF for tracking in clutter”. IEEE Transactions on Automatic Control, 36(5):588–602, May 1991.
20 T. Kirubarajan and Y. Bar-Shalom.” Probabilistic Data Association Techniques for Target Tracking in Clutter”. Proceedings of the IEEE, 92(3):536–557, Mar. 2004.
21 Gad.A,Majdi. F and Farooq. M. “A Comparison of Data Association Techniques for Target Tracking in Clutter” Proceedings of information Fusion, of the Fifth international conference vol 2:1126-1133,Nov 2002.
22 V P S Naidu. “Data Association and Fusion Algorithms for Tacking in Presence of Measurement Loss”. IE(I) Journal -AS, Vol 86, pages 17–28, May 2005.
Professor El Said Mostafa Saad
- Egypt
Mr. El. Bardawiny
- Egypt
Mr. H. I. Ali
- Egypt
Mr. N. M. Shawky
TECHNICAL RESEARCH CENTER - Egypt
negmshawky@gmail.com