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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.5, No.12, Nov. 2013

Performance Comparison of Hybrid GA-PSO Based Tuned IMMs for Maneuver Target Tracking

Full Text (PDF, 1054KB), PP.120-134


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Author(s)

Ravi Kumar Jatoth, T. Kishore Kumar

Index Terms

Extended Kalman Filter, Unscented Kalman Filter, Interactive Multiple Models, Target Tracking, Tuning of filter, Hybrid GA-PSO Algorithm

Abstract

Target tracking is very important field of research as it has wider applications in defense as well as civilian applications. Kalman filter is generally used for such applications. When the process and measurements are non linear extensions of Kalman filters like Extended Kalman Filter, Unscented Kalman Filters are widely used. UKF can give estimations up to second order characteristics of random process. The target is maneuvering and switching among different models like constant velocity (CV), constant acceleration (CA) or constant turn (CT), Interactive Multiple Models (IMM) are employed. Implementation of IMM filters for any application is difficult because of initialization of Kalman filter i,e, tuning of filter has to be performed before applying to real time situations. It demands prior estimations of Noise covariance matrices which are left for engineering intuitions. This paper presents the nonlinear state estimation using IMM and tuning of the filter is done using bio-inspired algorithms like PSO GA and Hybrid GA-PSO.

Cite This Paper

Ravi Kumar Jatoth, T. Kishore Kumar,"Performance Comparison of Hybrid GA-PSO Based Tuned IMMs for Maneuver Target Tracking", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.12, pp.120-134, 2013. DOI: 10.5815/ijisa.2013.12.11

Reference

[1]S.Sadhu,M.Srinivasan and T.Ghosal, “Robustness and Tuning of Bearing only tracking” pp 42-49,IE(I) Signal processing ,Journal,vol 87,Dec-2006. 

[2]L Song and J L Speyer “A Stochastic Analysis of Modified Gain Extended kalman filter With Applications to Estimation with Bearing s Only Measurements, IEEE Transactions on Automatic Control, , pp 940-949 vol 30,no 10,1985.

[3]Julier, Jeffery K. Uhlmann, “A New Extension of the Kalman Filter to Nonlinear System,” proceedings of American control conference, Seattle,WA, pp 1628-1632. 1995. 

[4]E.A. Wan, R. Van der Merwe, “The Unscented Kalman Filter for nonlinear estimation,” in Proceddings of IEEE symposium 2000 (AS-SPCC) Lake Louise, Canada pp. 153-158 October 2000.

[5]Oleksiy V.Korniyenko, Mohammad S.Sharawi, Oklahand University ,“Neural Network based approach for tuning kalman filter”, 2005 IEEE International Conference on I.E.E.E. Electro/Information Technology Conference (EIT 2005), Lincoln – Nebraska, May 22-25, 2005. 

[6]Michail N. Petsios, Emmanouil G. Alivizatos, Nikolaos K. Uzunoglu, “Manoeuvring target tracking using multiple bistatic range and range-rate measurements”, Science Direct, Signal Processing 87(2007) 665-686.

[7]Zhansheng Duan, X. Rong Li, Chongzhao Han, Hongyan Zhu, “Sequential Unscented Kalman Filter for Radar Target Tracking with Range Rate Measurements,” 2005 7th International Conference on Information Fusion (FUSION).

[8]R.G. Brown, P.Y.C. Hwang, “Introduction to Random Signals and applied Kalman Filtering,” third ed., Prentice Hall 1997.

[9] Goldberg, D.E., 1989. Genetic Algorithms in Search and Optimization. Addison-Wesley, Reading, MA.

[10] Dash, P.K., Panigrahi B.K., Shazia Hasan.: Hybrid Particle Swarm Optimization and Unscented Filtering Technique for Estimation of Nonstationary Signal Parameters. IETE journal of research, vol.55, issue 6, Nov-Dec (2009).

[11] K. Premalatha and A.M. Natarajan “Hybrid PSO and GA for Global aximization” Pg- 598-608 Int. J. Open Problems Compt. Math., Vol. 2, No. 4,December 2009.

[12]WANG MingHui, WAN Qun ,YOU ZhiSheng “A gate size estimation algorithm for data association filters” Springer Journal on Information Sciences in China Series, Apr. 2008 vol. 51 no. 4 pg 425-432.

[13]Krzysztof Szabat and Teresa Orlowska Kowalska “Performance Improvement of Industrial Drives With Mechanical Elasticity Using Nonlinear Adaptive Kalman Filter” IEEE Transactions on industrial electronics, vol. 55, no. 3, march 2008.

[14]H.A.P. Blom and Y. Bar-Shalom, “The Interacting Multiple Model Algorithms for Systems with Markovian Switching Coefficients”, IEEE Trans. on Automatic Control, vol. 33(8), 1988.

[15]N.M. Kwok_, D.K. Liu, G. Dissanayak “Evolutionary computing based mobile robot localization” Elsevier Engineering Applications of Artificial Intelligence 19 (2006) 857–868.

[16]Bernt M. A kesson, John Bagterp Jorgensen, Niels Kjølstad Poulsen, A generalized auto covariance least-squares method for Kalman filter tuning, A journal of process control , Elsevier 2007.

[17]Oleksiy V.Korniyenko, Mohammad S.Sharawi, Oklahand University ,”Neural Network based approach for tuning kalman filter”, 2005 IEEE International Conference on Volume , Issue , 22-25 May 2005 Page(s): 1 – 5

[18]K.L Shee Y.K Wong “Speed Estimation of Induction motor drive using an optimized EKF” IEEE transaction on industrial electronics” Vol 49, No 1 February 2002 pp 124-133.

[19]. Levent Ince a, Bulent Sezen b, Erhan Saridogan a, Huseyin Ince “An evolutionary computing approach for the target motion analysis (TMA) problem for underwater tracks Expert Systems with Applications 36 (2009) 3866–3879.

[20]Kennedy, J. (1997), “The particle swarm: social adaptation of knowledge”, Proceedings of IEEE International Conference on Evolutionary Computation, Indianapolis, IN, pp. 303-8.

[21]Dah-Jing Jwo, Shun-Chieh Chang, (2009),"Particle swarm optimization for GPS navigation Kalman filter adaptation", Aircraft Engineering and Aerospace Technology, Vol. 81 Iss: 4 pp. 343 – 352. 

[22]SURENDRA RAO Scientist – E Naval Science and Technological Laboratory, Visakhapatnam “Artificial Neural Network Embedded Kalman Filter Bearing Only Passive Target Tracking” Proceedings of the 7th Mediterranean Conference on Control and Automation (MED99) Haifa, Israel - June 28-30, 1999.

[23]B.J. Lee, J.B. Park, Y.H. Joo and S.H. Jin “Intelligent Kalman filter for tracking a manoeuvring target” IEE Proc.-Radar Sonar Navig., Vol. 151, No. 6, December 2004 pp 344-350.

[24]Zhi-Jun Yu, Shao-Long Dong Jian-Ming Wei, Tao Xing1, Hai-Tao Liu1 “Neural Network Aided Unscented Kalman Filter For Maneuvering Target Tracking In Distributed Acoustic Sensor Networks” Proceedings of the International Conference on Computing: Theory and Applications (ICCTA'07) (0-7695-2770-) 2007.