PIER C
 
Progress In Electromagnetics Research C
ISSN: 1937-8718
Home | Search | Notification | Authors | Submission | PIERS Home | EM Academy
Home > Vol. 62 > pp. 35-42

NOVEL MULTI-TARGET TRACKING ALGORITHM FOR AUTOMOTIVE RADAR

By X. Gong, Z. Xiao, and J.-Z. Xu

Full Article PDF (204 KB)

Abstract:
Tracking multiple maneuvering targets for automotive radar is a vital issue. To this end, a novel DS-UKGMPHD algorithm which combines diagraph switching (DS), unscented Kalman (UK) filter and Gaussian mixture probability hypothesis density (GMPHD) filter is proposed in this paper. The algorithm is capable of tracking a varying number of target cars detected by automotive radar with nonlinear measurement models in a cluttered environment. In addition, variable structure is used to accommodate various target motions in real world. Simulation results demonstrate the superiority of the presented algorithm to IMM-UKGMPHD filter in terms of estimation accuracy of both number and states.

Citation:
X. Gong, Z. Xiao, and J.-Z. Xu, "Novel Multi-Target Tracking Algorithm for Automotive Radar," Progress In Electromagnetics Research C, Vol. 62, 35-42, 2016.
doi:10.2528/PIERC15121802
http://www.jpier.org/pierc/pier.php?paper=15121802

References:
1. Wang, H. and J. Liu, "The analysis of target track of passive radar based on Kalman filter," Tactical Missile Technology, 2005.

2. Kim, B., K. Yi, H. J. Yoo, H. J. Chong, and B. Ko, "An IMM/EKF approach for enhanced multi-target state estimation for application to integrated risk management system," IEEE Transactions on Vehicular Technology, Vol. 64, No. 3, 1, 2014.
doi:10.1109/TVT.2014.2385477

3. Mahler, R. P. S., "Multitarget bayes filtering via first-order multitarget moments," IEEE Transactions on Aerospace & Electronic Systems, Vol. 39, No. 4, 1152-1178, 2003.
doi:10.1109/TAES.2003.1261119

4. Lundquist, C., L. Hammarstrand, and F. Gustafsson, "Road intensity based mapping using radar measurements with a probability hypothesis density filter," IEEE Transactions on Signal Processing, Vol. 59, No. 2, 1397-1408, 2011.
doi:10.1109/TSP.2010.2103065

5. Heuer, M., A. Al-Hamadi, A. Rain, and M. M. Meinecke, "Detection and tracking approach using an automotive radar to increase active pedestrian safety," 2014 IEEE Intelligent Vehicles Symposium Proceedings, 890-893, IEEE, 2014.
doi:10.1109/IVS.2014.6856589

6. Hong, S., L. Wang, Z.-G. Shi, and K. S. Chen, "Simplified particle phd filter for multiple-target tracking: Algorithm and architecture," Progress In Electromagnetics Research, Vol. 120, 481-498, 2011.
doi:10.2528/PIER11081901

7. Chen, J.-F., Z.-G. Shi, S.-H. Hong, and K. S. Chen, "Grey prediction based particle filter for maneuvering target tracking," Progress In Electromagnetics Research, Vol. 93, 237-254, 2009.
doi:10.2528/PIER09042204

8. Georgescu, R. and P. Willett, "The multiple model CPHD tracker," IEEE Transactions on Signal Processing, Vol. 60, No. 4, 1741-1751, 2012.
doi:10.1109/TSP.2012.2183128

9. Maher, R., "A survey of PHD filter and CPHD filter implementations," Proceedings of SPIE - The International Society for Optical Engineering, Vol. 6567, 65670O-65670O-12, 2007.

10. Hao, Y. L., F. B. Meng, F. Sun, and F. Shen, "Application of UK-GMPHDF algorithm based on imm in multiple maneuvering targets tracking," Systems Engineering - Theory & Practice, Vol. 31, No. 11, 2225-2233, 2011.

11. Li, X. R. and Y. Bar-Shalom, "Multiple-model estimation with variable structure," IEEE Transactions on Automatic Control Ac, Vol. 41, No. 4, 478-493, 1996.
doi:10.1109/9.489270

12. Clark, D., B. N. Vo, and J. Bell, "GM-PHD filter multitarget tracking in sonar images," Defense and Security Symposium, Vol. 6235, 62350R-62350R-8, International Society for Optics and Photonics, 2006.


© Copyright 2010 EMW Publishing. All Rights Reserved