volume | PIER Journals

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.

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

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 Google Scholar

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 Google Scholar

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 Google Scholar

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 Google Scholar

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 Google Scholar

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 Google Scholar

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 Google Scholar

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. Google Scholar

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. Google Scholar

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 Google Scholar

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. Google Scholar

Dr. Xun Gong

Dr. Zelong Xiao

Dr. Jian-Zhong Xu