Due to the Doppler Blind Zone (DBZ), the target tracking of Doppler radar becomes more and more complicated. In this paper, a multi-target tracking algorithm based on Gaussian Mixture Probability Hypothesis Density (GM-PHD) for DBZ is proposed. The algorithm introduces the Minimum Detectable Velocity (MDV) information to the traditional detection probability model to update the GM-PHD and the updated equation of the GM-PHD is deduced. The simulation results show that, compared to the traditional GM-PHD with the only Doppler measurement, the proposed algorithm improves greatly the radar tracking performance of moving target under the condition of minor MDV.
Ronald P S Mahler. Statistical Multisource-Multitarget Information Fusion[M]. Norwood, MA:ARTECH HOUSE, 2007:45-49.
[2]
范红旗, 卢大威, 刘本源. 多源多目标统计信息融合[M]. 北京:国防工业出版社, 2013:12-16. Fan Hong-qi, Lu Da-wei, and Liu Ben-yuan. Statistical Multisource-Multitarget Information Fusion[M]. Beijing:National Defense Industry Press, 2013:12-16.
[3]
Ouyang C, Ji H-B, and Tian Y. Improved Gaussian mixture CPHD tracker for multitarget tracking[J]. IEEE Transactions on Signal Processing, 2013, 49(2):1177-1191.
[4]
Beard M, Vo B-T, Vo B-N, et al.. A partially uniform target birth model for Gaussian mixture PHD/CPHD filtering[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4):2835-2844.
[5]
Beard M, Vo B-T, and Vo B-N. Multitarget filtering with unknown clutter density using a bootstrap GMCPHD filter[J]. IEEE Signal Processing Letters, 2013, 20(4):323-326.
[6]
Ristic B, Clark D, and Gordon N. Calibration of multi-target tracking algorithms using non-cooperative targets[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3):390-398.
[7]
Battistelli G, Chisci L, Fantacci C, et al.. Consensus CPHD filter for distributed multitarget tracking[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3):508-520.
[8]
Uney M, Clark D, and Julier S. Distributed fusion of PHD filters via exponential mixture densities[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3):521-531.
[9]
Vo Ba-Tuong, Vo Ba-Ngu, and Cantoni Antonio. The cardinality balanced multi-target multi-bernoulli filter and its implementations[J]. IEEE Transactions on Signal Processing, 2009, 57(2):409-423.
[10]
Vo B-N, Singh S, and Doucet A. Sequential Monte Carlo methods for multi-target filtering with random finite sets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4):1224-1245.
[11]
Kohlleppel R. Ground moving target tracking of PAMIR detections with a Gaussian mixture-PHD filter[C]. 201112th International Radar Symposium, Leipzig, 2011:193-198.
[12]
Kohlleppel R. Ground target tracking with signal adaptive measurement error covariance matrix[C]. 15th IEEE International Conference on Information Fusion, Singapore, 2012:550-557.
[13]
Mallick M, Krishnamurthy V, and Vo B-N. Integrated Tracking, Classification, and Sensor Management[M]. John Wiley & Sons Inc., 2013:21-23.
[14]
Koch W. On exploiting ‘negative’ sensor evidence for target tracking and sensor data fusion[J]. Information Fusion, 2007, 8(1):28-39.
[15]
Ulmke M, Erdinc O, and Willett P. GMTI Tracking via the Gaussian mixture cardinalized probability hypothesis density filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(4):1821-1833.
[16]
Vo B N and Ma W K. The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4091-4104.
[17]
Hu Zijun. A study of multi-target tracking based on random finite set using radar[D].[Ph.D. dissertation], Xidian University, 2015:5-9.
[18]
Vo B T, Vo B N, and Cantoni A. Analytic implementations of the cardinalized probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2007, 55(7):3553-3567.
[19]
Yoon J H, Kim D Y, Bae S H, et al.. Joint initialization and tracking of multiple moving objects using Doppler information[J]. IEEE Transactions on Signal Processing, 2011, 59(7):3447-3452.
2017, Vol. 6 
