TY - GEN
T1 - Adaptive tracking algorithm of multi-target based on fuzzy clustering
AU - Hu, Xiu Hua
AU - Guo, Lei
AU - Li, Hui Hui
PY - 2014
Y1 - 2014
N2 - For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.
AB - For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.
KW - Data association
KW - Fuzzy clustering
KW - Multi-sensor multi-target
KW - State estimation
UR - http://www.scopus.com/inward/record.url?scp=84897717302&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMM.513-517.448
DO - 10.4028/www.scientific.net/AMM.513-517.448
M3 - 会议稿件
AN - SCOPUS:84897717302
SN - 9783038350125
T3 - Applied Mechanics and Materials
SP - 448
EP - 452
BT - Applied Science, Materials Science and Information Technologies in Industry
T2 - 2014 International Conference on Advances in Materials Science and Information Technologies in Industry, AMSITI 2014
Y2 - 11 January 2014 through 12 January 2014
ER -