Title :
An Adaptive Technique for Track Association against Large Bias
Author :
Xin Li ; Chenglong He ; Wei Wu ; Zhaohua Xiong
Author_Institution :
Sci. & Technol. on Inf. Syst. Eng. Lab., Nanjing, China
Abstract :
In multi-radar data fusion systems, a large track bias (containing systematic bias and station bias) may bring great challenge to track association because it would make a rotation or a mass motion of the tracks, which always causes association mistakes. Unlike most of the published previous works, this paper for the first time proposes an adaptive technique for track association against large bias. The algorithm consists of three stages: the adaptive large track bias analysis, multi-period topology matching and the adaptive association adjustment. We also present an anti-large bias track association flow. With the help of topology, velocity and other invariable information, the M-best assumptions are made and after multi-period estimation the large track bias solution is figured out and given to guide the adaptive association adjustment, which would significantly improve the association probability. Experiment results show the effectiveness of the technique, which could differentiate and figure out the large track bias accurately, suggesting a great value in engineering.
Keywords :
estimation theory; probability; radar tracking; sensor fusion; M-best assumption; adaptive association adjustment; adaptive large track bias analysis; antilarge bias track association flow; mass motion; multiperiod estimation; multiperiod topology matching; multiradar data fusion system; probability; station bias; systematic bias; Estimation; Radar tracking; Systematics; Target tracking; Topology; adaptive; large track bias; topology information; track association;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
Print_ISBN :
978-1-4799-7004-9
DOI :
10.1109/ISCID.2014.89