An Adaptive Technique for Track Association against Large Bias

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.