A new adaptive track correlation method for multiple scenarios
Yaqi Cui, Yü Liu, Tiantian Tang, Hongfeng Zhu
Abstract
Abstract The traditional track correlation methods have problems such as limitation of the application scenarios, unstable performances and poor practicalities. To solve those problems, a new adaptive track correlation method for multiple scenarios was proposed in this paper using the theories and methods from machine learning. Through interpreting and translating the track correlation problem in the field of information fusion into the classification recognition problem in the field of machine learning, the new method was derived based on deep convolutional neural networks. The association performance and adaptation capabilities of the proposed method had been validated by simulation experiments. The results show that the proposed method is better than the traditional methods with respect to association performance and adaptation capabilities, and can solve the track association problems for multiple scenarios, for example sensors have systematic errors and targets are dense or in formation. Thus, it can be predicted that the proposed method would have a well‐applied foreground.