Abstract: In order to improve the intelligence of track association in radar data processing,make full use of the characteristic information of the target and simplify the processing flow,an end-to-end track association algorithm based on deep learning network model was proposed.Firstly,this paper analyzed the problem that the track correlation based on neural network has few sample details and complex processing flow.Then,it proposed an end-to-end deep learning model,which takes all the track information features as input.According to the processing characteristics of track correlation data,the convolutional neural networks structure is improved for feature extraction,and the processing ability of long short-term memory neural network for historical information and future information is fully utilized to analyze the correlation of track before and after.After the original data is processed with Kalman filtering,the final track correlation results are directly output through the long short-term memory deep neural network model based on the convolutional neural networks features extracting.In this paper,the precision,recall and accuracy were set to verify the performance of the track association model.The simulation results show that the proposed model can fully learn multiple feature information of the target and has a high track association accuracy,which has reference value for the intelligent analysis of track association.

Key words: Deep learning, Convolutional neural networks, Long short-term memory, Track association