基于深度学习的雷达目标航迹起始方法

本文提出了一种基于深度学习的雷达目标航迹起始方法，将目标航迹起始问题转化为深度神经网络模型二分类问题—“真实航迹”类和“虚假航迹”类。首先对空间配准后的目标点迹进行环形波门粗关联，得到粗关联暂时航迹；对粗关联暂时航迹进行特征向量建模，获得深度神经网络模型输入向量；利用仿真系统雷达数据，提取神经网络模型训练样本，设计深度全连接神经网络结构，训练网络模型得到优化的模型参数；使用训练好的模型参数实时计算目标起始航迹。仿真试验证明了该算法的有效性。

Track Initiation Method of Radar Target Based on Deep Learning

This paper presents a track initiation method of radar target based on deep learning. The algorithm transforms track initiation into the problem of neural networks for classification. The two classifications are real track and false track respectively. Firstly, associate target plots after spatial registration by Annular gate roughly, and get the temporary track. Secondly, the input vector of neural network is generated by eigenvector modeling of temporary track. Then extract neural network training samples from simulation system. And design structure of deep full connection neural network. Then get optimized model parameters by training the neural network model. Finally, using the optimized model parameters calculate initial track online. The simulation result validate the efficiency of our algorithm.