基于深层神经网络的雷达波形设计

针对雷达波形多准则优化目标函数难以建立的问题,降低目标响应的不确定性,提高雷达检测性能,提出了一种基于深层神经网络的雷达波形设计方法。首先,根据雷达回波数据形式进行深层神经网络(DNNs)结构设计;然后,将基于信噪比(SNR)和互信息(MI)准则产生的信号随机混合并与其所对应的环境信息组成训练集,对DNNs训练;最后将另一部分基于互信息准则产生的信号与其对应的环境信息作为测试集,利用DNNs生成信号并进行测试。实验结果表明,使用该方法产生的信号作为雷达发射波形与仅基于MI准则产生的信号作为雷达发射波形相比,雷达回波与目标的互信息量最大提高了21.37nat,雷达接收信号的信干噪比最大提高了1.35dB。与线性调频信号相比,相应的互信息量最大提高了950.76nat,相应的信干噪比最大提高了18.23dB。

A Waveform Design of Radar Based on Deep Neural Networks

Aimed at the problems that the radar waveform multi criteria optimization target function is difficult to establish, and in order to reduce the uncertainty of target response and to improve the radar detection performance, a radar waveform design method based on deep neural network is proposed. First, deep neural networks (DNNs) are designed according to the radar echo data. Then, the signals generated based on the Signal to Noise Ratio (SNR) and the Mutual Information (MI) criteria are randomly mixed, and the corresponding environmental information is used to form a training set, and the DNNs are trained. Finally, taking another part of the signals generated based on the mutual information criterion and its corresponding environmental information as test sets, this paper utilizes the DNNs for generating signals and testing. The experimental results show that if the signals generated by the method taken as a radar emission waveform compares to the signals generated based on the MI criterion alone taken as a radar transmission waveform, the mutual information of the radar echo and the target is increased by 21.37 nat, and the signals of the radar receiving signals are improved. The noise ratio is increased by a maximum of 1.35 dB. Compared with the chirp signals, the corresponding mutual information is increased by 950.76 nat, and the corresponding signal to noise ratio is increased by 18.23 dB.