Deep Neural Network-based Reference Signal Reconstruction for Passive Radar with Orthogonal Frequency Division Multiplexing Waveform
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摘要: 针对正交频分复用(OFDM)波形外辐射源雷达的参考信号获取问题，基于“解调-再调制”的重构方法结合了波形优势，能获得更为纯净的参考信号。该文在此基础上提出一种联合OFDM解调、信道估计、信道均衡和星座点逆映射的深度神经网络(DNN)重构方法，建立了基于DNN的参考信号重构方案，通过网络学习自适应深度挖掘从时域接收符号到传输码元之间的映射关系、隐式地估计信道响应，从而提高解调精度和重构性能。该文首先研究了仿真数据集的获取问题、DNN的搭建和训练问题，接着对基于DNN方法在导频数目减少、循环前缀的移除、存在符号定时偏差、存在载波频偏、对高峰值平均功率比信号进行时域加窗滤波等情况下的参考信号重构性能进行了仿真分析，仿真结果表明该方法对参考信号重构的有效性。Abstract: Considering the problem of obtaining the reference signal for passive radar with Orthogonal Frequency Division Multiplexing (OFDM) waveform, the reconstruction method based on "demodulation-remodulation" employs the waveform advantage to obtain a purer reference signal. On this basis, a Deep Neural Network (DNN) reconstruction method that combines OFDM demodulation, channel estimation, channel equalization, and constellation point inverse mapping is proposed to establish a DNN-based reference signal reconstruction scheme. This method can be used to adaptively and deeply excavate the mapping relationship between time-domain received symbols and transmission symbols through network learning, and implicitly estimate the channel response, thereby improving demodulation accuracy and reconstruction performance. Firstly, the acquisition of simulation data sets, the construction and training of DNN are studied in this paper.Then, the comparison between the DNN method and the traditional method about reference signal reconstruction performance is analyzed under the condition that the number of pilots is reduced, the cyclic prefix is removed, the symbol timing offset exists, the carrier frequency offset exists, the time domain windowing filter is performed on the high peak-to-average power ratio signal, and all the above parameters are superimposed. Finally, simulation results show the effectiveness of this method.
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