Xia Meng, Gong Wenrong, Yang Lichao
School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Science and Technology on Communication Information Security Control Laboratory, Jiaxing 314033, China.
Sensors (Basel). 2024 Aug 23;24(17):5471. doi: 10.3390/s24175471.
The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar.
以线性调频（LFM）信号作为基带波形的正交频分复用（OFDM）模式已在多输入多输出（MIMO）雷达系统中得到广泛研究和应用。然而，其脉冲压缩后的高旁瓣电平会影响雷达系统的目标检测。针对本文，进行了理论分析，以研究OFDM-LFM波形中高旁瓣电平的成因，并提出了一种基于深度神经网络的新型波形优化设计方法。该方法利用经典的ResNeXt网络构建双通道神经网络，并采用新的损失函数来设计OFDM-LFM波形的相位和带宽。同时，利用优化因子来解决峰值旁瓣电平（PSL）和积分旁瓣电平（ISL）的优化问题。我们的数值结果验证了理论分析的正确性和所提方法的有效性。所设计的OFDM-LFM波形在脉冲压缩方面表现出优异的性能，并提高了雷达的检测性能。
