ID 原文 译文
54557 引发了电磁频谱竞争激烈、电磁干扰严重和电磁资源利用率低下等诸多问题。 To this end, an important issue arises with regard to the competition of the spectrum resources.
54558 鉴于此，国内外专家学者提出了雷达通信一体化共享信号的解决思路。 Some papers have proposed the concept of the joint waveform to share the spectrum.
54559 其中，OFDM信号被视为最有潜力的一体化共享信号。 Following this concept, the OFDM waveforms have arouse as the potential solutions.
54560 然而，受通信信息的高随机性和OFDM帧结构的周期性影响，该信号的雷达点扩展函数旁瓣较高，且存在大量伪峰。 However, due to impact of the high randomness and the periodic characteristics of OFDM-based wireless communication signals, the point spread function for the radar sensing contains high side lobes and numerous fake peaks.
54561 为此，本文围绕OFDM雷达通信一体化共享信号，从模糊函数的角度剖析了该信号的本质缺陷， In this paper, the intrinsic drawbacks of the OFDM-based joint radar and communication waveforms are analyzed from the perspective of ambiguity function.
54562 并提出了一种利用失配滤波将旁瓣和伪峰外推至雷达观测窗口外的处理算法。 Following which, a novel side-lobe suppression algorithm using the mismatching is proposed by moving the ambiguous energy outside the radar observation window.
54563 通过理论分析和仿真可知，该算法可使OFDM一体化共享信号兼备高速通信和高性能雷达探测性能。 With the algorithm, it becomes possible to realize the wireless communication and the radar sensing in a shared time-frequency plane. Theoretical analysis is validated by simulations.
54564 针对复杂电磁环境中信号检测受限于低信噪比的问题，基于信号与噪声一体化的思路，提出了一种以电磁空间的所有电磁辐射信号为背景，并结合深度学习算法的电磁信号检测方法。 For the problem that signal detection is limited by low SNR(Signal-to-Noise Ratio) in complex electromagnetic environment, based on the integration of signal and noise, with the background of all electromagnetic radiation signals in electromagnetic space and deep learning algorithm, a signal detection method is proposed.
54565 首先建立动态场景的电磁环境模型，包括了通信基站信号、雷达信号、干扰信号等， First, the electromagnetic environment model of the dynamic scene is established, including communication base station signals, radar signals, interference signals, etc.
54566 其次使用加高斯窗傅里叶变换提取电磁信号时频域的能量分布特征， Second, the energy distribution characteristics of the electromagnetic signal in the time-frequency domain are extracted with the Gaussian window Fourier transform.