融合失配处理和LMS滤波的雷达通信一体化OFDM信号距离旁瓣抑制技术

随着5G乃至未来6G无线通信技术的发展，无线通信设备数量呈现爆炸式增长趋势。与之矛盾的是，电磁频谱环境日趋拥堵，接近枯竭的传统通信频段已无法满足激增的业务需求。在此背景下，面向雷达与通信的频谱共享的一体化信号引起了工业界和学术界的极大关注。然而，在匹配滤波框架下，一体化信号无法兼顾雷达和通信性能。通信信息势必会在雷达模糊函数中产生高旁瓣和伪峰。为此，部分学者基于正交频分复用（Orthogonal Frequency Division Multiplexing, OFDM）共享信号，提出将高旁瓣和伪峰外推至雷达观测窗口外的失配处理方法，用以兼顾雷达模糊性能。然而，该方法会产生信噪比损失，且信噪比损失随观测窗口增大而增大。鉴于此，本文提出融合失配处理和最小均方（Least Mean Square, LMS）滤波的算法。通过LMS和失配处理的深度融合，可突破信噪比损失与观测窗口宽度之间的约束，进而能在不减小观测范围的条件下降低信噪比损失，或在相同信噪比损失下大幅提升观测范围。 

Range Sidelobe Suppression Using Mismatching and LMS adaptive filter for Radar communication integrated OFDM signal

With the development of 5G and even 6G wireless communication technology in the future, the number of wireless communication devices presents an explosive growth trend.In contrast, the electromagnetic spectrum environment is increasingly congested, and the traditional communication spectrum that is nearly exhausted can no longer meet the surging business demand. In this context, the integrated signal of spectrum sharing for radar and communication has attracted great attention from the industry and academia. However, in the framework of matched filtering, the integrated signal couldn’t take into account the performance of radar and communication.The communication information was bound to produce high sidelobe and false peak in the radar ambiguity function. For this reason, some scholars proposed a misfit processing method based on Orthogonal Frequency Division Multiplexing (OFDM) shared signal, which extrapolated the high sidelobe and pseudo peak to the outside of the radar observation window, in order to give consideration to the radar fuzzy performance. However, this method had produced SNR loss, and the SNR loss increased with the increase of observation window. In view of this, this paper proposes a fusion mismatch processing and Least Mean Square (LMS) filtering algorithm. Through the deep fusion of LMS and mismatch processing, the constraint between SNR loss and observation window width can be broken, and then the SNR loss can be reduced without reducing the observation range, or the observation range can be greatly increased with the same SNR loss.