基于5G信号的同频多基地OFDM-MIMO外辐射源雷达目标参数估计方法

5G信号带宽大，分布广，采用OFDM-MIMO调制，在外辐射源雷达目标探测方面具有极大的应用潜力。然而基于5G信号的外辐射源雷达存在严重的同频干扰，且信号模糊函数结构复杂，传统基于“杂波相消+匹配滤波”的信号处理方法难以有效抑制干扰，目标参数估计性能差。本项目提出联合利用多个同频5G基站进行目标探测，将同频基站信号用作目标探测信号而非干扰信号，以规避同频干扰问题；利用基于发射波形的参数估计方法提高目标参数估计性能。联合利用多个同频基站探测目标时，面临多个“发射-接收对”信号在接收端无法区分的问题，现有的基于发射波形的参数估计方法并不适用;且涉及高维参数联合估计，传统高维参数估计方法复杂度大稳健性差。本项目拟结合5G信号OFDM-MIMO波形研究该雷达体制的目标参数估计理论，并借助稀疏统计学习研究高效稳健的参数估计方法。通过本项目研究，希望突破5G外辐射源雷达信号处理瓶颈，提高目标探测性能。

The 5th-generation (5G) mobile communication signal has great potential in target surveillance when used as the illuminator of opportunity (IoO) in passive radar，owing to its broad bandwidth, wide distribution and advanced modulation (i.e. OFDM-MIMO). However, the 5G signal based passive radar suffers from severe co-channel interference, which arises from the co-channel base stations (CC-BSs) that work in the same frequency channel with the base station used as the IoO (BS-IoO). What is worse, the 5G signal format is complicated which results in complicated ambiguity function. The traditional passive radar signal processing procedure, i.e. “clutter cancellation + matched filtering”, cannot suppress the interference adequately, and the target parameter estimation performance is poor. In this project, we propose to use multiple 5G CC-BSs for target surveillance, where the signals from the CC-BSs are used as the IoOs, not treated as the interference. In this manner, the co-channel interference is avoided. Furthermore, we use the waveform-aided parameter estimation method (WAPEM) to increase the target parameter estimation performance. When using multiple 5G CC-BSs for target surveillance, the target reflected signals from multiple “transmit-receive pairs” are mixed together and cannot be resolved in the radar receiver. Traditional WAPEM cannot cope with this situation. Moreover, target surveillance in this circumstance involves the high-dimensional parameter estimation; the traditional estimation methods suffer from high computational complexity and poor robustness. In this project, we study the target parameter estimation theorem in the 5G based passive radar by exploiting the OFDM-MIMO waveform of the 5G signals from multiple CC-BSs, and study the efficient and robust parameter estimation method based on the sparse statistical learning technique. By conducting the project, we hope to overcome the bottle-neck in the 5G based passive radar signal processing and boost the target surveillance performance.