作为我国未来新一代空管自动化系统的核心技术之一的广播式自动相关监视（ADS-B）易受到欺骗性和压制性等多种干扰。本项目旨在以压缩感知理论为基础，通过建立可变结构下的稀疏阵列采集模型，实现对ADS-B接收信号多种干扰的检测和对抗。首先，研究ADS-B信号的稀疏性原理，探索其特有的观测矩阵和感知信号恢复算法，建立稀疏阵列结构下的ADS-B信号采集模型；其次，通过对ADS-B信号进行总体经验模态分解，以及对得到的特征分量进行相关性分析，研究基于似然比模型的干扰检测和分类方法；再次，通过稀疏阵列特有的调零天线权重设置和抗干扰容量评估模型，建立用于调整稀疏阵列结构的反馈模型，研究可变结构下的ADS-B多种干扰同时抑制方法。本研究可为我国新一代空管自动化系统提供安全可靠的ADS-B信号源，能够提升对我国空域监视的安全水平，并为ADS-B在无人机空管和低空空域中的应用提供理论支持。

As one of core technics at the new generation of ATC automation system, the operation of automatic dependent surveillance-broadcast (ADS-B) daunts jamming and spoofing interferences. Based on compressive sensing theory, this project aims to establish a variable-structured sparse array sampling model, which achieves multiple interferences detection and suppression for ADS-B signal. First, the sparse principle of ADS-B signals, its unique observation matrix and the perception signal recovery algorithm are explored to establish ADS-B sparse array sampling model. Secondly, according to implementing ensemble empirical mode decomposition to ADS-B signal and correlation analysis of the obtained feature components, an interference detection and classification algorithm based on likelihood ratio is studied. Furthermore, by studying the weight of sparse array nulling antenna and assessment criteria of anti-interference capacity, a variable-structured sparse array is achieved to suppress multiple interferences using feedback model of sparse array. This project can provide the secure and reliable ADS-B signals for the new generation of ATC automation system and effectively improve the security level of air traffic surveillance. It also provides a theoretic foundation for ADS-B application ATC for unmanned aerial vehicle and at low-altitude airspace.