利用基字典构造的直扩测控信号稀疏性分析

压缩感知理论为直扩测控信号降低采集成本、缓解同步解调处理压力提供了新的思路，其中稀疏性是压缩感知理论的重要应用前提，但目前该类信号稀疏性的相关研究鲜有报道。该文从信号稀疏基字典构造着手，对直扩测控信号稀疏性进行了深入研究，提出了双阶段字典学习方法，对直扩测控信号数学模型进行了深入分析，通过两种方式分别获得了学习基字典和延时-多普勒基字典，并对两种基字典的稀疏表示性能进行了仿真验证。仿真结果表明，直扩测控信号在所构造两种基字典上表现出了很强的稀疏性，这为基于压缩感知的扩频测控信号处理奠定了理论基础。 

Sparsity Analysis of DS TT&C Signals via Basic Dictionary Building

Compressed Sensing (CS) theory provides a new solution for lowering acquisition cost and synchronous demodulation processing pressure of DS TT&C signal, furthermore sparsity is an important prerequisite for CS application, but the research on sparsity of the signal is seldom reported. In this paper, the sparsity of DS TT&C signal is in depth analyzed by building the basic dictionary, and a dual-stage dictionary learning method is proposed, moreover the basic learned dictionary and delay-Doppler dictionary are built based on the dual-stage dictionary learning method and detailed analysis of the signal model. Lastly, the performances of the basic dictionaries are verified by simulation experiments. The results show that DS TT&C signals receive a strong sparsity both in the built basic dictionaries, which provides a foundation for signal processing on the basis of CS.