动态稀疏频谱的自适应压缩感知与跟踪算法研究

在宽带频谱感知、通信侦察等应用中信号稀疏度往往是动态变化的。首先证明了重构误差随压缩比的增加单调减小，在此基础上，提出了一种压缩比随频谱稀疏度自适应调整的压缩采样新算法。新算法由压缩采样与压缩比自适应调整两部分组成，其中，压缩采样部分用于恢复原信号，并估计恢复信号与原信号之间的误差；压缩比自适应部分根据误差与压缩比之间的近似线性函数关系，自适应调整下一时刻的压缩比。计算机仿真结果表明:新算法能够以近似“最优”的压缩比对稀疏度慢变的频谱进行有效感知，并跟踪频谱稀疏度的变化；与传统压缩采样方法相比，在保证频谱感知精度的前提下，新算法能够总体上进一步显著降低采样速率。 

Adaptive Compressive Sensing And Tracking Of Dynamic Sparse Spectrum

The sparisty of the received signal is time-varying in many wireless communication applications, such as wideband wireless spectrum sensing, communication reconnaissance and so on. It is proved that reconstruction error is monotonically reduced according to the increasing of compressive ratio. A novel spectrum sensing algorithm based on compressive sampling is proposed, which can adjust the compressive rate according to the sparsity of the spectrum adaptively. This algorithm is composed of two parts: compressive sampling and compressive rate adaptivity. Firstly, the sparse spectrum is reconstructed and the error between the reconstructed spectrum and real spectrum is estimated. Secondly, the compressive rate of the next time slot is adjusted in the help of the approximate linear relationship between the error and the compressive rate. The simulation results illustrate that the new algorithm can sense and track the slow time-varying sparse wideband validly, and it can reduce the compressive sampling rate remarkably.