一种基于稀疏度估计的自适应压缩频谱感知算法

针对现有的频谱感知存在信号稀疏度估计所需压缩观测值不能满足信号稀疏度变化时实时跟踪的问题,研究一种基于稀疏系数信息估计的自适应宽带频谱压缩感知方法,在流信号进行稀疏度未知的压缩时,先采集由先验信息得到的观测值数目.在采集到的观测值数目上自适应调整,得到信号稀疏度估计所需的观测值数目,并精确估计信号的稀疏度.仿真结果表明,SCI-CSS算法对流信号频谱能够保持良好的收敛性和较快的跟踪速度,且能有效地确定使信号稀疏度估计所需压缩观测值数目并随信号稀疏度自适应调整,实现对信号稀疏度变化的实时跟踪.

An Improved Adaptive Compressed Wideband Spectrum Sensing Algorithm Based on Sparsity Estimation

In order to solve the problem that the compressed observations number of signal sparsity estimation can not satisfy the real-time tracking when the signal sparsity changes,an improved compressed spectrum sensing algorithm based on the sparse coefficient estimation (named SCI-CSS) is proposed for the wide-band spectrum sensing.This improved algorithm expanded the compressed spectrum sensing algorithm to the flow signal model.The previous frame signal sparsity estimation was used as the priori-information to adjust the number of compression observations required for the signal sparsity estimation of current frame signal.This allowed the real-time tracking of signal sparsity changes.Simulation results show that the SCI-CSS algorithm can maintain good convergence and fast tracking speed of the flow signal spectrum.The compressed observations number required for signal sparsity estimation can be effectively determined and adaptively adjusted with the sparsity to achieve real-time tracking of the signal sparsity changing.