一种改进的稀疏度自适应信号重构算法

Modified Sparsity Adaptive Signal Reconstruction Algorithm

在压缩感知的实际应用中,信号的稀疏度通常未知,需要用到稀疏度自适应重构算法。针对现有方法中对步长设定较严格和迭代次数较多等缺点,提出一种改进的基于差分的稀疏度自适应重构算法。该算法在未知信号稀疏度的情况下,首先利用原子匹配测试的方法对稀疏度进行初始估计,然后利用信号测量变化的不均匀性确定信号支撑集,进而达到重构的效果。仿真结果表明,在相同稀疏度下,该算法有较好的重构效果,且比同类算法的性能更高。

Sparse degree is always impossible to obtain in the practical application of compressive sensing, so the sparsity adaptive reconstruction algorithm is necessary. In this paper, a new adaptive matching pursuit algorithm based on difference is proposed to solve the problems of existing algo- rithms that the step size is difficult to determine and the computational cost is high due to too many iterations. Firstly, the initial sparse degree with atom matching test is estimated. Then the support set of the signal is selected. Last, according to the unbalanced signal measurement, the signal can be re- constructed. Simulation results show that the proposed algorithm can reconstruct the signal well, and the pertormance is better than others in the same sparse degree.