用于压缩感知的二值化测量矩阵

压缩感知是近年新兴的一种信号处理理论,在一定条件满足的情况下,压缩感知方法可通过远低于 Nyquist 频率的降采样数据以高概率近乎完美地重建原始信号。测量矩阵在压缩感知的整个处理过程中起着非常重 要的作用。本文从恢复算法入手提出二值化测量矩阵,并通过仿真对其性能加以验证。二值化后测量矩阵不仅在 性能上有一定提升,更重要的是可大大降低测量矩阵所需的存储空间以及压缩感知采样、恢复过程的运算量。

Binarized Measurement Matrix for Compressive Sensing

Compressive sensing (CS) is a newly developed theory in signal processing. If certain conditions are met, the original signal can be recovered nearly perfectly with a very high probability from the sampling data, of which the sam pling rate is much lower than the Nyquist sampling rate. Measurement matrix plays a very important role in the entire proce dure of CS. In this paper, the binarized measurement matrix is proposed from the perspective of recovery algorithm, and sim ulations are carried out to verify the performance. After binarization, the recovery performance of measurement matrices can be improved to a certain extent. And most importantly the storage of the measurement matrices and the computation cost of the sampling and recovery of CS can be greatly reduced.