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。 Compressed sensing is a new theory of signal processing in recent years. Under certain conditions, the compressed sensing method can almost perfectly reconstruct the original signal with high probability through down-sampled data far below the Nyquist frequency. The measurement matrix plays a very important role in the entire process of compressive sensing. This paper starts with the recovery algorithm to propose a binary measurement matrix, and verifies its performance through simulation. After the binarization measurement matrix not only has some performance improvement, more importantly, can greatly reduce the measurement matrix required storage space and compressed sensing sampling, the recovery process of computing.