Abstract: 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.