In compressed sensing, measurement matrix plays an important role in signal acquisition and reconstruction. The traditional random measurement matrices can achieve good performance on the condition that the sampling rate is high enough, whereas the reconstructions are not satisfactory at low sampling rates. Compared with these random measurement matrices, the deterministic measurement matrices possess their own constraints, which lead to worse performance. Based on the generalized rotation (GR) matrix, two kinds of structured random matrices are proposed as the generalized binary rotation (GBR) matrix and the pseudo random generalized binary rotation (PGBR) matrix. Simulation results for two dimensional signals show that the two series of new matrices perform better than the traditional measurement matrices. The amount of time required by the traditional and the new approaches is about the same. Moreover, they can obtain more accurate reconstructions at low sampling rates.