  Compressed sensing (CS) enables people to acquire the compressed measurements
directly and recover sparse or compressible signals faithfully even when the
sampling rate is much lower than the Nyquist rate. However, the pure random
sensing matrices usually require huge memory for storage and high computational
cost for signal reconstruction. Many structured sensing matrices have been
proposed recently to simplify the sensing scheme and the hardware
implementation in practice. Based on the restricted isometry property and
coherence, couples of existing structured sensing matrices are reviewed in this
paper, which have special structures, high recovery performance, and many
advantages such as the simple construction, fast calculation and easy hardware
implementation. The number of measurements and the universality of different
structure matrices are compared.
