  The task of compressed sensing is to recover a sparse vector from a small
number of linear and non-adaptive measurements, and the problem of finding a
suitable measurement matrix is very important in this field. While most recent
works focused on random matrices with entries drawn independently from certain
probability distributions, in this paper we show that a partial random
symmetric Bernoulli matrix whose entries are not independent, can be used to
recover signal from observations successfully with high probability. The
experimental results also show that the proposed matrix is a suitable
measurement matrix.
