  In this paper we show that if the restricted isometry constant $\delta_k$ of
the compressed sensing matrix satisfies \[ \delta_k < 0.307, \] then $k$-sparse
signals are guaranteed to be recovered exactly via $\ell_1$ minimization when
no noise is present and $k$-sparse signals can be estimated stably in the noisy
case. It is also shown that the bound cannot be substantively improved. An
explicitly example is constructed in which $\delta_{k}=\frac{k-1}{2k-1} < 0.5$,
but it is impossible to recover certain $k$-sparse signals.
