  In this paper we address the recovery conditions of weighted $\ell_p$
minimization for signal reconstruction from compressed sensing measurements
when partial support information is available. We show that weighted $\ell_p$
minimization with $0<p<1$ is stable and robust under weaker sufficient
conditions compared to weighted $\ell_1$ minimization. Moreover, the sufficient
recovery conditions of weighted $\ell_p$ are weaker than those of regular
$\ell_p$ minimization if at least $50%$ of the support estimate is accurate. We
also review some algorithms which exist to solve the non-convex $\ell_p$
problem and illustrate our results with numerical experiments.
