  The problem of joint direction-of-arrival estimation and distorted sensor
detection has received a lot of attention in recent decades. Most
state-of-the-art work formulated such a problem via low-rank and row-sparse
decomposition, where the low-rank and row-sparse components were treated in an
isolated manner. Such a formulation results in a performance loss. Differently,
in this paper, we entangle the low-rank and row-sparse components by exploring
their inherent connection. Furthermore, we take into account the maximal
distortion level of the sensors. An alternating optimization scheme is proposed
to solve the low-rank component and the sparse component, where a closed-form
solution is derived for the low-rank component and a quadratic programming is
developed for the sparse component. Numerical results exhibit the effectiveness
and superiority of the proposed method.
