  Intelligent reflecting surface (IRS) has emerged as a promising technology to
reconfigure the radio propagation environment by dynamically controlling
wireless signal's amplitude and/or phase via a large number of reflecting
elements. In contrast to the vast literature on studying IRS's performance
gains in wireless communications, we study in this paper a new application of
IRS for sensing/localizing targets in wireless networks. Specifically, we
propose a new self-sensing IRS architecture where the IRS controller is capable
of transmitting probing signals that are not only directly reflected by the
target (referred to as the direct echo link), but also consecutively reflected
by the IRS and then the target (referred to as the IRS-reflected echo link).
Moreover, dedicated sensors are installed at the IRS for receiving both the
direct and IRS-reflected echo signals from the target, such that the IRS can
sense the direction of its nearby target by applying a customized multiple
signal classification (MUSIC) algorithm. However, since the angle estimation
mean square error (MSE) by the MUSIC algorithm is intractable, we propose to
optimize the IRS passive reflection for maximizing the average echo signals'
total power at the IRS sensors and derive the resultant Cramer-Rao bound (CRB)
of the angle estimation MSE. Last, numerical results are presented to show the
effectiveness of the proposed new IRS sensing architecture and algorithm, as
compared to other benchmark sensing systems/algorithms.
