  Intelligent reflecting surface (IRS) is a rapidly emerging paradigm to enable
non-line-of-sight (NLoS) wireless transmission. In this paper, we focus on
IRS-aided radar estimation performance of a moving hidden or NLoS target.
Unlike prior works that employ a single IRS, we investigate this problem using
multiple IRS platforms and assess the estimation performance by deriving the
associated Cramer-Rao lower bound (CRLB). We then design Doppler-aware IRS
phase shifts by minimizing the scalar A-optimality measure of the joint
parameter CRLB matrix. The resulting optimization problem is non-convex, and is
thus tackled via an alternating optimization framework. Numerical results
demonstrate that the deployment of multiple IRS platforms with our proposed
optimized phase shifts leads to a higher estimation accuracy compared to
non-IRS and single-IRS alternatives.
