  This paper studies the multi-intelligent reflecting surface (IRS)-assisted
cooperative sensing, in which multiple active IRSs are deployed in a
distributed manner to facilitate multi-view target sensing at the
non-line-of-sight (NLoS) area of the base station (BS). Different from prior
works employing passive IRSs, we leverage active IRSs with the capability of
amplifying the reflected signals to overcome the severe multi-hop-reflection
path loss in NLoS sensing. In particular, we consider two sensing setups
without and with dedicated sensors equipped at active IRSs. In the first case
without dedicated sensors at IRSs, we investigate the cooperative sensing at
the BS, where the target's direction-of-arrival (DoA) with respect to each IRS
is estimated based on the echo signals received at the BS. In the other case
with dedicated sensors at IRSs, we consider that each IRS is able to receive
echo signals and estimate the target's DoA with respect to itself. For both
sensing setups, we first derive the closed-form Cram\'{e}r-Rao bound (CRB) for
estimating target DoA. Then, the (maximum) CRB is minimized by jointly
optimizing the transmit beamforming at the BS and the reflective beamforming at
the multiple IRSs, subject to the constraints on the maximum transmit power at
the BS, as well as the maximum amplification power and the maximum power
amplification gain constraints at individual active IRSs. To tackle the
resulting highly non-convex (max-)CRB minimization problems, we propose two
efficient algorithms to obtain high-quality solutions for the two cases with
sensing at the BS and at the IRSs, respectively, based on alternating
optimization, successive convex approximation, and semi-definite relaxation.
