  This paper considers networked sensing in cellular network, where multiple
base stations (BSs) first compress their received echo signals from multiple
targets and then forward the quantized signals to the cloud via
limited-capacity backhaul links, such that the cloud can leverage all useful
echo signals to perform high-resolution localization. Under this setup, we
manage to characterize the posterior Cramer-Rao Bound (PCRB) for localizing all
the targets as a function of the transmit covariance matrix and the compression
noise covariance matrix of each BS. Then, a PCRB minimization problem subject
to the transmit power constraints and the backhaul capacity constraints is
formulated to jointly design the BSs' transmission and compression strategies.
We propose an efficient algorithm to solve this problem based on the
alternating optimization technique. Specifically, it is shown that when either
the transmit covariance matrices or the compression noise covariance matrices
are fixed, the successive convex approximation technique can be leveraged to
optimize the other type of covariance matrices locally. Numerical results are
provided to verify the effectiveness of our proposed algorithm.
