  In this work, we investigate a reconfigurable intelligent surface (RIS) aided
integrated sensing and communication scenario, where a base station (BS)
communicates with multiple devices in a full-duplex mode, and senses the
positions of these devices simultaneously. An RIS is assumed to be mounted on
each device to enhance the reflected echoes. Meanwhile, the information of each
device is passively transferred to the BS via reflection modulation. We aim to
tackle the problem of joint localization and information retrieval at the BS. A
grid based parametric model is constructed and the joint estimation problem is
formulated as a compressive sensing problem. We propose a novel message-passing
algorithm to solve the considered problem, and a progressive approximation
method to reduce the computational complexity involved in the message passing.
Moreover, an expectation-maximization (EM) algorithm is applied for tuning the
grid parameters to mitigate the model mismatch problem. Finally, we analyze the
efficacy of the proposed algorithm through the Bayesian Cram\'er-Rao bound.
Numerical results demonstrate the feasibility of the proposed scheme and the
superior performance of the proposed EM-based message-passing algorithm.
