  This paper investigates the capabilities and effectiveness of backward
sensing centered on reconfigurable intelligent surfaces (RISs). We demonstrate
that the direction of arrival (DoA) estimation of incident waves in the
far-field regime can be accomplished using a single RIS by leveraging
configurational diversity. Furthermore, we identify that the spatial diversity
achieved through deploying multiple RISs enables accurate localization of
multiple power sources. Physically accurate and mathematically concise models
are introduced to characterize forward signal aggregations via RISs. By
employing linearized approximations inherent in the far-field region, the
measurement process for various configurations can be expressed as a system of
linear equations. The mathematical essence of backward sensing lies in solving
this system. A theoretical framework for determining key performance indicators
is established through condition number analysis of the sensing operators. In
the context of localization using multiple RISs, we examine relationships among
the rank of sensing operators, the size of the region of interest (RoI), and
the number of elements and measurements. For DoA estimations, we provide an
upper bound for the relative error of the least squares reconstruction
algorithm. These quantitative analyses offer essential insights for system
design and optimization. Numerical experiments validate our findings. To
demonstrate the practicality of our proposed RIS-centric sensing approach, we
develop a proof-of-concept prototype using universal software radio peripherals
(USRP) and employ a magnitude-only reconstruction algorithm tailored for this
system. To our knowledge, this represents the first trial of its kind.
