  This paper addresses an uplink localization problem in which a base station
(BS) aims to locate a remote user with the help of reconfigurable intelligent
surfaces (RISs). We propose a strategy in which the user transmits pilots
sequentially and the BS adaptively adjusts the sensing vectors, including the
BS beamforming vector and multiple RIS reflection coefficients based on the
observations already made, to eventually produce an estimated user position.
This is a challenging active sensing problem for which finding an optimal
solution involves searching through a complicated functional space whose
dimension increases with the number of measurements. We show that the long
short-term memory (LSTM) network can be used to exploit the latent temporal
correlation between measurements to automatically construct scalable state
vectors. Subsequently, the state vector is mapped to the sensing vectors for
the next time frame via a deep neural network (DNN). A final DNN is used to map
the state vector to the estimated user position. Numerical result illustrates
the advantage of the active sensing design as compared to non-active sensing
methods. The proposed solution produces interpretable results and is
generalizable in the number of sensing stages. Remarkably, we show that a
network with one BS and multiple RISs can outperform a comparable setting with
multiple BSs.
