Indoor localization has drawn much attention owing to its potential for
supporting location based services. Among various indoor localization
techniques, the received signal strength (RSS) based technique is widely
researched. However, in conventional RSS based systems where the radio
environment is unconfigurable, adjacent locations may have similar RSS values,
which limits the localization precision. In this paper, we present MetaRadar,
which explores reconfigurable radio reflection with a surface/plane made of
metamaterial units for multi-user localization. By changing the reflectivity of
metamaterial, MetaRadar modifies the radio channels at different locations, and
improves localization accuracy by making RSS values at adjacent locations have
significant differences. However, in MetaRadar, it is challenging to build
radio maps for all the radio environments generated by metamaterial units and
select suitable maps from all the possible maps to realize a high accuracy
localization. To tackle this challenge, we propose a compressive construction
technique which can predict all the possible radio maps, and propose a
configuration optimization algorithm to select favorable metamaterial
reflectivities and the corresponding radio maps. The experimental results show
a significant improvement from a decimeter-level localization error in the
traditional RSS-based systems to a centimeter-level one in MetaRadar