  Wireless sensing is a promising technology for future wireless communication
networks to realize various application services. Wireless local area network
(WLAN)-based localization approaches using channel state information (CSI) have
been investigated intensively. Further improvements in detection performance
will depend on selecting appropriate feature information and determining the
placements of distributed antenna elements. This paper presents a proposal of
an enhanced device-free WLAN-based localization scheme with beam-tracing based
antenna placement optimization and spatially localized regression, where
beam-forming weights (BFWs) are used as feature information for training
machine-learning (ML)-based models localized to partitioned areas. By this
scheme, the antenna placement at the access point (AP) is determined by solving
a combinational optimization problem with beam-tracing between AP and station
(STA) without knowing the CSI. Additionally, we propose the use of localized
regression to improve localization accuracy with low complexity, where
classification and regression based ML models are used for coarse and precise
estimations of the target position. We evaluate the proposed scheme effects on
localization performance in an indoor environment. Experiment results
demonstrate that the proposed antenna placement and localized regression scheme
improve the localization accuracy while reducing the required complexity for
both off-line training and on-line localization relative to other reference
schemes.
