  With the growth of Internet-of-Things (IoT), Location based Services (LBS)
are gaining significant attention over the past years. Location information is
one of the important ingredients for many LBS where the system requires to
localize multiple targets in indoor setting. As an emerging technique,
device-free localization (DFL) is promising to localize the target without
attaching any transceivers. In this paper, we propose CoMuTe, the first
convolutional neural network (CNN) based device free multiple target
localization leveraging the Channel State Information (CSI) from multiple
wireless links. The system represents the CSIs as Multi-link Time-Frequency
(MLTF) image by organizing them as time-frequency matrices and utilize these
MLTF images as the input feature for CNN network. The CoMuTe models multi
target localization as a multi label spot classification approach under the
assumption that each MLTF image is associated with multiple labels/spots. The
localization is performed with a training stage and a localization stage. In
the training stage, the CSI based MLTF images are constructed with single
target at each location. The constructed images are used to train the CNN via a
gradient based optimization algorithm. In the localization stage, the test MLTF
image obtained for targets at multiple spots is fed to the CNN network and
locations of the targets are calculated using sigmoid activation function in
the output layer under the multi label classification framework. Extensive
experiments are conducted to select appropriate parameters for the CNN
architecture as well as for the system design. The experimental results
demonstrate the superior performance of CoMuTe over existing multi target
localization approaches.
