  Deep learning has been widely adopted for channel state information
(CSI)-fingerprinting indoor localization systems. These systems usually consist
of two main parts, i.e., a positioning network that learns the mapping from
high-dimensional CSI to physical locations and a tracking system that utilizes
historical CSI to reduce the positioning error. This paper presents a new
localization system with high accuracy and generality. On the one hand, the
receptive field of the existing convolutional neural network (CNN)-based
positioning networks is limited, restricting their performance as useful
information in CSI is not explored thoroughly. As a solution, we propose a
novel attention-augmented residual CNN to utilize the local information and
global context in CSI exhaustively. On the other hand, considering the
generality of a tracking system, we decouple the tracking system from the CSI
environments so that one tracking system for all environments becomes possible.
Specifically, we remodel the tracking problem as a denoising task and solve it
with deep trajectory prior. Furthermore, we investigate how the precision
difference of inertial measurement units will adversely affect the tracking
performance and adopt plug-and-play to solve the precision difference problem.
Experiments show the superiority of our methods over existing approaches in
performance and generality improvement.
