CSI-based Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

• URL: http://arxiv.org/abs/2205.05775v1
• Date: Wed, 11 May 2022 21:11:05 GMT
• Title: CSI-based Indoor Localization via Attention-Augmented Residual Convolutional Neural Network
• Authors: Bowen Zhang and Houssem Sifaou and Geoffrey Ye Li
• Abstract summary: This paper presents a new localization system with high accuracy and generality. We propose a novel attention-augmented Residual CNN to utilize the local information and global context in CSI exhaustively. Consider 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.
• Score: 38.826117059245895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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