CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network
| Autor: | Bowen Zhang, Houssem Sifaou, Geoffrey Ye Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Applied Mathematics FOS: Electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Electrical Engineering and Systems Science - Signal Processing Computer Science Applications |
| Popis: | 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. 32 pages, Added references in section 2,3; Added explanations for some academic terms; Corrected typos; Added experiments in section 5, previous results unchanged; is under review for possible publication |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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