CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach

TL;DR

This paper introduces DeepFi, a deep learning-based indoor fingerprinting system utilizing CSI for high-accuracy localization, demonstrating significant error reduction over existing methods in real environments.

Contribution

The paper presents a novel deep learning framework for CSI-based fingerprinting, including a greedy training algorithm and probabilistic localization, improving indoor positioning accuracy.

Findings

DeepFi reduces localization error compared to existing methods.

DeepFi effectively trains deep networks for fingerprinting with layer-wise greedy learning.

Experimental results confirm improved accuracy in real indoor environments.

Abstract

With the fast growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted a lot of interest due to its high accuracy. In this paper, we present a novel deep learning based indoor fingerprinting system using Channel State Information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an off-line training phase and an on-line localization phase. In the off-line training phase, deep learning is utilized to train all the weights of a deep network as fingerprints. Moreover, a greedy learning algorithm is used to train the weights layer-by-layer to reduce complexity. In the on-line localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error compared with three existing methods in two representative indoor environments.