Convolutional 신경망(CNN)을 이용한 실내 위치 측정 시스템
Indoor Localization with WiFi Fingerprinting Using Convolutional Neural Network
- 주제(키워드) Indoor Localization , Deep Learning
- 발행기관 아주대학교
- 지도교수 홍송남
- 발행년도 2020
- 학위수여년월 2020. 2
- 학위명 석사
- 학과 및 전공 일반대학원 전자공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000029599
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Indoor localization has been an active research field for decades, because of its wide range of applications. WiFi fingerprinting, which estimates the user’s locations using pre-collecting WiFi signals as references, is of particular interest as these days, every user can easily access to WiFi networks. Among numerous methods, Deep Neural Network (DNN) based methods have shown an attractive performance but their major drawback is the sensitivity to the fluctuation of received signals caused by multipaths. In order to ensure satisfactory performance, thus, a sufficiently large number of possible cases should be trained, which costs a lot. In this thesis, we address the above problem by presenting a Convolutional Neural Network (CNN) based localization method. As success in image classifications, the proposed method can be robust to the small changes of received signals as it exploits the topology of a radio map as well as signal strengths. Via experimental results, we demonstrate that the proposed CNN method can outperform the other DNN based methods using publicly available datasets provided in IPIN 2015.
more목차
CHAPTER I. INTRODUCTION 1
1.1 Background and motivation 1
1.2 Thesis organization 3
CHAPTER II. PRELIMINARIES 4
2.1 WiFi fingerprinting. 4
2.2 Deep Neural Network (DNN) . 5
2.3 Convolution Neural Network (CNN) . 6
CHAPTER III. DEEP NEURAL NETWORK (DNN) BASED WIFI FINGER PRINTING METHOD . 7
3.1 Stacked Autoencoder . 7
3.2 Deep Neural Network + Stacked Autoencoder 8
CHAPTER IV. PROPOSED CONVOLUTIONAL NEURAL NETWORK (CNN) BASED WIFI FINGERPRINTING METHOD 10
4.1 Dataset 10
4.2 Convolutional Neural Network Architecture 12
4.3 Optimization techniques 16
CHAPTER V. SIMULATION RESULTS. 19
5.1 Experiment setting . 19
5.2 Classifier Optimization 20
5.3 Comparison with the existing methods. 22
CHAPTER VI. CONCLUSIONS 26
REFERENCES 27
