학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 여재익.본 논문에서는 스마트폰 Galaxy S8 환경에서 GPS / INS 결합 보행자 항법을 수행하였으며, 스마트폰 센서의 특성을 자세히 분석하였다. 이에 최근 공개된 Android GNSS API 를 사용하여 GPS 원시데이터를 항법에 이용하면서, Cycle slip 을 보정한 Carrier phase 를 이용한 속도 결정법을 사용하였다. 이로 인해 기존의 NMEA GPS 를 사용한 방식의 스마트폰 보행자 항법보다 정밀한 위치, 속도 항법이 가능하였고, 성능을 향상 시켰다. 또한 사용자 상황 분석이 가능한 분류 딥러닝 기법을 사용하여 각 보행 상황에 따른 분류감지가 가능하였음 보였으며, LSTM 의 입력부분을 변화한 몇가지 딥러닝 모델의 성능을 비교하였다. 이를 통해서 사용자의 보행 상황에 따른 적응적 보행자 항법 파라메터 결정이 가능함의 가능성을 보였다.In this research, the overall construction of the smartphone GPS / INS pedestrian dead reckoning system is detaily described with considering the smartphone sensor measurement properties. Also, the recent android GNSS API which can provide the raw GPS measurement is used. With carrier phase, the cycleslip compensated velocity determination is considered. As a result, the carrier phase /INS integrated pedestrian dead reckoning shows the more precise navigation accuracy than NMEA. Moreover, The deep learning approach is applied in the user context classification to change the parameters in the pedestrian dead reckoning system. The author compares the effect of several transformed inputs for the LSTM model and validate each classification performances.Abstract i
Contents ii
List of Figures iv
List of Tables vii
Chapter 1. Introduction 1
1.1 Motivation and Backgrounds 1
1.2 Research Purpose and Contribution 3
1.3 Contents and Methods of Research 3
Chapter 2. Smartphone GPS / INS measurements analysis 4
2.1 Smartphone GNSS measurements 4
2.1.1 Android Raw GNSS Measurements API 4
2.1.2 Raw GPS Measurements Properties 7
2.1.3 Smartphone NMEA Location Provider 8
2.1.4 Pseudorange Based Position Estimation 10
2.1.5 Position Determination Experiment 11
2.2 Smartphone INS Measurements 12
2.2.1 Android Sensor Manager API 12
2.2.2 INS Measurements Properties 13
2.2.3 Noise level, Constant bias, Scale factor, Calibration 14
2.2.4. Accelerometer, Gyroscope Calibration Experiment 17
2.2.5 Magnetometer Ellipse Fitting Calibration 22
2.2.6 Random Bias, Allan Variance Exiperiment 25
2.3 Developed Android Smartphone App 30
Chapter 3. Pedestrian Dead Reckoning 31
3.1 Pedestrian Dead-Reckoning System 31
3.1.1 Attitude Determination Quaternion Kalman Filter 32
3.1.2 Attitude Determination Simulation , Experiment 35
3.1.3 Walking Detection 39
3.1.4 Step Counting, Stride Length 41
3.1.5 Pedestrian Dead Reckoning Experiment 45
Chapter 4. Carrier phase / INS integrated Pedestrian Dead Reckoning 50
4.1 Carrier phase Cycleslip Compensation & Velocity Determination 50
4.1.1 Carrier phase Cycleslip Compensation 50
4.1.2 Android Environment Cycle slip Detection 51
4.1.3 False Alarm & Miss Detection Analysis 55
4.1.4 Doppler, Carrier Based Velocity Estimation 56
4.1.5 Cycle slip Compensation & Velocity Determination Experiment 58
4.2 Raw GPS / INS Integrated Pedestrian Dead Reckoning 63
4.2.1 GPS / INS Integration 63
4.2.2 Position Determination Extended Kalman Filter 65
4.2.3 Raw GPS / INS Integrated Pedestrian Dead Reckoning Experiment 66
Chapter 5. User Context Classification Deep learning for Adaptive PDR 69
5.1 Smartphone Location / Walking Context Classification 69
5.1.1 Smartphone Location / Walking Context Dataset 69
5.1.2 Deep Learning Models 71
5.1.3 Comparison of Input Transformations 72
Chapter 6. Conclusion & Future work 76
Chapter 7. Bibliography 77Maste
