Test and Integration of Location Sensors for Position Determination in a Pedestrian Navigation System

• 발행기관: 한국항해항만학회
• 저자명: Guenther Retscher Michael Thienelt
• 간행물 정보: 『한국항해항만학회 학술대회논문집』2006년도 International Symposium on GPS/GNSS Vol.1, 1~6쪽, 전체 6쪽
• 주제분류: 공학 > 해양공학
• 파일형태: PDF
• 발행일자: 2006.10.24

영문 초록

In the work package “Integrated Positioning” of the research project NAVIO (Pedestrian Navigation Systems in Combined Indoor/Outdoor Environements) we are dealing with the navigation and guidance of visitors of our University. Thereby start points are public transport stops in the surroundings of the Vienna University of Technology and the user of the system should be guided to certain office rooms or persons. For the position determination of the user different location sensors are employed, i.e., for outdoor positioning GPS and dead reckoning sensors such as a digital compass and gyro for heading determination and accelerometers for the determination of the travelled distance as well as a barometric pressure sensor for altitude determination and for indoor areas location determination using WiFi fingerprinting. All sensors and positioning methods are combined and integrated using a Kalman filter approach. Then an optimal estimate of the current location of the user is obtained using the filter. To perform an adequate weighting of the sensors in the stochastic filter model, the sensor characteristics and their performance was investigated in several tests. The tests were performed in different environments either with free satellite visibility or in urban canyons as well as inside of buildings. The tests have shown that it is possible to determine the user’s location continuously with the required precision and that the selected sensors provide a good performance and high reliability. Selected tests results and our approach will be presented in the paper.

목차

1. Introduction

2. The NAVIO System Design

3. Sensor Integration using a New Multi-SensorFusion Model

4. Location Sensor Testing

5. Sensor Integration Performance Test

6. Indoor Location Determination

7. Conclusions And Outlook

키워드

Integrated Positioning Navigation in unfamiliar environment Indoor location Sensor fusion Kalman filter