Assisted GNSS Positioning for Urban Navigation Based on Receiver Clock Bias Estimation and Prediction Using Improved ARMA Model

• 발행기관: 한국항해항만학회
• 저자명: LinyuanXia Esmond Mok
• 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

Among the various error sources in positioning and navigation, the paper focuses on the modeling and prediction of receiver clock bias and then tries to achieve positioning based on simulated and predicted clock bias. With the SA off, it is possible to model receiver clock bias more accurately. We selected several types of GNSS receivers for test using ARMA model. To facilitate prediction with short and limited sample pseudorange observations, AR and ARMA are compared, and the improved AR model is presented to model and predict receiver clock bias based on previous solutions. Our work extends to clock bias prediction and positioning based on predicted clock bias using only 3 satellites that is usually the case under urban canyon situation. In contrast to previous experiences, we find that a receiver clock bias can be well modeled using adopted ARMA model. Test has been done on various types of GNSS receivers to show the validation of developed model. To further develop this work, we compare solution conditions in terms of DOP values when point positioning is conducted using 3 satellites to simulate urban positioning environment. When condition allows, height component is derived from other ways and can be set as known values. Given this condition, location is possible using less than 2 GNSS satellites with fixed height. Solution condition is also discussed for this background using mode of constrained positioning. We finally suggest an effective predictive time span based on our test exploration under varied conditions.

목차

1. Introduction

2. Brief on ARMA models

3. Receiver Clock and Its Modeling

4. Prediction of Receiver Clock

5. Conclusion and Summary

키워드

receiver clock bias ARMA model model and prediction constrained positioning