Improving Covariance Based Adaptive Estimation for GPS/INS Integration

• 발행기관: 한국항해항만학회
• 저자명: Weidong Ding Jinling Wang Chris Rizos
• 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

It is well known that the uncertainty of the covariance parameters of the process noise (Q) and the observation errors (R) has a significant impact on Kalman filtering performance. Q and R influence the weight that the filter applies between the existing process information and the latest measurements. Errors in any of them may result in the filter being suboptimal or even cause it to diverge. The conventional way of determining Q and R requires good a priori knowledge of the process noises and measurement errors, which normally comes from intensive empirical analysis. Many adaptive methods have been developed to overcome the conventional Kalman filter’s limitations. Starting from covariance matching principles, an innovative adaptive process noise scaling algorithm has been proposed in this paper. Without artificial or empirical parameters to be set, the proposed adaptive mechanism drives the filter autonomously to the optimal mode. The proposed algorithm has been tested using road test data, showing significant improvements to filtering performance.

목차

1. Introduction

2. Adaptive Kalman filtering

3. Testing

4. Conclusion

키워드

GPS/INS adaptive Kalman filter stochastic modelling covariance matching process noise scaling