The Data Processing Method for Small Samples and Multi-variates Series in GPS Deformation Monitoring

• 발행기관: 한국항해항만학회
• 저자명: Liu Guo-lin Zheng Wen-Hua Wang Xin-zhou Zhang Lian-Peng
• 간행물 정보: 『한국항해항만학회 학술대회논문집』2006년도 International Symposium on GPS/GNSS Vol.1, 1~5쪽, 전체 5쪽
• 주제분류: 공학 > 해양공학
• 파일형태: PDF
• 발행일자: 2006.10.24

영문 초록

Time series analysis is a frequently effective method of constructing model and prediction in data processing of deformation monitoring. The monitoring data sample must to be as more as possible and time intervals are equal roughly so as to construct time series model accurately and achieve reliable prediction. But in the project practice of GPS deformation monitoring, the monitoring data sample can’t be obtained too much and time intervals are not equal because of being restricted by all kinds of factors, and it contains many variates in the deformation model moreover. It is very important to study the data processing method for small samples and multi-variates time series in GPS deformation monitoring. A new method of establishing small samples and multi-variates deformation model and prediction model are put forward so as to resolve contradiction of small samples and multi-variates encountered in constructing deformation model and improve formerly data processing method of deformation monitoring. Based on the system theory, a deformation body is regarded as a whole organism; a time-dependence linear system model and a time-dependence bilinear system model are established. The dynamic parameters estimation is derived by means of prediction fit and least information distribution criteria. The final example demonstrates the validity and practice of this method.

목차

1. Introduction

2. Time-dependence Deformation Monitor System Model

3. Dynamic Parameters Estimation

4. Distinguish Rule and Example

5. Conclusion

키워드

Small Samples Data processing GPS deformation monitoring