The characteristics of the normal equation created in recovering the Earth gravity model （EGM） by least-squares （LS） adjustment from the in-situ disturbing potential is discussed in detail. It can be concluded tha...The characteristics of the normal equation created in recovering the Earth gravity model （EGM） by least-squares （LS） adjustment from the in-situ disturbing potential is discussed in detail. It can be concluded that the normal equation only depends on the orbit, and the choice of a priori gravity model has no effect on the LS solution. Therefore, the accuracy of the recovered gravity model can be accurately simulated. Starting from this point, four sets of disturbing potential along the orbit with different level of noise were simulated and were used to recover the EGM. The results show that on the current accuracy level of the accelerometer calibration, the accuracy of the EGM is not sufficient to reflect the time variability of the Earth＇s gravity field, as the dynamic method revealed.展开更多
Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. I...Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.展开更多
基金Funded by the National Natural Science Foundation of China （No.40274004）, and the Open Fund of Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, China （No. 06-09）. The authors are grateful to Prof. CHAO Dingbo for his critical comments and also thank Dr. Dadzie very much for his proof-reading.
文摘The characteristics of the normal equation created in recovering the Earth gravity model （EGM） by least-squares （LS） adjustment from the in-situ disturbing potential is discussed in detail. It can be concluded that the normal equation only depends on the orbit, and the choice of a priori gravity model has no effect on the LS solution. Therefore, the accuracy of the recovered gravity model can be accurately simulated. Starting from this point, four sets of disturbing potential along the orbit with different level of noise were simulated and were used to recover the EGM. The results show that on the current accuracy level of the accelerometer calibration, the accuracy of the EGM is not sufficient to reflect the time variability of the Earth＇s gravity field, as the dynamic method revealed.
基金the National Natural Science Foundation of China (Nos. 60772007 and 60672008)China Postdoctoral Sci-ence Foundation (No. 20070410258)
文摘Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.
