QU Guoqing, SUN Zhen, SU Xiaoqing, DU Cunpeng. Adaptive Relaxation Regularization Algorithm for Nonlinear Parameter Estimation[J]. Geomatics and Information Science of Wuhan University, 2019, 44(10): 1491-1497. DOI: 10.13203/j.whugis20170425
Citation: QU Guoqing, SUN Zhen, SU Xiaoqing, DU Cunpeng. Adaptive Relaxation Regularization Algorithm for Nonlinear Parameter Estimation[J]. Geomatics and Information Science of Wuhan University, 2019, 44(10): 1491-1497. DOI: 10.13203/j.whugis20170425

Adaptive Relaxation Regularization Algorithm for Nonlinear Parameter Estimation

Funds: 

The National High-tech Research and Development Program of China 2016YFB051700

the National Natural Science Foundation of China 41674014

the National Natural Science Foundation of China 41704003

More Information
  • Author Bio:

    QU Guoqing, professor, specializes in the geodesy data processing, qgq@sdut.edu.cn

  • Corresponding author:

    SUN Zhen, postgraduate. E-mail:986254102@qq.com

  • Received Date: December 16, 2018
  • Published Date: October 04, 2019
  • This paper discusses the ill-posed nonlinear least squares problem, and proposes an adaptive relaxation algorithm based on the regularization method for stabilizing the nonlinear parameter estimation. The improved algorithm achieves the adaptive selection on the regularization parameter and iterative step by using an incremental geometric regularization parameter and the minimal residual criterion. The numerical convergence experiments of the method are performed. The results show that the numerical precision of our proposed method is better than that of the linearized adjustment estimation, and the convergence property is more efficient than the iterative Tikhonov regularization method.
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