Analysis of Relaxation Factor Function for Ionospheric Tomography

In ionospheric tomography, the relaxation factor in the algorithm is a crucial parameter in the iterative reconstruction process, which determines the amount of correction in each update and affects the accuracy and efficiency of the algorithm. Therefore, in order to improve the accuracy of the algorithm, reduce the number of iterations, and enhance the efficiency of tomographic inversion, this paper proposes a method to set the relaxation factor in the algebraic reconstruction algorithm as a decaying function. This algebraic reconstruction algorithm is applied to pixel-based tomography of the ionosphere in the Hong Kong region. The convergence accuracy, external consistency accuracy, and number of iterations obtained using this method are compared with those obtained when the relaxation factor is fixed. The results show that when the relaxation factor is chosen as one of these decaying functions, the convergence accuracy is better than that with a fixed value, and the number of iterations is significantly reduced. This method can improve the accuracy and efficiency of the algebraic reconstruction algorithm in ionospheric tomography.