Application of random sample consensus method for parameter estimation of reflectometry density profile in toroidal plasma

Microwave reflectometry diagnostics have been widely used to measure density profiles in fusion plasma. However, the high sensitivity of the diagnostics to plasma turbulence often results in large radial deviations in the edge density profile and causes difficulty in profile evaluation. To improve the performance of profile evaluation, a modified RANdom SAmple Consensus (RANSAC) method has been applied to fit the density profiles measured by reflectometry on the experimental advanced superconducting tokamak. Compared with the traditional least-squares method, the modified RANSAC method is much more efficient and robust in fitting the experimental profiles. Furthermore, a combination of RANSAC and a genetic algorithm (GA-RANSAC) is used to further optimize the profile evaluation procedure. The results show that this GA-RANSAC method yields better performance and stabler convergence than the modified RANSAC alone.