Research On Wavefront Aberration Correction Algorithm For Wavefront Sensorless Adaptive Optical System

Adaptive optics is an effective technology for correction of wavefront aberrations caused by atmospheric turbulence.Because the traditional adaptive optics system is limited in the strong turbulence environment,the wavefront sensorless adaptive optics system based on the parallel optimization algorithm is well developed.The research shows that the iterative optimization algorithm is an effective method for wavefront aberration correction,however,some iterative algorithms have the disadvantages of being sensitive to initial values,easy to fall into local extremum and slow convergence.Firstly,based on the basic model of laser light wave in turbulent atmosphere,this thesis compares several wavefront aberration fitting algorithms and compares their advantages and disadvantages.The wavefront fitting scheme based on Zernike polynomial method is determined.We numerically simulate the performance of two algorithms,use Zernike polynomial to fit the static aberration,and study a series of parameters,especially single-order aberrations and random multi-order aberrations as the initial phase to the correction performance,and the correction performance of the two algorithms is respectively evaluated using two evaluation functions,Sum-Square Error(SSE)and Strehl Ratio(SR).Time consumption is also mentioned to evaluate the performance of two algorithms.Finally,a wavefront sensorless adaptive optics system based on improved genetic algorithm was determined.In this thesis,the numerical simulation of improved genetic algorithm is completed based on Python platform and Deap / Geatpy algorithm framework.In terms of object optimization,the algorithm takes the Zernike polynomial coefficient required for wavefront reconstruction as the optimization object to replace the traditional deformable mirror driving voltage,and greatly improves the convergence speed.In terms of parameter optimization,this thesis finds a kind of optimization operator function.The optimal combination of operators makes the algorithm still have strong correction ability within the finite population size,and the time taken to optimize SR to 0.8 is shortened to 2.3s.The signal-to-noise ratio is increased by 10 to 11 times.In the process optimization,an optimization scheme based on multi-group competition evolution genetic algorithm is proposed to make the algorithm Optimization be done in very small evolutionary algebras.Finally,an adaptive optical wavefront reconstruction experimental system based on Python and C mixed programming is designed.The device selection and system construction are completed.The control module package and Python-based algorithm module of C-based deformable mirror and CCD camera are completed.The package realizes multi-platform interaction,completes the instantiation of improved genetic algorithm,and finally carries out experimental exploration and result analysis.The experimental results show that the improved genetic algorithm proposed by this design has a faster convergence speed,and the spot shape and light energy distribution after system correction are ideal.