In this research, we propose an adaptive optical wavefront reconstruction technique grounded in the multi-plane phase difference method. During the wavefront reconstruction process, wavefront information is approximated using multiple planes, while Zernike coefficients are estimated through an iterative optimization algorithm. Furthermore, we introduce a combination of Hessian matrix analysis and Tikhonov regularization to further refine the accuracy and stability of the wavefront reconstruction. During the experiment, the beam expander system was combined with the 4f system, and precise displacement stages were utilized for multifocal plane measurements. The results indicate that this method markedly enhances the efficacy of wavefront correction, offering a novel technical approach for the application of adaptive optics systems in the realm of high-precision imaging. This methodology addresses the challenges of low reconstruction accuracy, prolonged computation times, and excessively stringent requirements for focusing plane measurement accuracy and defocusing amounts associated with traditional phase difference algorithms in the wavefront correction process.

An Adaptive Unscented Kalman Filter (AUKF) method combining sensor fusion algorithm with Artificial Neural Network (ANN) is designed for high precision attitude tracking of low cost, small size Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU) in high dynamic environment. The different control strategies fusing multi MEMS inertial sensors are adopted under various dynamic situations. The AUKF attitude estimation approach utilizing the MEMS sensor and Global Positioning System (GPS) could provide reliable estimation in high dynamic environmental variations. The adaptive scale factor is used to adaptively weaken or enhance the effects on new measurement data through the adjustment of the estimation according to the predicted residual vector. To solve the problem that new measurement data could not be updated in case of GPS failure situation, an attitude algorithm based on RBF-ANN feedback correction is proposed to apply in AUKF. The estimated deviation of predicted non-augmented system state would be provided based on Radial Basis Function (RBF)-ANN. The corrected the predicted non-augmented system state would be used for estimation process in AUKF. The experiment platform simulating the rotation of spinning projectile the was setup. The comparative experimental results show better control performance of the proposed method under various dynamic conditions.