The disturbance in the adaptive optical system includes not only the low-frequency broadband disturbance but also the high-frequency narrow-band disturbance caused by the vibration of system components and atmospheric turbulence. A nonlinear active disturbance rejection control (NLADRC) method is proposed to solve the control problem of a deformable mirror under high-frequency narrow-band disturbance. This method does not completely depend on the mathematical model, and the algorithm structure is simple. The differential tracker flattens the abrupt part of the signal, and the extended state observer is used to estimate the disturbance in real time. Finally, the nonlinear control rate is used to compensate the disturbance. The simulation results show that compared with the classical PID control and the classical linear active disturbance rejection control, the NLADRC method can effectively improve the bandwidth of the control system and show good suppression performance against high-frequency narrow-band disturbance, and the system is more robust. Both in time domain and frequency domain, it can accurately track the system state, reduce the system delay and system error, and has better dynamic performance.
For the distributed aperture synthesis imaging system based on digital holography, the influence of sub-aperture’s tilt and displacement on the synthetic aperture image is analyzed firstly, and then a correction method based on the image correlation and image sharpness optimization is proposed and validated by experiments. The tilt of the photo detector in the sub aperture leads to the displacement of the reconstructed target amplitude while the displacement leads to the piston, tilt and displacement of the reconstructed target amplitude. For the two errors, the image correlation method can be used to estimate the tilt and displacement values initially, and then the tilt and displacement values are iterated by the stochastic parallel gradient descent algorithm to improve the sharpness of synthetic aperture images. The experimental results show that the sharpness of synthetic aperture image are significantly improved after correction, but speckle noise has an obvious influence on the correction accuracy.
For adaptive optics without wavefront detection, the wavefront control method based on deep learning is analyzed. The simulation model of adaptive optics is established,The far-field spot data collected by the photodetector is used as the input of the neural network model, and the Zernike mode coefficient is used as the output. The fully trained model can quickly and accurately recover and control the low-order wavefront. The simulation results show that convolution neural network can effectively extract image features, which is better than ordinary depth neural network model. For convolution network model, the larger the number of training sets, the smaller the value of loss function after convergence, and the higher the accuracy of the model. Compared with the traditional iterative optimization control method, the control method based on neural network model has obvious advantages in real-time.
Phase unwrapping is a classical signal processing problem, which refers to the recovery of the original phase value from the wrapped phase. Two dimensional phase unwrapping is widely used in optical measurement technology, such as digital holographic interferometry, fringe projection profilometry, synthetic aperture radar and many other applications. In this paper, a phase unwrapping method with the convolution neural network is proposed, and the feasibility is analyzed by numerical simulation. The convolution neural networks with different parameters are set up, and the phase screens used for the training set and testing set of convolution neural network are simulated with MATLAB software. The numerical simulation results show that the four convolution neural network models can be used for phase unwrapping, but the parameters have a significant impact on its accuracy.
The phase grating wavefront curvature sensor based on liquid crystal spatial light modulator is introduced. A close-loop phase retrieval method based on Eigen functions of Laplacian is proposed, and its accuracy and efficiency are analyzed through numerical experiments of atmospheric phase retrieval. The results show that the close-loop phase retrieval method has a high accuracy. Moreover, it is stable regardless of modal cross coupling.
Considering the influences of speckle noises and wavefront aberration error on image quality in active imaging based on spatial heterodyne detection, a wavefront correction method that based on metric optimization of multi images is proposed, in which the multi images are generated by aperture dividing technique. An experimental setup is established, and the experiments that correcting the aberration of itself with the above method are performed, in which the stochastic parallel gradient descent algorithm and image sharpness function are used. The results show that the method of multi images averaging can be used to improve the signal-to-noise ratio of target image effectively, and a higher quality of target image can be achieved after correction by optimizing the image sharpness metric that generated with the averaging data of multi images.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.