In the adaptive optics (AO) system, to improve the effectiveness and accuracy of wavefront sensing-less technology, a phase-based sensing approach using machine learning is proposed. In contrast to the traditional gradient-based optimization methods, the model we designed is based on an improved convolutional neural network. Specifically, the deconvolution layer, which reconstructs unknown input by measuring output, is introduced to represent the phase maps of the point spread functions at the in focus and defocus planes. The improved convolutional neural network is utilized to establish the nonlinear mapping between the input point spread functions and the corresponding phase maps of the optical system. Once well trained, the model can directly output the aberration map of the optical system with good precision. Adequate simulations and experiments are introduced to demonstrate the accuracy and real-time performance of the proposed method. The simulations show that even when atmospheric conditions D/r₀ = 20, the detection root-mean-square of wavefront error of the proposed method is 0.1307 λ, which has a better accuracy than existing neural networks. When D/r₀ = 15 and 10, the root-mean-square error is respectively 0.0909 λ and 0.0718 λ. It has certain applicative value in the case of medium and weak turbulence. The root-mean-square error of experiment results with D/r₀ = 20 is 0.1304 λ, proving the correctness of simulations. Moreover, this method only needs 12 ms to accomplish the calculation and it has broad prospects for real-time wavefront sensing.

In this paper, an improved method of measuring wavefront aberration based on image with machine learning is proposed. This method had better real-time performance and higher estimation accuracy in free space optical communication in cases of strong atmospheric turbulence. We demonstrated that the network we optimized could use the point spread functions (PSFs) at a defocused plane to calculate the corresponding Zernike coefficients accurately. The computation time of the network was about 6–7 ms and the root-mean-square (RMS) wavefront error (WFE) between reconstruction and input was, on average, within 0.1263 waves in the situation of D/r0 = 20 in simulation, where D was the telescope diameter and r0 was the atmospheric coherent length. Adequate simulations and experiments were carried out to indicate the effectiveness and accuracy of the proposed method.

Acquisition, tracking, and pointing (ATP) is a key technology in free space laser communication that has a characteristically high precision. In this paper, we report the acquisition and tracking of low-Earth-orbit satellites using shipborne ATP and verify the feasibility of establishing optical links between laser communication satellites and ships in the future. In particular, we developed a shipborne ATP system for satellite-to-sea applications in laser communications. We also designed an acquisition strategy for satellite-to-sea laser communication. In addition, a method was proposed for improving shipborne ATP pointing error. We tracked some stars at sea, achieving a pointing accuracy of less than 180μrad.We then acquired and tracked some low-Earth-orbit satellites at sea, achieving a tracking accuracy of about 20μrad. The results achieved in this work experimentally demonstrate the feasibility of ATP in satellite-to-sea laser communications.

In free space optical communications, long-distance transmission leads to the attenuation of beacon light, where we adopt a quadrant detector (QD) to receive the weak signal. However, the background light interferes so strongly that the output signal-to-noise ratio (SNR) of QD is at a low level, which causes a decrease in accuracy of the direct detection method. This requires finding a new light spot detection method, so an improved detection method is proposed. Because the dark current noise and the background light noise are both white noise, we adopt a Kalman filter to estimate the real output of four electric signals of QD. Unfortunately, running these through an amplifier introduces some direct current (DC) offsets into the signals. In order to balance the effect of the DC offsets, we consider using the modulation method, where we employ a sine signal to modulate the intensity of the beacon light at the transmitting end, after which we can give an inverse gain to move the center of signals to near zero to eliminate the DC offsets when we calculate the data. In Kalman filtering, we use the peak values of the signals in every period after the analog to digital converter (ADC) as the elements of the measurement matrix. Experimental results show that even when QD output SNR is about −10 dB, the detection root-mean-square errors decrease by 51.5% using the improved detection method compared with the direct detection method. Moreover, Kalman filtering does not require a large amount of data, which means it works efficiently, can reduce the cost of hardware resources, and is available for the real-time calculation of spot position.

High-precision pointing is a key technology in free-space optical communication, as it improves the acquisition probability and reduces the acquisition time. High-precision pointing of an optical communication terminal (OCT) on a motion platform is particularly important for establishing a global communication network. An OCT on a motion platform is subjected to both linear and nonlinear pointing errors. The present paper analyzes both types of pointing errors in detail. Linear pointing errors can be well corrected by the parameter model. This paper proposes a K-nearest neighbor (KNN) algorithm that corrects nonlinear pointing errors and verifies its effectiveness through tracking star experiments. After correcting the nonlinear pointing errors through KNN algorithm, the modified and actual open-loop pointing errors were 69.0 and 70.8 μrad, respectively, reduced from 87.3 and 91.9 μrad, respectively, in the parameter model. Thus, the proposed KNN algorithm can effectively improve the pointing accuracy of an OCT on a motion platform.

Photoelectric angle encoders, working as position sensors, have a great influence on the accuracy and stability of telescope control systems (TCS). In order to improve the tracking precision of TCS, a method based on subdivision error compensation for photoelectric angle encoders is proposed. First, a mathematical analysis of six types of subdivision errors (DC error, phase error, amplitude error, harmonic error, noise error, and quantization error) is presented, which is different from the previously used analysis based on the Lissajous figure method. In fact, we believe that a mathematical method is more efficient than the figure method for the expression of subdivision errors. Then, the distribution law and period length of each subdivision error are analyzed. Finally, an error compensation algorithm is presented. In a real TCS, the elevation jittering phenomenon occurs, which indicates that compensating for the amplitude error is necessary. A feed-forward loop is then introduced into the TCS, which is position loop- and velocity loop-closed, leading to a decrease of the tracking error by nearly 54.6%, from 2.31” to 1.05”, with a leading speed of 0.25°/s, and by 40.5%, from 3.01” to 1.79”, with a leading speed of 1°/s. This method can realize real-time compensation and improve the ability of TCS without any change of the hardware. In addition, independently of the environment and the kind of control strategy used, this method can also improve the tracking precision presumably because it compensates the measuring error inside the photoelectric angle encoder.