A feature-based phase retrieval wavefront sensing approach using machine learning is proposed in contrast to the conventional intensity-based approaches. Specifically, the Tchebichef moments which are orthogonal in the discrete domain of the image coordinate space are introduced to represent the features of the point spread functions (PSFs) at the in-focus and defocus image planes. The back-propagation artificial neural network, which is one of most wide applied machine learning tool, is utilized to establish the nonlinear mapping between the Tchebichef moment features and the corresponding aberration coefficients of the optical system. The Tchebichef moments can effectively characterize the intensity distribution of the PSFs. Once well trained, the neural network can directly output the aberration coefficients of the optical system to a good precision with these image features serving as the input. Adequate experiments are implemented to demonstrate the effectiveness and accuracy of proposed approach. This work presents a feasible and easy-implemented way to improve the efficiency and robustness of the phase retrieval wavefront sensing.

Recently, considerable efforts have been devoted to the estimation of soil properties using optical payloads mounted on drones or satellites. Nevertheless, many studies focus on diverse pretreatments and modeling techniques, while there continues to be a conspicuous absence of research examining the impact of parameters related to optical remote sensing payloads on predictive performance. The main aim of this study is to evaluate how the spectral resolution and signal-to-noise ratio (SNR) of spectrometers affect the precision of predictions for soil organic matter (SOM) content. For this purpose, the initial soil spectral library was partitioned into to two simulated soil spectral libraries, each of which were individually adjusted with respect to the spectral resolutions and SNR levels. To verify the consistency and generality of our results, we employed four multiple regression models to develop multivariate calibration models. Subsequently, in order to determine the minimum spectral resolution and SNR level without significantly affecting the prediction accuracy, we conducted ANOVA tests on the RMSE and ${\mathrm{R}}^2$ obtained from the independent validation dataset. Our results revealed that (i) the factors significantly affecting SOM prediction performance, in descending order of magnitude, were the SNR levels > spectral resolutions > estimation models, (ii) no substantial difference existed in predictive performance when the spectral resolution fell within 100 nm, and (iii) when the SNR levels exceeded 15%, altering them did not notably affect the SOM predictive performance. This study is expected to provide valuable insights for the design of future optical remote sensing payloads aimed at monitoring large-scale SOM dynamics.

The positioning accuracy and speed stability of the brushless DC motor (BLDC motor), as the drive element of the optomechanically scanned system (OMSS), are closely interrelated to the final imaging quality of the system. Active disturbance rejection control (ADRC) with strong anti-interference ability, fast response and good robustness is one of the extensively used control strategies. However, the performance of ADRC working in a complicated environment will be limited due to the controller structure, parameter tuning and the influence of multi-source nonlinear disturbance. Therefore, an improved ADRC method is proposed, which can switch between ‘point-to-point control mode’ and ‘stable speed control mode’ according to the system requirements. To further suppress the torque ripple and improve the control performance of the system, an improved harmonic injection scheme is added, and the parameters of improved ADRC are tuned by a slime mould algorithm based on a Levy flight operator (LF-SMA). The stability of the proposed ADRC is proved by Lyapunov stability theory. The experimental results show that the proposed control scheme could be available to reduce the torque ripple of the system.

Space-based gravitational wave detection programs, such as the Laser Interferometer Space Antenna (LISA) or Taiji program, obtain gravitational wave signals by measuring the change in the distance between three satellites by laser. The telescope is an important part of the measurement system, and its function is to transmit and receive laser signals. Due to changes in the space environment, the telescope will inevitably introduce additional dynamic aberrations, which will bring optical path errors to the inversion of gravitational wave signals. Taking LISA as an example, to achieve pm-level measurement accuracy at the detection frequency of 0.1 mHz–1 Hz, the stability requirements of the telescope are less than 1 pm/Hz^1/2. This paper theoretically deduces the aberration types that affect the telescope’s stability and conducts simulation analysis according to the actual phase demodulation method, which verifies the theory’s correctness. In addition, using this theory, it can be concluded that under the condition that the total size of the telescope aberration is determined to be stable, reducing the ratio of rotationally symmetric aberrations such as “spherical aberration” and “defocusing” among common aberrations can significantly improve the stability of the telescope. The conclusion guides the optical system design of LISA or Taiji.

Barrier-function-based adaptive fast-terminal sliding-mode control approaches have been devised to enhance the precision of speed regulation of permanent magnet synchronous motors (PMSMs). Firstly, the speed loop utilizes fast-terminal sliding-mode control, which contributes to a faster convergence rate and enhances the robustness of the system. By adopting this control technique, the system can quickly reach the desired speed setpoint and effectively handle disturbances. Secondly, an adaptive law based on the barrier function is employed to adjust the control gain adaptively. The proposed adaptive law considers the magnitude of the disturbance and effectively mitigates chattering resulting from excessive switching gain. Unlike conventional control methods, the design of the adaptive fast-terminal sliding-mode control does not require attaining the upper limit of the lumped disturbances. Experimental results are presented to validate the proposed approach. These results demonstrate that the proposed method outperforms the conventional terminal sliding mode control technique in terms of handling both external and internal disturbances.

Cloud and aerosol polarization imaging detector (CAPI) is one of the important payloads on the China Carbon Dioxide Observation Satellite (TANSAT), which can realize multispectral polarization detection and accurate on-orbit calibration. The main function of the instrument is to identify the interference of clouds and aerosols in the atmospheric detection path and to improve the retrieval accuracy of greenhouse gases. Therefore, it is of great significance to accurately identify the clouds in remote sensing images. However, in order to meet the requirement of lightweight design, CAPI is only equipped with channels in the near-ultraviolet to near-infrared bands. It is difficult to achieve effective cloud recognition using traditional visible light to thermal infrared band spectral threshold cloud detection algorithms. In order to solve the above problem, this paper innovatively proposes a cloud detection method based on different threshold tests from near ultraviolet to near infrared (NNDT). This algorithm first introduces the 0.38 μm band and the ratio of 0.38 μm band to 1.64 μm band, to realize the separation of cloud pixels and clear sky pixels, which can take advantage of the obvious difference in radiation characteristics between clouds and ground objects in the near-ultraviolet band and the advantages of the band ratio in identifying clouds on the snow surface. The experimental results show that the cloud recognition hit rate (POD_cloud) reaches 0.94 (ocean), 0.98 (vegetation), 0.99 (desert), and 0.86 (polar), which therefore achieve the application standard of CAPI data cloud detection The research shows that the NNDT algorithm replaces the demand for thermal infrared bands for cloud detection, gets rid of the dependence on the minimum surface reflectance database that is embodied in traditional cloud recognition algorithms, and lays the foundation for aerosol and CO₂ parameter inversion.