The near-space atmosphere is thin, and the atmospheric refraction and scattering on optical observation is very small, making it very suitable for wide-area and high-resolution surveillance using high-altitude balloon platforms. This paper adopts a 9344 × 7000 CMOS sensor to obtain high-resolution images, generating large-field-of-view imaging through the swing scanning of the photoelectric sphere and image stitching. In addition, a zoom lens is designed to achieve flexible applications for different scenarios, such as large-field-of-view and high-resolution imaging. The optical design results show that the camera system has good imaging quality within the focal length range of 320 mm–106.7 mm, and the relative distortion values at different focal lengths are less than 2%. The flight results indicate that the system can achieve seamless image stitching at a resolution of 0.2 m@20 km and the imaging field of view angle exceeds 33°. This system will perform other near-space flight experiments to verify its ultra-wide (field of view exceeding 100°) high-resolution imaging application.

Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines were created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak-seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data are extracted by the statistics of the wave number locations with high frequency. The attention mechanism was used for the peak data, and the attention mechanism was weighted to the feature map of the feature extraction block. The training set, validation set and prediction set were divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, the CWT-AM-CNN proposed in this paper was compared with the classical classifier algorithm based on CO₂ feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets was as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets.

Aiming to address the problem of moving mirror speed fluctuations in moving mirror control systems, an improved active disturbance rejection double closed-loop controller (IADR-DCLC) is proposed and verified by simulation to realize the high-performance control of a moving mirror control system. First, the mathematical model of a rotary-type voice coil motor (RT VCM) is established, and the relationship between the angular velocity of the RT VCM and the optical path scanning velocity is analyzed. Second, in order to suppress the model uncertainty and external disturbance of the system, an improved active disturbance rejection controller (IADRC) is proposed. Compared with a conventional ADRC, the tracking differentiator of the proposed IADRC is replaced with desired signal optimization (DSO), and the actual speed is introduced to the extended state observer (ESO). The IADRC is used in the position–speed double closed-loop control model. Finally, the simulation results show that the IADR-DCLC has not only a good tracking effect but also a good anti-interference ability and can meet the requirements of the moving mirror control system for the uniformity of optical-path scanning speed and accurate control of the position of the moving mirror.

Near-space high-altitude balloon-based platforms have a series of advantages and provide superior conditions for optical observation. In order to ensure the stability of the optical axis of the optical detection load and stable tracking of the target, a near-space high-altitude balloon-based high-precision pointing and tracking system was designed, which can compensate for changes in the pitch angle and azimuth angle of the platform during flight. The system includes a primary platform stable pointing system and a secondary precise tracking system. In the finished flight experiment, the primary platform pointing system and secondary precise tracking system on the balloon-based observation platform worked normally, providing a guarantee for the coronagraph’s stable tracking and detection of the sun. The primary platform pointing system can realize ±1° pointing accuracy, and the simulated accuracy of the secondary precise tracking system is 4″, which guaranteed that the coronagraph obtained more than 20,000 images. In subsequent works, we will upgrade and optimize the whole system and conduct our next flight experiment in the future.

Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines are created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data is extracted by the statistics of the wave number locations with high frequency. Attention mechanism is used for the peak data, and the attention mechanism is weighted to the feature map of the feature extraction block. The training set, vali-dation set and prediction set are divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, CWT-AM-CNN proposed in this paper is compared with the classical classifier algorithm based on CO2 feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets is as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets.

Multispectral imaging plays an important role in fields such as environmental monitoring and industrial inspection. To meet the demands for high spatial resolution, portability, and multi-scenario use, this study presents a reconfigurable 2 × 3 multispectral camera-array imaging system. The system features a modular architecture, allowing for the flexible exchange of lenses and narrowband filters. Each camera node is equipped with an FPGA that performs real-time sensor control and data preprocessing. A companion host program, based on the GigE Vision protocol, was developed for synchronous control, multi-channel real-time visualization, and unified parameter configuration. End-to-end performance verification confirmed stable, lossless, and synchronous acquisition from all six 3072 × 2048-pixel resolution channels. Following field alignment, the 16 mm lens achieves an effective 4.7 MP spatial resolution. Spectral profile measurements further confirm that the system exhibits favorable spectral response characteristics. The proposed framework provides a high-resolution and flexible solution for portable multispectral imaging.