Phase-sensitive Optical Time-Domain Reflectometer ([see formula in PDF]-OTDR) technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals. This technology is widely used in applications such as intrusion monitoring and structural health assessments. Traditional signal processing methods, such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), have limitations in feature extraction and classification in complex environments. Conversely, a single deep learning model often struggles with capturing long time-series dependencies and mitigating noise interference. In this study, we propose a deep learning model that integrates Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), and Transformer modules, leveraging [see formula in PDF]-OTDR technology for distributed fiber vibration sensing event recognition. The hybrid model combines the CNN's capability to extract local features, the LSTM's ability to model temporal dynamics, and the Transformer's proficiency in capturing global dependencies. This integration significantly enhances the accuracy and robustness of event recognition. In experiments involving six types of vibration events, the model consistently achieved a validation accuracy of 0.92, and maintained a validation loss of approximately 0.2, surpassing other models, such as TAM+BiLSTM and CNN+CBAM. The results indicate that the CNN+LSTM+Transformer model is highly effective in handling vibration signal classification tasks in complex scenarios, offering a promising new direction for the application of fiber optic vibration sensing technology.

To meet the ground test requirements of star sensors, we establish the star map simulation algorithm and the interactive interface in multiple scenarios. The combination of the degradation model of star points, the imaging noise model, and the attitude disturbance model is introduced to solve the problem of different patterns of noise existing in the actual measurement, improving the traditional simulation model. In addition, a user-friendly interface design makes it easier for both scholars and average individuals to understand the parameters and then generate static single-frame star maps—or a series of dynamic sequence star maps—under various conditions. The results of the proposed star map simulation method are highly comparable to the actual captured star images, and this method can be applied for the tests and calibrations of star sensors.

For a long time, wind speed profile measurement has been the primary task of weather forecasting. Therefore, the detection of atmospheric wind speed is extremely important for studying the changes in atmospheric motion. In order to solve the problems of insufficient data collection, low resolution, and low accuracy in atmospheric wind field detection, this paper introduces the relevant theories of wind speed detection, completes the optical design of the system according to the research objectives, and determines the selection of optical devices. At the same time, a Doppler wind lidar system based on a quadrichannel Mach–Zehnder interferometer is designed and built to carry out ground-based observation experiments, collect echo signal data, and inverse the atmospheric radial wind speed. Furthermore, the wind measurement error is analyzed. Firstly, the paper introduces the basic principle of the wind measurement system, i.e., using the Doppler effect of light, and then analyzes the frequency discrimination device of the system in detail, and obtains the theoretical calculation method of atmospheric wind speed inversion. At the same time, the relevant datasets of wind measurement system are analyzed, including backscattering ratio, aerosol, and molecular extinction coefficient, and the emission mechanism of the large pulse laser is also studied in detail, which provides a theoretical basis for the model construction of Doppler lidar and the research on the enhancement of pulsed laser emission energy. Secondly, according to the research index of wind measurement, a Doppler wind measurement lidar system based on a quadrichannel Mach–Zehnder interferometer is designed, including the design of ab external light path transceiver system, internal light path interferometer, software and hardware, and algorithm. The calibration of the quadrichannel Mach–Zehnder interferometer is completed, with its maximum interference contrast reaching 0.869. Through the self-developed optical transceiver system and data acquisition system, the echo signal of lidar is received and detected. Lastly, the data of echo signals collected by the interferometer are analyzed, the radial atmospheric wind speed profile is inversed, and the signal-to-noise ratio and wind speed measurement error of the system are evaluated. The experimental results show that the maximum signal-to-noise ratio (SNR) of the system can reach 1433 when the emission pulse energy of the large pulse laser is adjusted to 255 mJ, and the farthest wind speed detection distance is about 8 km. The high-precision wind speed detection range can reach 2 km, the actual wind measurement errors in this range are all within 1.593 m/s, and the minimum error is only 0.418 m/s. In addition, the backscattering coefficient and extinction coefficient of atmospheric molecules and aerosols in the range of 8 km and the atmospheric temperature in the range of 10 km are also measured. The measurement accuracy of the aerosol extinction coefficient is ±0.001 m⁻¹, and the measurement error of atmospheric temperature within 10 km is within 2 K, achieving the expected goals.