The telemetric current fluctuation of infrared earth sensor motor for a certain type of satellite is discussed, which occurred in the satellite test. The brushless DC motor selected for the infrared earth sensor has the characteristics of low-speed and small-load. By analyzing the steady-speed driving circuit of this brushless DC motor, and according to the working principle of the circuit phase locking, the cause of the telemetric current fluctuation is demonstrated. The mechanism analysis shows that the telemetric current fluctuation of the brushless DC motor is caused by the current spike generated in the motor commutation being sampled by the sampling circuit, which neither affects the safe and reliable operation of the motor nor affects the product's service life on the ground and on-orbit. By optimizing the sampling signal generation software, the telemetric current fluctuation of the motor can be effectively avoided.

Middle-wave HgCdTe detectors are the most commonly used high-performance cooled infrared focal plane array (IRFPA) detectors, which are widely used in many fields. The middle-wave mercury cadmium telluride detector has a type of isolated pixel in actual use. It has a slightly lower voltage level than the surrounding pixels, but it does not meet the blind pixel judgment standard of the national standard, which will affect the use in photoelectric systems. We named them sub bad pixels. Relying on human eyes to judge such pixels not only takes a lot of time, but also different people have different judgment standards for sub bad pixels. Aiming at the difficulty of judging the sub bad pixels mentioned above, through analyzing the judgment process of the human eyes on the sub bad pixels, and using the computer to simulate the human judgment, the judgment standard of the sub bad pixels is quantified, and the automatic identification and statistical functions are realized. Compared with the original human eye judgment method, the accuracy and efficiency of recognition are greatly improved.

Based on the actual needs of shipborne air defense missile development, the infrared radiation characteristics of Naval Strike Missile (NSM) attacking at low altitude are deeply studied, and the physical model of NSM anti-ship missile is established. The NSM anti-ship missile model is meshed by ICEM software. The temperature and infrared radiation brightness distribution of NSM anti-ship missile are simulated by CFD software. Finally, the infrared radiation intensity of NSM anti-ship missile at different detection angles is analyzed and calculated by the reverse Monte Carlo method, which provides support for the evaluation of interception capability of ship-borne air defense missiles at different angles.

In the measurement of radiation characteristics, the selection of integration time of infrared camera is an important factor affecting the accuracy of characteristic measurement. To solve this problem, the infrared image contrast, image signal-to-noise ratio and radiation characteristics measurement accuracy under the same temperature and different integration time are analyzed in this paper based on the experimental data of radiation characteristics measurement. The analysis results show that the integration time affects the measurement accuracy of radiation characteristics by affecting the contrast and signal-to-noise ratio of infrared image. Appropriately improving the integration time of infrared camera can enhance the contrast and signal-to-noise ratio of image, so as to improve the measurement accuracy of radiation characteristics. The results have guiding significance for the selection of camera integration time of infrared radiation characteristic measurement system.

In order to realize the infrared phase detection of debonding defects of concrete strengthened by bonded steel plate, the detection model is established according to the theory of heat transfer. The surface temperature is obtained by using the finite volume method, and the phase distribution is obtained by FFT. The relationship between phase difference and steel thickness, defect width, defect thickness as well as heat flux intensity is studied. The results show that the maximum value of phase difference decreases when the steel thickness increases with a certain defect size. For a given thickness of the steel plate and defect, the maximum value of phase difference increases with the increase of defect width. With a certain thickness of steel plate and defect width, when the defect height increases, the maximum value of detected surface phase difference increases to a certain extent, but the increase is small.Under certain steel thickness and defect size, the phase difference of the detected surface is almost unchanged when the heat flux increases. The results provide a theoretical basis for infrared phase detection of debonding defects of concrete strengthened by bonded steel plate.

Due to the influence of fiber loss, the temperature measurement accuracy of long distance fiber is low. In order to improve the long distance optical fiber temperature measurement accuracy, a design scheme of distributed optical fiber temperature measuring system is proposed in this paper. The loss of Raman scattering light is determined through the theoretical analysis, and the fitting model is built based on the test data of room temperature. The loss is separated, and the influence of fiber loss on temperature is eliminated by compensating the fiber loss which improves the accuracy of long distance fiber temperature. The real-time dynamic demodulation of fiber temperature is realized by the reference fiber ring. The fiber ring to be measured is placed in a constant temperature water tank at 50℃, 60℃, 70℃ and 80℃ respectively for experiment, and the experimental data are processed and analyzed.The results show that the temperature measurement accuracy of the system is ±1℃, and the spatial resolution is 2 m.

With the development of the city, the number of vehicles is increasing. This phenomenon not only increases urban congestion, but also leads to frequent traffic accidents. In order to improve the ability of urban governance, it is necessary to improve the monitoring ability of urban vehicles. In this paper, UAV is used to take low-altitude photography of four scenes in Shanghai and Chifeng area, and aerial remote sensing image data are obtained. Then, single target extraction is carried out for vehicles in UAV images combined with Unet convolutional neural network technology of deep learning. The results show that the ability of deep learning to recognize vehicles in UAV images is much higher than the random forest method in traditional machine learning, which reaches an ultra-high accuracy of 99%. And the estimation result of the number of cars in each scene is very close to the real number. According to the research results, the vehicle detection method combining UAV and deep learning technology has real time and practical feasibility, which can provide reliable technical means for real-time vehicle monitoring and traffic management in cities.