One of the main tasks of the spacecraft is to carry out long-term spaceflights, science research and other activities. For different scientific applications, we need to know about the strain of cabin for evaluating the health condition of spacecraft. Here, two factors mainly work. One is the temperature, and another is refueling operations. For the latter, it is necessary to carry out the unscheduled refueling for life or experiments. The advantages of FBG sensor is suitable for application in aircraft. This paper shows the detection of static and dynamic strain under in flight environment of a certain aircraft by FBG sensors and resistance strain gauge. In the static strain detection, taking the resistance strain gauge as measurement standard, FBG sensors have a large measurement error. For example, for testing position "A", the initial error is around 28.13%. After thermal compensation of bulkhead for FBG sensors, error value reduces to 5.95%. After thermal compensation of bulkhead both for FBG sensors and resistance strain gauge, error value reduces to 0.36%. In the dynamic strain measurement, the impact test of different positions in the same height and different heights on the same position for the bulkhead is carried out. The results show that the two measurement methods are accordant in high frequency and able to identify impact signals effectively. It is very important for structure prediction and health assessment.
Low level light(LLL) image contains rich information on environment details, but is easily affected by the weather. In the case of smoke, rain, cloud or fog, much target information will lose. Infrared image, which is from the radiation produced by the object itself, can be "active" to obtain the target information in the scene. However, the image contrast and resolution is bad, the ability of the acquisition of target details is very poor, and the imaging mode does not conform to the human visual habit. The fusion of LLL and infrared image can make up for the deficiency of each sensor and give play to the advantages of single sensor. At first, we show the hardware design of fusion circuit. Then, through the recognition probability calculation of the target(one person) and the background image(trees), we find that the trees detection probability of LLL image is higher than that of the infrared image, and the person detection probability of the infrared image is obviously higher than that of LLL image. The detection probability of fusion image for one person and trees is higher than that of single detector. Therefore, image fusion can significantly enlarge recognition probability and improve detection efficiency.
The unmanned aerial vehicle (UAV) in flight needs to face the complicated environment, especially to withstand harsh weather conditions, such as the temperature and pressure. Compared with conventional sensors, fiber Bragg grating (FBG) sensor has the advantages of small size, light weight, high reliability, high precision, anti-electromagnetic interference, long lift-span, moistureproof and good resistance to causticity. It’s easy to be embedded in composite structural components of UAVs. In the paper, over 1000 FBG sensors distribute regularly on a wide range of UAVs body, combining wavelength division multiplexing (WDM), time division multiplexing (TDM) and multichannel parallel architecture. WDM has the advantage of high spatial resolution. TDM has the advantage of large capacity and wide range. It is worthful to constitute a sensor network by different technologies. For the signal demodulation of FBG sensor array, WDM works by means of wavelength scanning light sources and F-P etalon. TDM adopts the technology of optical time-domain reflectometry. In order to demodulate efficiently, the most proper sensor multiplex number with some reflectivity is given by the curves fitting. Due to the regular array arrangement of FBG sensors on the UAVs, we can acquire the health state of UAVs in the form of 3D visualization. It is helpful to master the information of health status rapidly and give a real-time health evaluation.