The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

For the rapid response of the aircraft, it is required that the aircraft is not only quick but also chooses the shortest flight routes. The great circle track is the shortest flight route on the earth between any two points, but the great circle track needs to cross the polar region when the aircraft flies over the hemispheres of East and West. Because the polar region has some particularity, such as the landform characteristics, the climatic conditions, and the vast territory, the transpolar aircraft is the key technology of global navigation. This article researched on the algorithm of SINS/GPS integrated navigation system and analyzed the result of simulation when the SINS/GPS integrated navigation system works in the middle-and low-latitude region and the polar region. The result of simulation shows that the working performance of SINS/ GPS integrated navigation system is improved and makes use of their respective advantages to overcome the shortcomings. By comparing the result of simulation, the navigation precision is consistent when the SINS/GPS integrated navigation system works in the middle-and low-latitude region and the polar region.

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The detection of internal damage characteristics of concrete is an important aspect of damage evolution mechanism in concrete meso-structure. In this paper, the improved Faster R-CNN is used to detect the porosity and cracks in concrete CT images. Based on the Faster R-CNN, ResNet-101 and ResNet-50 are used as the main framework. Feature pyramid network (FPN) and ROI Align are introduced to improve the performance of the model. FPN can generate high quality feature maps. ROI Align solves the region mismatch caused by the quantization operation. Experiments show that the detection accuracy of ResNet-101[Formula: see text]+[Formula: see text]FPN[Formula: see text]+[Formula: see text]ROI Align reaches 87.08%, which is 4.74 higher than that of ResNet-101. The detection accuracy of ResNet-50 [Formula: see text]+[Formula: see text] FPN [Formula: see text]+[Formula: see text] ROI Align reached 81.36%, which is 3.12% points higher than ResNet-50. These two improved algorithms are slower than the original algorithm for the detection time of a single picture. An effective method is provided to analyze concrete meso-damage evolution through the research.