KEYWORDS: Data acquisition, Field programmable gate arrays, Extremely high frequency, Design and modelling, Data processing, Radar signal processing, Radar, Computing systems, Digital signal processing, Data transmission
For the current millimeter wave radar imaging data volume than the mud level meter, rain gauge and other single-point monitoring means of data volume tens of times or hundreds of times geometrically increasing and other challenges, in order to meet the data acquisition computing needs, designed based on FPGA ground disaster system data acquisition computing design of colleges and universities. First of all, we analyzed the requirements of data acquisition of ground disaster system, designed the overall architecture of the system based on FPGA, developed XC7K325T as the main control chip, ADC analog-to-digital conversion and other related modules in one, and then conducted experimental verification of the data acquisition and operation system of ground disaster system, which proved that the system has good acquisition and processing performance, and effectively overcame the problems of large amount of data acquisition of ground disaster system and inaccurate acquisition and operation processing. The system can effectively overcome the problems of large volume of data collection and inaccuracy of data collection and processing, and is conducive to the subsequent data processing of the ground disaster system.
Rapid extraction of ship target information in Synthetic Aperture Radar (SAR) images plays an important role in sea surface monitoring and military prevention. However, the existing detection algorithms have disadvantages such as large model volume and slow detection speed, which are not suitable for the requirements of future star-earth integrated target detection. To solve these problems, this article proposes a SAR ship target detection method based on the improved Nanodet algorithm. To solve the problem of multi-level feature map fusion, the Ghost-pan module is added to the network to enlarge the receptive field and better fuse multi-scale features. At the same time, Resnet18 is used instead of the original backbone network, and depth-wise separable convolution is used instead of ordinary convolution to reduce the model parameter volume and improve detection efficiency. Conducted ablation experiments on the SAR dataset, and the results show that the proposed method achieves better accuracy and faster detection speed.
In order to improve the prediction accuracy of the slope deformation prediction method in mining area, aiming at the problems of many disaster causing factors of slope deformation and BP neural network is easy to fall into local minimum value, a prediction method based on the combination of genetic algorithm and BP neural network based on grey correlation analysis is proposed. The grey correlation analysis method is used to screen the main influencing factors, and the factors with high correlation degree are used as input indexes to simplify the network structure. Then, the genetic algorithm is used to optimize the BP neural network to establish the GA-BP model, and finally the prediction is compared. The results show that the grey correlation analysis can further improve the consistency between the predicted value and the real value, and the model can accurately predict the slope deformation. The research results have important auxiliary reference significance for mine safety production.
Mining coal mines can cause large scale and extensive surface subsidence in mining areas. It not only affects the economic development of the mine, but also poses a threat to the surrounding environment and the safety of people's lives. Therefore, it is very important to carry out long time surface monitoring in mining area. For D-InSAR (Differential Interferometric Synthetic Aperture Radar) technology is vulnerable to the phenomenon of unstable monitoring results caused by temporal and spatial decoherence and atmospheric delays, this study uses the StaMPS (Stanford Method for Persistent Scatterers) technology for time-series subsidence monitoring. 22 Sentinel-1A images from June 15, 2019 to December 6, 2020 were used to monitor the subsidence of Zhangshuanglou Coal Mine. The results show that: During the monitoring period, there are three obvious subsidence funnels in Zhangshuanglou Coal Mine. The study area is sinking almost all the time with only a brief rebound. The maximum displacement velocity reached -63.9 mm/yr and maximum cumulative displacement value reached -95.8 mm. Moreover, the subsidence value and velocity are almost inversely proportional to the distance to the center of the funnel. This proves that StaMPS technology and Sentinel-1A data can be used to monitor surface subsidence in mining areas and provide a basis for the study of the subsidence pattern and causes of the target area.
Aiming at the problem that the traditional viaduct deformation monitoring has high accuracy but long monitoring period and consumes a lot of manpower and material resources, it is difficult to extract the severe deformation area of viaduct in time. In this paper, based on the small baseline set interferometric synthetic aperture radar (SBAS-InSAR) technology, the deformation information of viaducts and surrounding areas is retrieved, and the severe deformation areas of urban viaducts and surrounding areas are extracted. Taking three viaducts with large traffic flow in Hohhot as the research object, the deformation results of the study area from August 2020 to September 2021 were obtained. The deformation causes are analyzed combined with the inversion results. The results show that there are five large deformation areas in the three viaducts, and the main deformation causes include soil erosion or urban waterlogging caused by rainfall, surface construction and rail transit operation. The research shows that this method can accurately extract the severe deformation area of urban viaducts, and provide a reference for analyzing the causes of viaduct deformation.
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