The application of magnetic field detector in the actual road section mainly depends on the layout of these detectors in the actual traffic environment. For example, geomagnetism and coils are easily disturbed by vehicles near the road, resulting in missed or false detection; Video detection equipment depends on weather, visibility and other environmental conditions, and there will be a large degree of missed detection. Compared with the disadvantages of the above sensors, Millimeter-wave Radar has many unique advantages, including insensitivity to light or weather, wider application range compared with vision based technology, and higher accuracy,which makes millimeter-wave radar more outstanding in traffic monitoring applications. This paper presents a detection model of millimeter wave radar spectrum based on YOLOX, which is used to monitor traffic. Firstly, this paper enhances the millimeter-wave radar spectrum data used for training by gray-scale transformation, solves the problem that the Doppler target echo is not obvious, and obtains a clearer target image. Secondly, the latest YOLOX algorithm is used to train 2844 radar spectrum diagrams, and the performance evaluation is carried out to obtain the YOLOX average mAP@0.5 The value is 0.916 and FPS is 35.8. Finally, experiments show that YOLOX algorithm is better than YOLOv5 algorithm mAP@0.5 It is 2.9% higher, which proves the superiority of the algorithm.
Over-the-horizon radar (OTHR) is an important equipment for the ultralong-range early warning in the military, but the use of constant false-alarm rate (CFAR), which is a traditional detection method, makes it difficult in multiaircraft formation recognition. To solve this problem, a multi-aircraft formation recognition method based on deep transfer learning in OTHR is proposed. First, the range-Doppler images of aircraft formation in OTHR are simulated, which are composed of four categories of samples. Secondly, a recognition model based on Convolutional Neural Network (CNN) and CFAR detection technology is constructed, whose training method is designed as a two-step transfer. Finally, the trained model can well distinguish the spectral characteristics of aircraft formation, and then recognize the aircraft number of a formation. Experiments show that the proposed method is better than the traditional CFAR detection method, and can detect the number of aircraft more accurately in the formation with the same false alarm rate. INDEX TERMS multi-aircraft formation; range-Doppler image; OTHR; deep transfer learning
By mining association rules in large data, we can reveal useful information contained in the data and find out the relationship between things or the law of motion. However, because of the huge transaction data, the association rules obtained by mining are complex and massive. It is difficult to find useful association relations, especially when the re-demand is uncertain. To solve this problem, this paper first uses Apriori algorithm to mine association rules from a data set, then defines similarity measure between association rules, and applies DBSCAN clustering algorithm to association rules analysis. The analysis results show that this method is effective in association rules analysis.
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