Wind Shear Forecasting for Radar Signal Clusters Using Wavelet Transformation and Class Separation Analysis
DOI:
https://doi.org/10.13052/dgaej2156-3306.405615Keywords:
Wavelet transformation, class separation degree, signal cluster set, radar signal source, wind shear prediction, feature selectionAbstract
Wind shear (WS) prediction is a critical meteorological challenge that has a major impact on flight safety and radar signal transmission. It remains a key focus in both meteorological and aerospace research. To improve the accuracy of wind shear forecasting, this study proposes a fusion model that combines wavelet transformation with class separation for predicting wind shear within radar signal clusters. The model first utilizes wavelet transformation to extract time-frequency characteristics from radar signals. Subsequently, class separation is applied to assess the separability of signals in the feature space, enabling effective dimensionality reduction and feature selection. The research was carried out using data from Tianfu International Airport, involving 95 radar systems located in various positions. Experimental results demonstrate that the proposed model surpasses other existing models in terms of prediction accuracy, robustness, and generalization capability. When the radar antenna size was set to 30.48 cm, the model achieved a radar radiation intensity of 45 W/m, notably outperforming alternative approaches. Furthermore, under a radar activity level of 400, the model exhibited a low error rate of only 3.5×10−3, highlighting its precision and stability. The model also maintains consistent performance across diverse environmental conditions, indicating strong adaptability. This study introduces a novel technical approach to enhance the ability of radar signal clustering in wind shear prediction, offering significant practical value in mitigating aviation risks associated with wind shear.
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