基于支持向量机的机场智能驱鸟决策

为提高机场鸟击防范管理水平，实现探鸟雷达与多种驱鸟设备联动，提出一种基于支持向量机（SVM）的机场智能驱鸟决策方法。该方法包括训练和测试两部分。训练部分利用机场鸟类探测预警与驱赶联动系统获取的大量历史鸟情信息，结合专家知识，通过数据预处理与支持向量机训练，建立驱鸟策略分类模型；测试部分根据驱鸟实时智能决策结果，对驱鸟策略分类模型进行持续修正与优化。通过某机场的实测鸟情信息数据与若干驱鸟实例，证明驱鸟策略分类模型具有较高的决策正确率，并能够通过自身修正与优化应对各种新问题。本文方法针对实时鸟情信息，实现了多种驱鸟设备的优化组合，克服了驱鸟设备长期重复运行造成的鸟类对驱鸟设备的耐受性问题，极大改善了驱鸟效果。 

Intelligent decision making for airport bird-repelling with support vector machine

To impove the management of bird-strike avoidance at airport and realize the linkage of avian radar with multiple bird-repelling devices, an intelligent decision making method was proposed for airport bird-repelling based on support vector machine (SVM). The method includes two steps of training and testing. In the training step, the bird-repelling strategy classification model was established by data pretreatment and SVM training, which are combined with expert knowledge and large amount of historical bird information collected by the airport linkage system for bird detection, surveillance and repelling. In the testing step, the bird-repelling strategy classification model was continuously corrected and optimized according to the real-time intelligent bird-repelling strategy results. Through the real bird information data and several bird-repelling examples of a certain airport, it is demonstrated that the decision accuracy of bird-repelling strategy classification model is relatively high, and it can solve new problems by self correction and optimization. The proposed method achieves the optimized combination of multiple bird-repelling devices against real-time bird information with great improvement of bird-repelling effect, overcoming the tolerance of birds to the bird-repelling devices due to their long-term repeated operation.