复杂环境中无人机数据链干扰效果预测方法

针对无人机(unmanned aerial vehicle, UAV)数据链在地理环境、气象环境、电磁环境等构成的复杂环境中受干扰程度提出一种结合支持向量机(support vector machine, SVM)与功率准则的预测评估方法。首先对系统误码率以及干扰传播模型进行仿真,并针对两个单音干扰存在的情况获得仿真数据、划分干扰等级,得到学习样本;比较了特征归一化和特征降维这两种不同输入形式与4种不同核函数下的学习效果,获得了具有良好效果的预测模型;应用该模型并结合具体地理环境对实际场景进行干扰程度预测,结果与理论值相符,证明了方法的可行性和有效性;预测结果也可用于UAV航路规划与航路评估,具有很强的应用性。

Prediction of interference effect on UAV data link in complex environment

A method to predict the interference effect on unmanned aerial vehicle (UAV) data link in complex environment containing terrain, weather and electromagnetic conditions using the support vector machine (SVM) and the power rule is proposed. First, bit error rate (BER) of UAV data link and the propagation model are simulated, then obtain the simulation data in terms of the two single-tone interferences case， rate simulation results into five different degrees， and get importing learning samples. Compare the learning accuracy of two types of input parameters including feature normalization and feature dimension reduction and four different kernels by grid search and cross validation to obtain the best prediction model. Finally, the feasibility and validity of this method is demonstrated by comparing the theoretical calculation and the prediction results of an actual scenario by using this model. The prediction results can be also used in path planning and path analyzing of UAV, so this method has high practicability.