直升机林区植保作业飞行进入回避区识别方法OA

As one of the important vehicles,helicopters play a key role in the forest-protection and pest control task.Compared with unmanned vehicles,helicopter can fly faster,carry more payloads with longer endurance.Avoiding entering the H-V diagram is a key issue for a helicopter's take-off and landing phase,and a significant task is to automatically identify entering the area.A method was developed for helicopters entering the H-V diagram based on support vector machine(SVM)theory,which had significant value for helicopters' safety management and flight evaluation.By selecting some data of a helicopter's H-V diagram as the training and testing groups,and the cross-validation algorithm was used to optimize kernel function's parameters,a prediction model for H-V diagram was developed based on SVM.Both the poly and RBF kernel functions were adopted for comparing the test results,and also the flight data(height-velocity)around the H-V diagram were identified based on the prediction model.The calculation showed that although the same accuracy(0.894)was obtained by using the poly and RBF kernel models,the RBF model's prediction accuracy got to 100％,better than poly kernel model(97.3％),which again showed that the RBF kernel model had enhanced generalization ability.In the future work,the high-speed H-V curve's identification should be emphasized so as to enhance the safety for helicopters in plant protection operations.