基于径向基神经网络的侦察目标意图识别研究

Detection and Recognition of Air Targets by Unmanned Aerial Vehicle Based on RBF Neural Network

针对无人机空中侦察目标意图识别的问题,提出一种基于径向基神经网络的目标意图识别模型。首先利用雷达收集空中目标相关参数;然后对收集到的数据进行处理并提取特征;最后利用知识库训练好的径向基神经网络对处理后的数据进行模式识别,得到空中目标的意图。案例分析表明,相对于BP神经网络和支持向量机的目标意图识别模型,基于径向基神经网络的空中目标意图识别模型具有更高的准确性。

In order to solve the problem of detection and recognition of air targets by unmanned aerial vehicle（UAV）,a targetidentification model is established based on radial basis function（RBF）neural network. First,the relevant parameters of the targetin the air is collected by the radar. Then,the collected data is processed and the feature is extracted. Finally,the radial basis func-tion neural network,which has been trained by the knowledge dataset,is used to identify the pattern of the processed data and getthe intention of air target. The case study shows that the target identification model based on RBF neural network has a higher identi-fication precision as compared with the target identification model based on the BP neural network and SVM.