基于卷积神经网络的卫星网络协调态势评估方法

为充分发掘利用海量卫星网络数据,提高决策效率,加强空间频轨资源获取与储备的分析手段,尤其是对地球静止轨道资源的协调获取问题,提出基于机器学习算法的卫星网络态势评估策略。通过对卫星网络协调因素进行特征分析,选择卷积神经网络(Convolution Neural Network, CNN)为目标算法模型,并建立算法模型的训练数据集及Label规则,采用分裂信息增益度量方法对数据进行降维处理,建立CNN评估模型,并进行了验证分析。结果表明,CNN模型对卫星网络协调态势评估问题测试的正确率高达80%以上,具有较高的评估效能。随着数据量的增多,CNN评估效果逐步提升,是一种在卫星网络协调态势分析、资源储备的有效评估方法。

Satellite networks coordination situation assessment method based on convolution neural network

In order to fully explore the use of massive satellite network data, improve decision-making efficiency,and strengthen the analysis methods of spatial frequency and orbit resource acquisition and storage, especially for the GSO (geostationary satellite orbit) resource selection problem, a satellite network situation assessment strategy based on machine learning algorithm was proposed. By analyzing the characteristics of satellite network coordination factors, the CNN (convolutional neural network) was selected as the target algorithm model, and the training data set and label rules of the algorithm model were established. The data is reduced by the split information gain measurement method and a CNN evaluation model was established. Afterwards, a verification analysis was performed. Results show that the CNN model has a correct rate of 80% or more for the satellite network coordination situation assessment problem, and has high evaluation efficiency. Moreover, with the increase of the amount of data, the evaluation effect of CNN is gradually improved, which indicates the proposed method is an effective evaluation method for coordination situation analysis and resource reserve in satellite networks.