电波科学学报2025，Vol.40Issue(5)：846-854,9.DOI:10.12265/j.cjors.2024176
基于粒子群优化GCN-LSTM的星间频谱预测方法
Inter-satellite spectrum prediction based method on particle swarm optimization GCN-LSTM neural network
摘要
Abstract
For the problem that geosynchronous earth orbit(GEO)satellite spectrum changes unsmoothly and nonlinearly when low earth orbit(LEO)satellite accesses GEO satellite spectrum,an improved particle swarm optimization(IPSO)based on improved particle swarm optimization,is proposed.IPSO optimizes the interstar-spectrum prediction model of graph convolutional networks and long short term memory(GCN-LSTM).The model uses GCN-LSTM network to learn the time-frequency domain characteristics of spectral data and adjust the weight allocation of key information by combining the attention mechanism.The particle swarm algorithm is improved by using the nonlinear adjustment of inertia weight strategy and Cauchy's variance strategy seeks to optimize the number of the first layer of LSTM units,the number of the second layer of LSTM units,the learning rate,dropout and batch size,which in turn improves the prediction accuracy of the model.Using the collected high-orbit satellite spectrum dataset,experimental comparisons are completed for three spectrum prediction scenarios of 1 s,30 s and 1 min,and the results show that compared with the ConvLSTM baseline model,the mean absolute error(MAE)of the proposed model has been reduced by 27％,17.63％,17.68％,respectively,and it has better spectrum prediction capability.关键词
星间频谱预测/粒子群优化(PSO)/时间序列预测/图神经网络/注意力机制Key words
inter-stellar spectrum prediction/particle swarm optimization(PSO)/time series prediction/graph neural network/attention mechanism分类
电子信息工程引用本文复制引用
满笑军,李华昱,牟伟清,郭兰图,张鹏,张贵临,冯诗惠..基于粒子群优化GCN-LSTM的星间频谱预测方法[J].电波科学学报,2025,40(5):846-854,9.基金项目
中国电波传播研究所稳定支持科研经费资助项目(A132305210) （A132305210）
