航空科学技术2025，Vol.36Issue(6)：40-48,9.DOI:10.19452/j.issn1007-5453.2025.06.005
基于空中目标特征模的RCS序列预测方法研究
Research on RCS Sequence Prediction Method Based on Air Target's Characteristic Mode
摘要
Abstract
With the rapid advancement of radar technology,there is an increasing demand for enhanced target stealth performance and precise radar detection.Consequently,the accurate and swift prediction of radar cross section(RCS)sequences has emerged as a critical focus in aerospace and civil applications.This paper presents a method for efficiently predicting the RCS sequence of airborne targets using their intrinsic characteristic modes.By reconstructing the intrinsic characteristic mode,this paper rapidly derive the electromagnetic response of the target under arbitrary excitation,ensuring the accuracy of the calculated RCS sequences.The linear reconstruction of the RCS sequence relies on parameters such as the target's flight position and attitude angle,allowing us to transform the prediction of the RCS over a specified period into a prediction of its positional and motion parameters during that interval.This paper evaluate the predictive capabilities of bidirectional long short-term memory(LSTM),unidirectional LSTM,and backpropagation(BP)neural networks.Experimental results indicate that the bidirectional LSTM outperforms the other two models in time series prediction tasks.Finally,utilizing both predicted and actual motion parameters,this paper efficiently and accurately calculate the target's RCS sequences through linear reconstruction of the modal current and electric field derived from the target's characteristic mode.The rapid prediction of aerial target RCS sequences can establish an extensive and reliable database for space object identification and surveillance,demonstrating substantial practical significance for RCS sequence-based space target recognition.关键词
RCS/特征模/线性重构/LSTM/特征电流Key words
RCS/characteristic mode/linear reconstruction/LSTM/modal current分类
电子信息工程引用本文复制引用
谷继红,石家敏,王照源,康婕,丁大志..基于空中目标特征模的RCS序列预测方法研究[J].航空科学技术,2025,36(6):40-48,9.基金项目
国家自然科学基金(62301258) （62301258）
航空科学基金(20230027059001) （20230027059001）
江苏省基础研究计划自然科学基金(BK20230918) National Natural Science Foundation of China(62301258) （BK20230918）
Aeronautical Science Foundation of China(20230027059001) （20230027059001）
Jiangsu Provincial Basic Research Program Natural Science Foundation(BK20230918) （BK20230918）
