Intelligent Passive Detection of Aerial Target in Space-Air-Ground Integrated Networks

作     者：Mingqian Liu Chunheng Liu Ming Li Yunfei Chen Shifei Zheng Nan Zhao Mingqian Liu;Chunheng Liu;Ming Li;Yunfei Chen;Shifei Zheng;Nan Zhao

作者机构：State Key Laboratory of Integrated Service NetworksXidian UniversityShaanxiXi’an 710071China Guilin Changhai Development Co.LtdGuilin 541001China School of EngineeringUniversity of WarwickCoventry CV47ALU.K. School of Information and Communication EngineeringDalian University of TechnologyDalian 116024China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2022年第19卷第1期

页      面：52-63页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 

基　　金：supported by the National Natural Science Foundation of China under Grant 62071364 in part by the Aeronautical Science Foundation of China under Grant 2020Z073081001 in part by the Fundamental Research Funds for the Central Universities under Grant JB210104 in part by the Shaanxi Provincial Key Research and Development Program under Grant 2019GY-043 in part by the 111 Project under Grant B08038。 

主　　题：aerial target detection decoupling echo state networks delayed feedback networks multilayer perceptron satellite illuminator space-air-ground integrated networks 

摘      要：Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks(SAGIN). In this paper, we propose a novel intelligent passive detection method for aerial target based on reservoir computing networks. Specifically, delayed feedback networks are utilized to refine the direct signals from the satellite in the reference channels. In addition, the satellite direct wave interference in the monitoring channels adopts adaptive interference suppression using the minimum mean square error filter. Furthermore, we employ decoupling echo state networks to predict the clutter interference in the monitoring channels and construct the detection statistics accordingly. Finally, a multilayer perceptron is adopted to detect the echo signal after interference suppression. Extensive simulations is conducted to evaluate the performance of our proposed method. Results show that the detection probability is almost 100% when the signal-to-interference ratio of echo signal is-36 dB, which demonstrates that our proposed method achieves efficient passive detection for aerial targets in typical SAGIN scenarios.