Design and Implementation of Radar Intelligent Recognition Simulation System for Aerospace Targets

doi:10.3969/j.issn.1009-086x.2024.02.017

Abstract

Abstract:

Training， testing， and evaluating radar intelligent recognition （RIR） methods usually require the construction of large datasets. Acquiring effective datasets efficiently is an urgent issue to be solved. The application scenarios of the RIR system for aerospace targets were analyzed first， and the construction idea of the RIR simulation system （RIRSS） for aerospace targets was given according to application demands. Then， the modular and component-based design methods were utilized to design the RIRSS framework for aerospace targets， and an intelligent recognition simulation system for aerospace targets was constructed， which could simulate the electromagnetic characteristics and dynamic echo of aerospace targets quickly and efficiently， with a scalable and reconfigurable capability. Simulation results and preliminary application show that this system can be used in the test and evaluation of the RIR method for aerospace targets in various scenarios.

Key words: aerospace target, intelligent recognition, target characteristics, radar echo simulation, component

摘要：

雷达智能识别方法训练、测试与评估通常需要构建大样本的数据集，如何快速获得有效的数据集是亟待解决的问题。分析了空天目标雷达智能识别系统应用场景，并结合应用需求给出了空天目标雷达智能识别仿真系统建设思路；采用组件化、模块化设计方法，设计空天目标雷达智能识别仿真系统框架，构建空天目标智能识别仿真系统，快速准确模拟空天目标电磁特性和动态回波，具有可扩展、可重构能力。仿真结果和初步应用表明，该系统可用于不同应用场景下空天目标雷达智能识别方法的测试与评估。

关键词: 空天目标, 智能识别, 目标特性, 雷达回波仿真, 组件化

CLC Number:

TN955

Xiaofeng AI, Jing WU, Jingke ZHANG, Yiqi ZHU, Zhiming XU, Qihua WU. Design and Implementation of Radar Intelligent Recognition Simulation System for Aerospace Targets[J]. Modern Defense Technology, 2024, 52(2): 151-162.

艾小锋, 吴静, 张静克, 朱义奇, 徐志明, 吴其华. 空天目标雷达智能识别仿真系统设计与实现[J]. 现代防御技术, 2024, 52(2): 151-162.

Figures/Tables 21

Fig. 1 Overall scheme of RIRSS for aerospace targets

Fig. 2 Overall structure of RIRSS for aerospace targets

Fig. 3 Processing flow of RIRSS for aerospace targets

Fig. 4 Component library

Fig. 5 Component construction process

Fig. 6 Electromagnetic calculation model

Fig. 7 Point scattering model of aircraft

Fig. 8 Simulation flow of fully polarized echo

Fig. 9 Linear frequency modulation baseband signal

Fig. 10 Phase encoding baseband signal

Fig. 11 RCS verification

Fig. 12 Imaging results generated by scattering center model

Fig. 13 Imaging results generated by electromagnetic calculation data

Fig. 14 Imaging results of stealth aircraft

Fig. 15 Simulation verification process

Table 1 Simulation dataset parameters

参数	目标编号
参数	1	2	3
高度/m	1.600	2.590	0.610
底半径/m	0.400	0.563	0.150
进动角/（°）	2~8
进动频率/Hz	1~3
章动角/（°）	1~4
章动频率/Hz	1~3
带宽/GHz	1
频点数目	110

Fig. 16 Feature vector v

Fig. 17 Optimization results of RBF-SVM feature vectors

Fig. 18 Feature optimization of AGR-SVM

Fig. 19 ISAR feature optimization structure of AGR-SVM

Fig. 20 Recognition verification of measurement data

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