基于混合体制雷达网的弹道目标微特征及外形参数提取

doi:10.7527/S1000-6893.2016.0054

电子与控制

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

基于混合体制雷达网的弹道目标微特征及外形参数提取

李靖卿^1,2, 冯存前¹, 孙宏伟², 贺思三¹

1. 空军工程大学防空反导学院, 西安 710051;
2. 中国人民解放军 93764部队, 包头 075000

收稿日期:2015-07-13 修回日期:2016-02-29 出版日期:2016-06-15 发布日期:2016-03-04
通讯作者: 冯存前男,博士,教授,博士生导师。主要研究方向:雷达信号处理、雷达电子战新技术。Tel:029-84789131 E-mail:fengcunqian@sina.com E-mail:fengcunqian@sina.com
作者简介:李靖卿男,硕士研究生。主要研究方向:雷达信号处理。Tel:029-84789420 E-mail:lijingqing_1025@126.com;孙宏伟男,博士,高级工程师。主要研究方向:雷达系统、电子对抗技术。E-mail:liuyanhui1999@126.com;贺思三男,博士,讲师。主要研究方向:雷达信号处理、复杂运动目标成像。E-mail:hesisan@163.com
基金资助:
国家自然科学基金(61372166,61501495);陕西省自然科学基础研究计划资助项目(2014JM8308)

Micro-motion feature and shape parameters extraction based on hybrid-scheme radar network for ballistic targets

LI Jingqing^1,2, FENG Cunqian¹, SUN Hongwei², HE Sisan¹

1. Air and Missile Defense College, Air Fore Engineering University, Xi'an 710051, China;
2. No. 93764 Unit, People's Liberation Army of China, Baotou 075000, China

Received:2015-07-13 Revised:2016-02-29 Online:2016-06-15 Published:2016-03-04
Supported by:
National Natural Science Foundation of China ( 61372166, 61501495): The Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (2014JM8308)

摘要/Abstract

摘要：

针对弹道中段目标微特征难以识别与分辨的问题,提出了一种基于低分辨雷达和高分辨雷达相结合的混合体制雷达网的有翼弹道目标微特征及外形参数提取方法。依据非线性信号参量可分离模型,利用非线性最小二乘估计方法解算出有翼弹道目标群各散射中心的幅相参数,结合不同雷达提取的微特征的关联性,利用散射中心关联处理实现了各类散射中心的分离。在此基础上,利用弹道目标的微特征,结合弹道目标各散射中心的相对位置关系,重构出各目标的三维微特征及各散射中心的三维位置矢量,进而估计出目标的进动特征和结构参数。仿真结果表明:当信噪比(SNR)为5 dB时,该方法的重构精度保持在92%左右。

关键词: 微特征, 混合体制雷达网, 匹配, 特征提取, 弹道目标

Abstract:

Aiming at the complexity of recognition and resolution on ballistic mid-course target, a method for three-dimensional reconstruction of ballistic target based on hybrid-scheme radar network combined low-resolution radar with high-resolution radar is proposed. Based on the separable model of nonlinear signal parameter, the amplitude-phase parameters of each scattering center on the ballistic target group with empennages are calculated by nonlinear least squares estimation method. Combined with the relationship of micro-motion features between radars, various scattering centers are separated by association processing between scattering centers. Ultimately, the three-Dimensional micro-motion features and the three-Dimensional position vectors are reconstructed by utilizing both the micro-Doppler characteristics and the relative position relation of each scattering center of ballistic target, and then the precession feature and structural parameters are estimated. Simulation results validate that the reconstruction precision of three-dimensional features has been maintained at about 92% when the signal noise ratio (SNR) is 5 dB.

Key words: micro-motion feature, hybrid-scheme radar network, matching, feature extraction, ballistic target

中图分类号:

V243.2
TN953

李靖卿, 冯存前, 孙宏伟, 贺思三. 基于混合体制雷达网的弹道目标微特征及外形参数提取[J]. 航空学报, 2016, 37(6): 1963-1973.

LI Jingqing, FENG Cunqian, SUN Hongwei, HE Sisan. Micro-motion feature and shape parameters extraction based on hybrid-scheme radar network for ballistic targets[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(6): 1963-1973.

参考文献

[1] CHEN V C. Advances in applications of radar micro-Doppler signatures[C]//Proceeding of IEE Antenna Measurements & Application, 2014:1-4.
[2] LIU Z, WEI X Z, LI X. Aliasing-free micro-Doppler analysis based on short-time compressed sensing[J]. IET Signal Processing, 2014, 8(2):176-187.
[3] CHEN V C. The micro-Doppler effect in radar[M]. London:Artech House, 2011:24-33.
[4] SMITH G E, WOODBRIDGE K, BAKER C J, et al. Multistatic micro-Doppler radar signatures of personnel targets[J]. IET Signal Processing, 2010, 4(3):224-233.
[5] VESPE M, BAKER C, GRIFFITHS H. Radar target classification using multiple perspectives[J]. IET Radar, Sonar & Navigation, 2007, 1(4):300-307.
[6] 韩勋, 杜兰, 刘宏伟. 基于窄带雷达组网的空间锥体目标特征提取方法[J]. 电子与信息学报, 2014, 36(12):2956-2962. HAN X, DU L, LIU H W. Feature extraction of space cone-shaped target based on narrow-band radar network[J]. Journal of Electronics & Information Technology, 2014, 36(12):2956-2962(in Chinese).
[7] 向道朴. 微多普勒回波模拟与微动特征提取技术研究[D]. 长沙:国防科学技术大学, 2010:95-111. XIANG D P. Research on micro-Doppler echo simulation and micro-motion signature extraction technology[D]. Changsha:National University of Defense Technology, 2010:95-111(in Chinese).
[8] 张栋, 冯存前, 贺思三, 等. 组网雷达弹道目标三维进动特征提取[J]. 西安电子科技大学学报, 2015, 42(2):146-151. ZHANG D, FENG C Q, HE S S, et al. Extraction of three-dimensional precession features of ballistic targets in netted radar[J]. Journal of Xidian University, 2015, 42(2):146-151(in Chinese).
[9] LUO Y, ZHANG Q, YUAN N, et al. Three-dimensional precession feature extraction of space targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2):1313-1329.
[10] AI X F, HUANG Y, ZHAO F, et al. Imaging of spinning targets via narrow-band T/R-R bistatic radars[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(2):362-366.
[11] PAN X Y, WANG W, LIU J, et al. Modulation effect and inverse synthetic aperture radar imaging of rotationally symmetric ballistic targets with precession[J]. IET Radar, Sonar & Navigation, 2013, 7(9):950-958.
[12] CAMP W W, JOSEPH T M, O' DONNELL R M. Wideband radar for ballistic missile defense and range-Doppler imaging of satellites[J]. Lincoln Laboratory Journal, 2000, 2(2):267-268.
[13] GUO K Y, SHENG X Q. Precise recognition of warhead and decoy based on components of micro-Doppler frequency curves[J]. Science China Information Sciences, 2012, 55(4):850-856.
[14] BARHAT M. Signal detection and estimation[M]. London:Artech House, 2005:576-580.
[15] WANG J, LEI P, SUN J P, et al. Spectral characteristics of mixed micro-Doppler time-frequency data sequences in micro-motion and inertial parameter estimation of radar targets[J]. IET Radar, Sonar & Navigation, 2014, 8(4):275-281.
[16] SHAO C Y, DU L, HAN X. Multiple target tracking based separation of micro-Doppler signals from coning target[C]//Proceeding of IEEE Radar Conference. Piscataway, NJ:IEEE Press, 2014:130-133.
[17] 李靖卿, 冯存前, 张栋. 基于自适应视野聚类匹配的多目标分离与提取[J]. 系统工程与电子技术, 2015, 37(9):1974-1979. LI J Q, FENG C Q, ZHANG D. Multi-target separation and extraction based on adaptive vision cluster matching[J]. Systems Engineering and Electronics, 2015, 37(9):1974-1979(in Chinese).
[18] 张群, 罗迎. 雷达目标微多普勒效应[M]. 北京:国防工业出版社, 2013:22-81. ZHANG Q, LUO Y. Micro-Doppler effect of radar targets[M]. Beijing:National Defense Industry Press, 2013:22-81(in Chinese).
[19] ALEX R, ALESSANDRO L. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6):1492-1496.
[20] 李孝辉, 杨旭海, 刘娅, 等. 时间频率信号的精密测量[M]. 北京:科学出版社, 2010:162-180. LI X H, YANG X H, LIU Y, et al. Precise measure of time-frequency signature[M]. Beijing:Science Press, 2010:162-180(in Chinese).
[21] OSSAMA A, AHMED G. Dynamic-size multiple populations genetic algorithm for multigravity-assist trajectory optimization[J]. Journal of Guidance, Control, and Dynamics, 2012, 35(2):520-529.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于混合体制雷达网的弹道目标微特征及外形参数提取

Micro-motion feature and shape parameters extraction based on hybrid-scheme radar network for ballistic targets

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	刘雷, 刘大卫, 王晓光, 陈俊男, 刘东兴. 无人机集群与反无人机集群发展现状及展望[J]. 航空学报, 2022, 43(S1): 726908-726908.
[2]	钟梦帆, 裴继红. 局部特征点对称约束的图像拼接增强方法[J]. 航空学报, 2022, 43(S1): 726948-726948.
[3]	张烁, 刘治汶. 航空发动机轴承故障结构化贝叶斯稀疏表示[J]. 航空学报, 2022, 43(9): 625056-625056.
[4]	韩淞宇, 邵海东, 姜洪开, 张笑阳. 基于提升卷积神经网络的航空发动机高速轴承智能故障诊断[J]. 航空学报, 2022, 43(9): 625479-625479.
[5]	杨特, 杨智春, 梁舒雅, 康在飞, 贾有. 平稳随机载荷的信号特征提取与深度神经网络识别[J]. 航空学报, 2022, 43(9): 225952-225952.
[6]	郑覃, 杨小贺, 叶俊, 冯锦璋. 内涵工况对风扇增压级双涵匹配的影响[J]. 航空学报, 2022, 43(7): 125477-125477.
[7]	陆传雨, 陆铭慧, 刘浩宇, 徐喜浩. 基于改进式SL0算法的超声混叠信号分离[J]. 航空学报, 2022, 43(7): 425419-425419.
[8]	李新凯, 张宏立, 范文慧. 非匹配扰动下变体无人机预设性能控制[J]. 航空学报, 2022, 43(2): 325008-325008.
[9]	战庆亮, 白春锦, 张宁, 葛耀君. 基于时程卷积自编码的机翼绕流特征识别方法[J]. 航空学报, 2022, 43(11): 526531-526531.
[10]	陈呈, 赵丹, 王岳青, 邓亮, 杨超, 苏铖宇, 王昉. NNW-TopViz流场可视分析系统[J]. 航空学报, 2021, 42(9): 625747-625747.
[11]	王怡星, 韩仁坤, 刘子扬, 张扬, 陈刚. 流体力学深度学习建模技术研究进展[J]. 航空学报, 2021, 42(4): 524779-524779.
[12]	张伟伟, 寇家庆, 刘溢浪. 智能赋能流体力学展望[J]. 航空学报, 2021, 42(4): 524689-524689.
[13]	张凯, 王凯迪, 杨曦, 李少毅, 王晓田. 基于DNET的空中红外目标抗干扰识别算法[J]. 航空学报, 2021, 42(2): 324223-324223.
[14]	王洛烽, 陈仁良. 重型直升机飞行动力学刚弹耦合建模及空中共振稳定性分析[J]. 航空学报, 2021, 42(12): 124634-124634.
[15]	朱大虎, 徐小虎, 蒋诚, 李文龙. 复杂叶片机器人磨抛加工工艺技术研究进展[J]. 航空学报, 2021, 42(10): 524265-524265.