一种GTD模型参数估计的改进2D-TLS-ESPRIT算法

doi:10.13700/j.bh.1001-5965.2019.0556

北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (10): 1982-1989.doi: 10.13700/j.bh.1001-5965.2019.0556

一种GTD模型参数估计的改进2D-TLS-ESPRIT算法

郑舒予¹, 张小宽², 郭艺夺², 宗彬锋², 徐嘉华¹

1. 空军工程大学研究生院, 西安 710051;
2. 空军工程大学防空反导学院, 西安 710051

收稿日期:2019-10-29 发布日期:2020-10-29
通讯作者: 张小宽 E-mail:15529037989@163.com
作者简介:郑舒予男,硕士研究生。主要研究方向:散射中心提取、目标探测与识别;张小宽男,教授,硕士生导师。主要研究方向:目标探测与识别、雷达目标电磁散射特性研究;郭艺夺男,博士,讲师。主要研究方向:MIMO雷达信号处理、阵列信号处理;宗彬锋男,博士,讲师。主要研究方向:天线与电磁波传输;徐嘉华男,硕士研究生。主要研究方向:目标探测与识别。
基金资助:
国家自然科学基金（61372033）

An improved 2D-TLS-ESPRIT algorithm of GTD model parameter estimation

ZHENG Shuyu¹, ZHANG Xiaokuan², GUO Yiduo², ZONG Binfeng², XU Jiahua¹

1. Graduate School, Air Force Engineering University, Xi'an 710051, China;
2. Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China

Received:2019-10-29 Published:2020-10-29
Supported by:
National Natural Science Foundation of China (61372033)

摘要/Abstract

摘要： 针对经典二维总体最小二乘法旋转不变子空间（2D-TLS-ESPRIT）算法估计二维几何绕射理论（GTD）模型参数精度不高、抗噪性能较差这一问题，提出了一种改进2D-TLS-ESPRIT算法。首先，改进算法通过将目标的极化散射矩阵加入到二维GTD散射中心模型，使得模型对目标极化散射特征的描述更加精准；其次，构建置换矩阵得到原始回波矩阵的共轭矩阵，并将两者结合起来，从而延长了目标电磁散射数据的长度；最后，仿真结果验证了改进算法的参数估计性能与噪声鲁棒性均要优于同类已有算法，雷达散射截面积（RCS）外推结果进一步验证了改进算法参数估计性能的先进性。

关键词: 参数估计, 几何绕射理论(GTD)散射中心模型, 极化散射矩阵, 雷达散射截面(RCS)外推, 共轭矩阵

Abstract: The parameter estimation and noise robustness ability of classical Two-Dimensional Total-Least-Square Estimating Signal Parameter via Rotational Invariance Techniques (2D-TLS-ESPRIT) algorithm are not effective when extracting parameters of the two-dimensional Geometric Theory of Diffraction (GTD) model. To solve this problem, an improved 2D-TLS-ESPRIT algorithm is proposed in this paper. Firstly, polarization scattering matrix is added into the two-dimensional GTD model and hence the full-polarization scattering center model can be obtained. Secondly, the covariance matrix of the original echo matrix can be achieved by constructing a permutation matrix. The length of electromagnetic scattering data can be added by combing these two matrices. Finally, the simulation results prove that parameter estimation performance and noise robustness ability of the improved algorithm are better than those of the same kind of algorithms. The Radar Cross Section (RCS) extrapolation results also validate the superiority of the improved algorithm in parameter estimation performance.

Key words: parameter estimation, Geometric Theory of Diffraction (GTD) scattering center model, polarization scattering matrix, Radar Cross Section (RCS) extrapolation, permutation matrix

中图分类号:

TN957

郑舒予, 张小宽, 郭艺夺, 宗彬锋, 徐嘉华. 一种GTD模型参数估计的改进2D-TLS-ESPRIT算法[J]. 北京航空航天大学学报, 2020, 46(10): 1982-1989.

ZHENG Shuyu, ZHANG Xiaokuan, GUO Yiduo, ZONG Binfeng, XU Jiahua. An improved 2D-TLS-ESPRIT algorithm of GTD model parameter estimation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(10): 1982-1989.

参考文献

[1] 许小剑.雷达目标散射特性测量与处理新技术[M].北京:国防工业出版社,2018.XV X J.New techniques for radar target scattering signature measurement and processing[M].Beijing:National Defense Industrial Press,2018(in Chinese).
[2] HE X Y,TONG N N,HU X W,et al.High-resolution imaging and 3D reconstruction of precision targets by exploiting sparse apertures[J].IEEE Transactions on Aerospace and Electronic System,2017,53(3):1212-1220.
[3] FAN J H,ANDREW T.Target reconstruction based on attributed scattering centers with application to robust SAR ATR[J].Remote Sensing,2018,10(4):655-673.
[4] SUN Y G,DU L,WANG Y,et al.SAR automatic target recognition based on dictionary learning and joint dynamic sparse representation[J].IEEE Geoscience and Remote Sensing Letters,2016,13(12):1777-1781.
[5] DONG G G,KUANG G Y.SAR target recognition via representation of monogenic signal on Grassmann manifolds[J].IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing,2016,9(3):1308-1319.
[6] PEI J F,HUANG Y L,HUO W B,et al.SAR automatic target recognition based on multiview deep learning framework[J].IEEE Geoscience and Remote Sensing Letters,2018,56(4):2196-2210.
[7] 郭琨毅,王嘉欣,张尊,等.基于散射中心方位特性的大角度成像数据缩减方法[J].电波科学学报,2019,34(1):91-96.GUO K Y,WANG J X,ZHANG Z,et al.Wide-angle imaging simplified method based on the scattering center aspect dependences[J].Chinese Journal of Radio Science,2019,34(1):91-96(in Chinese).
[8] 郑舒予,张小宽,刘铭,等.基于一维散射中心模型的RCS频率全角度外推[J].空军工程大学(自然科学版),2019,20(6):79-83.ZHENG S Y,ZHANG X K,LIU M,et al.RCS frequency full extraction based on one-dimensional scattering center model[J].Journal of Air Force Engineering University (Natural Science Edition),2019,20(6):79-83(in Chinese).
[9] 郑舒予,张小宽,宗彬锋.基于改进MUSIC算法的散射中心参数提取及RCS重构[J].系统工程与电子技术,2020,42(1):76-82.ZHENG S Y,ZHANG X K,ZONG B F.Extraction of scattering center parameters and reconstruction of RCS based on improved MUSIC algorithm[J].Systems Engineering and Electronics,2020,42(1):76-82(in Chinese).
[10] 贺治华,黎湘,张旭峰,等.基于MUSIC算法的GTD模型参数估计[J].系统工程与电子技术,2005,27(10):1685-1688.HE Z H,LI X,ZHANG X F,et al.MUSIC-based parametric estimation of GTD model[J].Systems Engineering and Electronics,2005,27(10):1685-1688(in Chinese).
[11] CHEN X,WAN B Q,ZHAO T,et al.Parameter estimation of three-dimensional scattering centers based on state space and ESPRIT method[J].Procedia Computer Science,2019,147:435-440.
[12] ZHENG S Y,ZHANG X K,ZONG B F,et al.GTD model parameters estimation based on improved LS-ESPRIT algorithm[C]//2019 Photonics & Electromagnetics Research Symposium,2019:19534708.
[13] 王菁,周建江,汪飞.基于GTD模型的目标二维散射中心提取[J].电子与信息学报,2009,31(4):958-962.WANG J,ZHOU J J,WANG F.2D-scattering centers extraction of objects based on GTD model[J].Journal of Electronics & Information Technology,2009,31(4):958-962(in Chinese).
[14] HUA Y B.Estimating two-dimensional frequencies by matrix enhancement and matrix pencil[J].IEEE Transactions on Signal Processing,1992,40(9):2267-2280.
[15] POTTER L C,CHIANG D M,CARRIERE R.A GTD-based parametric model for radar scattering[J].IEEE Transaction on Antennas and Propagation,1995,43(10):1058-1066.
[16] ROUQUETTE S,NAJIM M.Estimation of frequencies and damping factors by two-dimensional ESPRIT type methods[J].IEEE Transactions on Signal Processing,2001,49(1):237-245.
[17] 张贤达.现代信号处理[M].北京:清华大学出版社,2002:56-60.ZHANG X D.Modern signal processing[M].Beijing:Tsinghua University Press,2002:56-60(in Chinese).

一种GTD模型参数估计的改进2D-TLS-ESPRIT算法

An improved 2D-TLS-ESPRIT algorithm of GTD model parameter estimation

RichHTML

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]	宋耀辉, 黄仰超, 张衡阳, 秦智康, 高维廷. 基于FRFT的多分量LFM信号检测与参数估计方法[J]. 北京航空航天大学学报, 2020, 46(6): 1221-1228.
[2]	陈尔康, 荆武兴, 高长生. 弹性高速飞行器的状态/参数滚动时域估计[J]. 北京航空航天大学学报, 2019, 45(2): 291-298.
[3]	刘科生, 王思洋. 含函数型自变量回归模型中的变量选择[J]. 北京航空航天大学学报, 2019, 45(10): 1990-1994.
[4]	雷梦颖, 魏彦兆, 李阳, 王丽娜. 稀疏多小波时变系统辨识及脑电信号时频分析[J]. 北京航空航天大学学报, 2018, 44(6): 1312-1320.
[5]	葛承垄, 朱元昌, 邸彦强. 面向装备RUL预测的平行仿真技术[J]. 北京航空航天大学学报, 2018, 44(4): 725-736.
[6]	谌炳汉, 左峥嵘, 夏鲁瑞. 单平台成像目标运动特性反演方法[J]. 北京航空航天大学学报, 2018, 44(12): 2528-2535.
[7]	王鑫, 张晓林, 曹晏波. 一种基于迭代的多音干扰消除方案[J]. 北京航空航天大学学报, 2017, 43(3): 573-582.
[8]	郑明国, 吴成东, 陈东岳, 贾同, 金勇虎. 基于非参数边缘积分估计的局部特征描述子[J]. 北京航空航天大学学报, 2016, 42(3): 497-505.
[9]	王晓红, 李宇翔, 余闯, 王立志. 基于β似然函数的参数估计方法[J]. 北京航空航天大学学报, 2016, 42(1): 41-46.
[10]	王海泉, 马伟建, 顾娟娟, 刘丹, 王亚. 基于ICT分布的容迟网络接触模型[J]. 北京航空航天大学学报, 2015, 41(8): 1422-1429.
[11]	李强, 王中宇, 王卓然, 燕虎. 压力传感器激波管校准条件下的动态参数估计[J]. 北京航空航天大学学报, 2015, 41(7): 1223-1230.
[12]	张洪礼, 罗钦钦, 韩潮. UKF参数估计在三体Lambert问题中的应用[J]. 北京航空航天大学学报, 2015, 41(2): 228-233.
[13]	梁子璇, 任章. 基于在线气动参数修正的预测制导方法[J]. 北京航空航天大学学报, 2013, 39(7): 853-857.
[14]	马纪明, 万蔚, 王法岩. 基于模型诊断技术的神经网络实现方法[J]. 北京航空航天大学学报, 2013, 39(2): 178-183.
[15]	和丽清, 孙先仿, 邱红专. 非线性系统的结构选择及其参数的集员辨识[J]. 北京航空航天大学学报, 2010, 36(10): 1189-1193.