基于降维变换的低复杂度双基地EMVS-MIMO雷达高分辨多维参数估计

doi:10.12305/j.issn.1001-506X.2024.06.08

系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (6): 1899-1907.doi: 10.12305/j.issn.1001-506X.2024.06.08

• 传感器与信号处理 • 上一篇

基于降维变换的低复杂度双基地EMVS-MIMO雷达高分辨多维参数估计

谢前朋¹, 杜奕航², 孙兵³, 闫华¹, 潘小义⁴^,*, 赵锋⁴

1. 中国人民解放军95913部队, 辽宁沈阳 110041
2. 国防科技大学第六十三研究所, 江苏南京 210007
3. 中国卫星海上测控部, 江苏江阴 214430
4. 国防科技大学电子信息系统复杂电磁环境效应国家重点实验室, 湖南长沙 410073

收稿日期:2022-03-29 出版日期:2024-05-25 发布日期:2024-06-04
通讯作者: 潘小义
作者简介:谢前朋 (1991—), 男, 工程师, 博士, 主要研究方向为阵列信号处理、雷达信号处理
杜奕航 (1991—), 男, 高级工程师, 博士, 主要研究方向为信号处理
孙兵 (1991—), 男, 工程师, 博士, 主要研究方向为阵列信号处理、雷达信号处理
闫华 (1973—), 女, 高级工程师, 硕士, 主要研究方向为通信信号处理
潘小义 (1986—), 男, 副教授, 博士, 主要研究方向为ISAR成像、雷达信号处理、电子对抗
赵锋 (1978—), 男, 教授, 博士, 主要研究方向为电子信息系统仿真建模评估
基金资助:
国家自然科学基金(61890545);国家自然科学基金(61890542);国家自然科学基金(61890540)

High resolution multidimensional parameter estimation for low complexity bistatic EMVS-MIMO radar based on reduced-dimensional transformation

Qianpeng XIE¹, Yihang DU², Bing SUN³, Hua YAN¹, Xiaoyi PAN⁴^,*, Feng ZHAO⁴

1. Unit 95913 of the PLA, Shenyang 110041, China
2. The Sixty-Third Research Institute, National University of Defense Technology, Nanjing 210007, China
3. China Satellite Maritime Tracking and Control Department, Jiangyin 214430, China
4. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China

Received:2022-03-29 Online:2024-05-25 Published:2024-06-04
Contact: Xiaoyi PAN

摘要/Abstract

摘要：

针对当前算法在实现双基地电磁矢量传感器多输入多输出(electromagnetic vector sensors multiple input multiple output, EMVS-MIMO)雷达的多维参数估计时计算代价较高的问题, 通过利用降维变换技术来实现低复杂度的角度参数和极化参数求解。针对阵列接收数据维度较大问题, 通过设计相应的波束空间变换矩阵来实现对阵列接收数据的降维处理。针对算法本身的较高计算复杂度问题, 采用低计算复杂度的平行因子分解算法。所提算法能够精确地实现对发射因子矩阵和接收因子矩阵的求解。同时, 通过新的旋转不变关系构建新的估计信号参数, 可以实现对发射/接收俯仰角的求解。进一步, 发射/接收方位角、发射/接收极化角和发射/接收极化相位差的估计可以通过发射/接收空间响应矩阵的重构来实现。仿真实验结果表明, 所提算法在降低计算复杂度的同时能够保持优越的多维参数估计性能。

关键词: 双基地电磁矢量传感器多输入多输出(electromagnetic vector sensors multiple input multiple output, EMVS-MIMO)雷达, 多维参数估计, 波束空间变换, 平行因子分解算法

Abstract:

To solve the high computational cost problem of the current algorithm to achieve multi-dimensional parameter estimation of bistatic electromagnetic vector sensors multiple input multiple output (EMVS MIMO) radar, the dimensionality-reduction transformation technology is utilized to achieve low complexity angle and polarization parameter solutions. To address the issue of large dimensionality in array received data, a corresponding beamspace transformation matrix is designed to achieve dimensionality-reduction processing of array received data. To address the high computational complexity of the algorithm itself, a parallel factor decomposition algorithm with low computational complexity is adopted. The proposed algorithm can accurately solve the transmission factor matrix and reception factor matrix. Moreover, by constructing a new estimating signal parameter via rotational invariance techniques (ESPRIT) relationship, the solution of the transmit/receive pitch angle can be achieved. Furthermore, the estimation of the transmit/receive azimuth angle, transmit/receive polarization angle and transmit/receive polarization phase difference can be achieved through the reconstruction of the transmit/receive spatial response matrix. Simulation experiments show that the proposed algorithm can maintain superior multidimensional parameter estimation performance while reducing computational complexity.

Key words: bistatic electromagnetic vector sensors multiple input multiple output (EMVS-MIMO) radar, multi-dimensional parameter estimation, beamspace spatial transformation, parallel factor decomposition algorithm

中图分类号:

TN958

谢前朋, 杜奕航, 孙兵, 闫华, 潘小义, 赵锋. 基于降维变换的低复杂度双基地EMVS-MIMO雷达高分辨多维参数估计[J]. 系统工程与电子技术, 2024, 46(6): 1899-1907.

Qianpeng XIE, Yihang DU, Bing SUN, Hua YAN, Xiaoyi PAN, Feng ZHAO. High resolution multidimensional parameter estimation for low complexity bistatic EMVS-MIMO radar based on reduced-dimensional transformation[J]. Systems Engineering and Electronics, 2024, 46(6): 1899-1907.

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参考文献 24

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算法类型	计算复杂度
ESPRIT算法^[9]	ο{(6M)²(6N)²L+(6M)³(6N)³+2K²6(N+M－2)+6K³+7(M+N)K²+12K+36MN(36MN－K)+(36MN－K)K²}
PM算法^[10]	ο{(6M)²(6N)²L+72MNK²+2K²6(N+M－2)+6K³+7(M+N)K²+12K+36MN(36MN－K)+(36MN－K)K²}
Tensor子空间算法^[13]	ο{(6M)²(6N)²L+4(6M)³(6N)³+2K²6(N+M－2)+6K³+7(M+N)K²+12K+36MN(36MN－K)+(36MN－K)K²}
PARAFAC算法^[12]	ο{κ(3K³+108MNKL+3K²(36MN+6NL+6ML))+2K²6(N+M－2)+6K³+7(M+N)K²+12K}
本文所提算法	ο{κ(3K³+108${\rm{\bar M}}$${\rm{\bar N}}$KL+3K²(36${\bar M}$${\bar N}$+6${\rm{\bar N}}$L+6${\rm{\bar M}}$L))+2K²6(${\bar N}$+${\bar M}$－2)+6K³+7(${\bar M}$+${\bar N}$)K²+12K}

目标	俯仰角θ	方位角ϕ	极化角γ	极化相位差η
1	40, 24	15, 21	10, 42	38, 17
2	20, 38	25, 32	22, 33	48, 27
3	30, 16	35, 55	45, 60	56, 39

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基于降维变换的低复杂度双基地EMVS-MIMO雷达高分辨多维参数估计

High resolution multidimensional parameter estimation for low complexity bistatic EMVS-MIMO radar based on reduced-dimensional transformation

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