分布式MIMO雷达子空间干扰背景下双层非均匀多秩目标检测方法

王勇; 许姗姗; 刘维建; 曹秋生; 田晗

doi:10.12000/JR25008

分布式MIMO雷达子空间干扰背景下双层非均匀多秩目标检测方法

DOI: 10.12000/JR25008 CSTR: 32380.14.JR25008

1.
西安电子科技大学西安 710071
2.
空军预警学院武汉 430019
3.
中国电子科技集团第二十七研究所郑州 450000

基金项目: 国家自然科学基金(62371379, 62471485, 62071482)

详细信息

作者简介:
王　勇，博士生，主要研究方向为雷达系统、信息处理

许姗姗，硕士生，主要研究方向为雷达目标检测、雷达信号处理

刘维建，副教授，主要研究方向为雷达目标自适应检测、阵列信号处理

曹秋生，研究员，主要研究方向为无人系统

田　晗，博士生，主要研究方向为雷达系统、信息处理

通讯作者:
许姗姗 23021211650@stu.xidian.edu.cn

责任主编：刘军 Corresponding Editor: LIU Jun
中图分类号: TN957.51
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- 被引次数: 0
出版历程
- 收稿日期: 2025-01-06
- 修回日期: 2025-04-30
- 网络出版日期: 2025-06-11

Double Hierarchical Nonhomogeneous Multirank Target Detection Method for Distributed MIMO Radars in Subspace Interference Scenarios

1.
Xidian University, Xi’an 710071, China
2.
Air Force Early Warning Academy, Wuhan 430019, China
3.
The 27th Research Institute of China Electronics Technology Group, Zhengzhou 450000, China

Funds: The National Natural Science Foundation of China (62371379, 62471485, 62071482)

More Information

Corresponding author: XU Shanshan 23021211650@stu.xidian.edu.cn

摘要

摘要: 针对分布式多输入多输出(MIMO)雷达在子空间干扰和非均匀杂波中检测目标场景，该文提出了一种面向分布式MIMO雷达双层非均匀多秩目标检测方法。首先，利用目标信号和干扰位于两个相互线性独立且秩大于 1 的子空间，两个子空间对应的子空间矩阵和相应距离单元的坐标向量都是未知的，建立了多秩目标模型及子空间干扰模型；然后，设计分布式MIMO雷达系统的双层非均匀结构，每个发射-接收对的干扰是非均匀的，即每个发射-接收对具备不同的统计量。此外，每一个发射接收对的杂波是非均匀的。在此基础上，通过采取Rao与Wald检验准则，构建待解参数估计策略，并通过功率中值归一化协方差估计，设计了面向分布式MIMO雷达子空间干扰背景下双层非均匀多秩目标Rao检测器和Wald检测器。最后，通过理论推导证明了所提检测方法相对于杂波协方差矩阵结构具有恒虚警特性。仿真实验结果表明，所提检测方法能够保证对杂波协方差矩阵结构具有恒虚警特性，此外，相较于现有分布式MIMO雷达检测方法，所提检测方法有效改善了目标检测性能和干扰抑制性能。
- 分布式MIMO雷达 /
- 双层非均匀 /
- 目标检测 /
- 子空间干扰 /
- 恒虚警特性
Abstract: The paper proposes a double hierarchical nonhomogeneous multirank target detection method for the distributed Multiple-Input Multiple-Output (MIMO) radar to detect targets in scenarios with subspace interference and nonhomogeneous clutter. First, a multirank target model and subspace interference model are established based on the fact that the target signal and interference are located in two linearly independent subspaces, each with a rank greater than 1. The corresponding subspace matrices of the two subspaces and the coordinate vectors of the corresponding distance units are unknown. Then, a distributed MIMO radar system with a double hierarchical nonhomogeneous structure is designed, and the interference of each transmit-receive pair is nonhomogeneous, i.e., each transmit-receive pair possesses different statistics. In addition, the clutter in one transmit-receive pair is nonhomogeneous. Subsequently, the double hierarchical nonhomogeneous multirank target Rao detector and Wald detector are designed for the distributed MIMO radar in the context of subspace interference. This is achieved by adopting the Rao and Wald test criteria, constructing the parameter estimation strategy to be solved, and estimating the power median normalized covariance. Theoretical derivation showed that the proposed method had a constant false alarm property for the clutter covariance matrix structure. Simulation experiments showed that the proposed method guarantees a constant false alarm property for the clutter covariance matrix structure; in addition, compared with the existing distributed MIMO radar detection methods, the proposed detection method improves the target detection and interference suppression performances.
- Distributed MIMO radar /
- Double hierarchical nonhomogeneous /
- Target detection /
- Subspace interference /
- Constant false alarm property

HTML全文

图 1 分布式MIMO雷达发射-接收框图

Figure 1. Distributed MIMO radar transmit-receive block diagram

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图 2 Rao和Wald检测器框图

Figure 2. Block diagram of Rao and Wald detectors

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图 3 本文所提检测器的CFAR特性曲线

Figure 3. CFAR characteristic curve of the detector proposed in this paper

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图 4 不同训练样本数下的检测概率与SNR的关系(非起伏目标)

Figure 4. Detection probability versus SNR for different number of training samples (non-fluctuated target)

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图 5 ${P_{{\mathrm{fa}}}} = {10^{ - 4}}$时不同训练样本数下的检测概率与SNR的关系

Figure 5. Detection probability versus SNR for different number of training samples at${P_{{\mathrm{fa}}}} = {10^{ - 4}}$

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图 6 不同SNR下的检测概率与INR的关系

Figure 6. Detection probability versus INR at different SNRs

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图 7 不同T-R对下检测概率与SNR的关系

Figure 7. Relationship between detection probability and SNR under different T-R pairs

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图 8 不同训练样本数下的检测概率与SNR的关系(起伏目标)

Figure 8. Detection probability versus SNR for different number of training samples (fluctuated target)

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图 9 不同SNR下的检测概率与INR的关系(起伏目标)

Figure 9. Detection probability versus INR at different SNRs (fluctuated target)

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图 10 不同T-R对下检测概率与SNR的关系(起伏目标)

Figure 10. Relationship between detection probability and SNR under different T-R pairs (fluctuated target)

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表 1 所有检测器的计算复杂度

Table 1. Computational complexity of all detectors

检测器名称	计算复杂度
MIMO-Rao-hom与 MIMO-Wald-hom	$O(MN{L^3})$
MIMO-Rao与MIMO-Wald	$ O(MN({L^3} + {L^2}K)) $
MIMO-Rao-nscm与 MIMO-Wald-nscm	$O(MN({L^3} + {L^2}K + LK))$
MIMO-Raoi-m与MIMO-Waldi-m	$O(MN({L^3} + {L^2}K + K\log K))$

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