基于特征值融合的动态信道化子带检测算法

doi:10.12305/j.issn.1001-506X.2025.02.03

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (2): 360-368.doi: 10.12305/j.issn.1001-506X.2025.02.03

• 电子技术 • 上一篇

基于特征值融合的动态信道化子带检测算法

陈侯伯¹^,², 刘霖¹^,²^,*, 崔宁¹, 张旭冉³, 赵麒瑞¹, 刘翔¹

1. 中国科学院空天信息创新研究院, 北京 100094
2. 中国科学院大学电子电气与通信工程学院, 北京 100049
3. 复旦大学信息科学与工程学院, 上海 200433

收稿日期:2024-01-02 出版日期:2025-02-25 发布日期:2025-03-18
通讯作者: 刘霖
作者简介:陈侯伯 (2000—), 男, 硕士研究生, 主要研究方向为高速实时信号处理、雷达信号处理
刘霖 (1982—), 男, 副研究员, 硕士, 主要研究方向为合成孔径雷达、数字信号处理
崔宁 (1995—), 男, 助理研究员, 博士, 主要研究方向为机载雷达广域GMTI、MMTI、AMTI和SAR-GMTI及其在实际雷达系统中的应用
张旭冉 (2000—), 男, 硕士研究生, 主要研究方向为光纤传感、高速光通信系统
赵麒瑞 (1998—), 男, 助理工程师, 硕士, 主要研究方向为高速实时信号处理
刘翔 (1979—), 男, 工程师, 本科, 主要研究方向为微流控芯片及系统可靠性
基金资助:
国家自然科学基金(61971026)

Dynamic channelized subband detection algorithm based on eigenvalue fusion

Houbo CHEN¹^,², Lin LIU¹^,²^,*, Ning CUI¹, Xuran ZHANG³, Qirui ZHAO¹, Xiang LIU¹

1. Aerospace Information Innovation Institute, Chinese Academy of Sciences, Beijing 100094, China
2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Information Science and Technology, Fudan University, Shanghai 200433, China

Received:2024-01-02 Online:2025-02-25 Published:2025-03-18
Contact: Lin LIU

摘要/Abstract

摘要：

针对动态数字信道化接收领域中传统子带检测算法需要信号和噪声先验信息等问题, 提出基于特征值融合的动态信道化子带检测算法。首先, 基于随机矩阵理论(random matrix theory, RMT), 利用采样协方差矩阵中的最大、最小和平均特征值, 引入融合参数α, 构造融合检测统计量。随后, 通过最小特征值的极限分布, 推导出一种高效的检测门限, 并据此设计一套基于特征值融合的子带盲检测算法, 命名为α-最大、最小和平均特征值(α-maximum-average-minimum eigenvalue, α-MAME)算法。在实验阶段, 对不同动态数字信道化接收条件下的算法性能进行仿真验证。结果表明, 与现有算法相比, 所提子带检测算法在低信噪比和低维度条件下具有更好的检测性能。

关键词: 动态数字信道化, 子带检测, 特征值检测, 随机矩阵

Abstract:

To address issues in traditional subband detection algorithms in the field of dynamic digital channelized reception field, such as the need for prior information about signals and noise. An eigenvalue fusion based dynamic channel subband detection algorithm is proposed. Firstly, based on random matrix theory (RMT), the maximum, minimum, and average feature values in the sampled covariance matrix are employed, incorporating a fusion parameter α to construct a fusion detection statistical value. Subsequently, an efficient detection threshold is derived through the limit distribution of the minimum feature value. Based on this, a subband blind detection algorithm based on feature-value fusion is designed, which is named as the α-maximum-average-minimum eigenvalue (α-MAME) algorithm. In the experiment, the algorithm's performance is simulated and verified under various receiving conditions of dynamic digital channels. The experimental results indicate that compared to existing algorithms, the proposed subband detection algorithm demonstrates superior detection performance under low signal-to-noise ratios and low-dimensional conditions.

Key words: dynamic digital channelization, subband detection, eigenvalue detection, random matrix

中图分类号:

TN911.7

陈侯伯, 刘霖, 崔宁, 张旭冉, 赵麒瑞, 刘翔. 基于特征值融合的动态信道化子带检测算法[J]. 系统工程与电子技术, 2025, 47(2): 360-368.

Houbo CHEN, Lin LIU, Ning CUI, Xuran ZHANG, Qirui ZHAO, Xiang LIU. Dynamic channelized subband detection algorithm based on eigenvalue fusion[J]. Systems Engineering and Electronics, 2025, 47(2): 360-368.

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t	F₁(t)
-3.895	0.01
-3.183	0.05
-2.782	0.10
0.450	0.09
0.980	0.95
2.022	0.99

算法名称	特征值选取	检测门限推导方式
MME	最大、最小特征值	最大特征值极限分布
AME	平均、最大特征值	正态分布
AEME	平均、最小特征值	最小特征值极限分布
MMAE	当前子带最大特征值、所有子带平均特征值	平均特征值极限分布
本文算法	最大、最小、平均特征值	最小特征值极限分布

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[2]	邹卫霞, 丁奇, 周正, 张春青. 基于特征值极限分布的双门限频谱感知算法[J]. Journal of Systems Engineering and Electronics, 2012, 34(3): 588-591.

基于特征值融合的动态信道化子带检测算法

Dynamic channelized subband detection algorithm based on eigenvalue fusion

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