基于周期截断数据矩阵奇异值分解的干扰抑制技术

戚连刚; 申振恒; 王亚妮; 国强; KaliuzhnyMykola

doi:10.11999/JEIT210397

基于周期截断数据矩阵奇异值分解的干扰抑制技术

doi: 10.11999/JEIT210397

戚连刚^{1, 2},
申振恒^{1, 4},
王亚妮^{1, 4},
国强^{1, 4, ,},
KaliuzhnyMykola^{1, 3}

1.
哈尔滨工程大学信息与通信工程学院哈尔滨 150001
2.
中国电子科技集团公司第十研究所敏捷智能计算四川省重点实验室成都 610036
3.
哈尔科夫国立无线电电子大学乌克兰哈尔科夫 61166
4.
先进船舶通信与信息技术工业和信息化部重点实验室哈尔滨 150001

基金项目: 国家重点研发计划(2018YFE0206500)，国家自然科学基金(62071140,62101155)

详细信息

作者简介:
戚连刚：男，1990年生，讲师，预聘副教授，研究方向为自适应干扰抑制、卫星导航信号处理

申振恒：男，1997年生，硕士生，研究方向为自适应干扰抑制

王亚妮：女，1991年生，博士生，研究方向为智能阵列信号处理、自适应干扰抑制

国强：男，1972年生，教授，研究方向为电子对抗、智能信号处理与识别

KaliuzhnyMykola：男，1948年生，教授，研究方向为电子对抗、电磁目标识别

通讯作者:
国强　guoqiang@hrbeu.edu.cn

中图分类号: TN96; TN97
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出版历程
- 收稿日期: 2021-05-12
- 修回日期: 2021-10-20
- 网络出版日期: 2021-11-04
- 刊出日期: 2022-06-21

Interference Suppression Technology Based on Singular Value Decomposition of Periodic Truncated Data Matrix

QI Liangang^{1, 2},
SHEN Zhenheng^{1, 4},
WANG Yani^{1, 4},
GUO Qiang^{1, 4
, ,},
Kaliuzhny Mykola^{1, 3}

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Agile and Intelligent Computing Key Laboratory of Sichuan Province, Chengdu 610036, China
3.
Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine
4.
Key Laboratory of Advanced Marine Communication and InformationTechnology, Ministry of Industry and Information Technology, Harbin 150001, China

Funds: The National Key Research and Development Program (2018YFE0206500), The National Natural Science Foundation of China (62071140,62101155)

摘要

摘要: 针对现有适用于单天线接收机的干扰抑制技术难以为周期调频(PFM)干扰和卫星导航信号提供足够分离度，导致消除干扰成分时卫星导航信号损伤较大的问题，该文提出一种基于周期截断数据矩阵奇异值分解的干扰抑制方法。利用调频干扰信号的周期性把分散在较大带宽的能量集中到重排数据中几个甚至单个频点；进而采用奇异值分解(SVD)将干扰与期望信号映射进不同的投影子空间以消除干扰成分。仿真结果表明该方法可以降低在剔除干扰时卫星导航信号损失，提升卫星导航接收机对抗宽带周期调频干扰的能力。
- 卫星导航接收机 /
- 周期调频干扰抑制 /
- 周期截断数据矩阵 /
- 奇异值分解
Abstract: The separation between the Periodic Frequency Modulation (PFM) interference and satellite navigation signal is not enough in the traditional transform domain, which causes severe satellite navigation signal degradation is suppressing interference. To solve this problem, a data rearrangement method based on period truncation is proposed. Using the periodicity of the PFM interfering signal, the energy scattered in a larger bandwidth is concentrated to a single frequency point in the rearranged data. Then, Singular Value Decomposition (SVD) is adopted to map the interference and the desired signal into different projection subspaces to eliminate the interference components. The simulation results show that the proposed method can reduce the overlap degree of the PFM interference and satellite navigation signals, and reduce the damage of satellite navigation signals.
- Satellite navigation receiver /
- Periodic Frequency Modulation(PFM) interference suppression /
- Periodic truncated data matrix /
- Singular Value Decomposition(SVD)

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图 1 本文所提方法原理框图

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图 2 单分量干扰场景下各方法干扰抑制性能

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图 3 双分量干扰场景1下各方法干扰抑制性能

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图 4 双分量干扰场景2下各方法干扰抑制性能

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图 5 双分量场景2下干扰抑制效果随信噪比变化情况

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表 1 各干扰场景干扰参数

干扰场景	调频率(GHz/s)	扫频周期(ms)	带宽(MHz)	起始频率，终止频率(MHz)
单线性调频干扰场景	10	0.2	2	0.1,2.1
双线性调频干扰场景1	10；–2	0.2；0.4	2；0.8	0.1,2.1；2,1.2
双线性调频干扰场景2	10；–2	0.2；0.32	2；0.64	0.1,2.1；2,1.36

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表 2 单分量干扰场景与双分量干扰场景1性能变化对比

	MSTFT		WPCT		FRFT		文献[11]方法(16个周期)		本文方法(16个周期)
INR (dB)	30	45	30	45	30	45	30	45	30	45
ΔNMSE	↑0.3	↑0.3	↑0.4	↑0.3	↑0.2	↑0.1	0	0	0	0
ΔSINR	↓2.7	↓5.7	↓9.8	↓7.2	↓1.8	↓5.1	↓1.8	↓2.9	0	0
ΔAF	↓2.2	↓1.8	↓3.9	↓2.2	↓10.3	↓1.6	↓1.2	↓0.8	↓0.7	↓0.7

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表 3 双分量干扰场景1与双分量干扰场景2性能变化对比

	MSTFT		WPCT		FRFT		文献[11]方法(16个周期)		本文方法(16个周期)
INR(dB)	30	45	30	45	30	45	30	45	30	45
ΔNMSE	0	0	0	↑0.1	0	↑0.1	↑0.1	↑0.1	0	0
ΔSINR	0	↓1.2	↓1.1	↓1.5	0	↓1.5	↓1.5	↓2.5	↓0.4	↓5.4
ΔAF	↓1.1	0	↓1.0	0	↓1.3	↓0.9	↓2.0	↓0.9	↓0.6	↓12.1

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表 4 计算复杂度对比

方法	计算复杂度	单干扰场景	双干扰场景1	双干扰场景2
本文方法	$ 2{N_p}(4 + 6{\log _2}{N_p}){\text{ + }}{N_j}\left[ {\left\lceil {{N_I}/(MQ)} \right\rceil ({\text{3}}MQ + {M^3} + {\text{3}}{M^2}{\text{ - }}M + 2{Q^2} - Q + {\text{3}})} \right] $	3.9×10⁸	7.6×10⁸	8.8×10⁸
MSTFT	$2K{N_I}/({N_t} - v){N_t}{\log_2}{N_t} + {N_I}$	9.2×10⁹	9.2×10⁹	9.2×10⁹
WPCT	$8{N_I}{N_t}{\log_2}{N_t}({2^5} - 1) + {({N_I})^2} + {N_I}$	9.0×10¹²	9.0×10¹²	9.0×10¹²
FRFT	${N_j}(r + 2{N_I}/{N_t})(6{N_t}{\log_2}{N_t} + 3{N_t}) + 2{N_I}$	5.1×10⁹	5.1×10⁹	1.0×10¹⁰
文献[9]	$2{N_I}{N_t}{\log_2}({N_t}) + {N_I} + {N_t}({N_t} - 2)$	6.1×10¹⁰	–	–
文献[11]	$2{N_p}(4 + 6 {\log_2}{N_p}) + {N_j}[8{N_L}(1 + 2 {\log_2}{N_L}) + 8\left\lceil { {N_I}/{N_B} } \right\rceil {N_B} {\log_2}{N_B}]$	9.9×10⁷	9.9×10⁷	1.9×10⁸

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