基于杂波聚类与贪婪策略的电离层杂波智能处理方法

位寅生; 周建宇; 许荣庆

doi:10.12000/JR20086

基于杂波聚类与贪婪策略的电离层杂波智能处理方法

DOI: 10.12000/JR20086

哈尔滨工业大学电子与信息工程学院哈尔滨 150001

基金项目: 国家自然科学基金重点项目(61831010)，黑龙江省科学基金项目(JQ2019F001)

详细信息

作者简介:
位寅生(1974–)，男，黑龙江人，哈尔滨工业大学电子与信息工程学院党委书记兼副院长，教授，博士生导师，主要从事杂波建模与抑制方法、抗干扰与抗杂波雷达信号体制、毫米波雷达探测技术研究

周建宇(1988–)，男，黑龙江人，哈尔滨工业大学博士，主要进行高频雷达抗干扰研究

许荣庆(1958–)，男，黑龙江人，哈尔滨工业大学电子工程技术研究所所长，教授，博士生导师，主要从事新体制雷达系统技术、雷达成像技术和现代信号处理技术研究

通讯作者:
位寅生 hitweiysgroup@163.com

责任主编：陈建文 Corresponding Editor: CHEN Jianwen
中图分类号: TN95
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出版历程
- 收稿日期: 2020-06-27
- 修回日期: 2020-08-06
- 网络出版日期: 2020-08-28

Intelligent Suppression Method for Ionospheric Clutter Based on Clustering and Greedy Strategy

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

Funds: The National Natural Science Foundation of China (61831010), Heilongjiang Provincial Natural Science Foundation of China (JQ2019F001)

More Information

Corresponding author: WEI Yinsheng, hitweiysgroup@163.com

摘要

摘要: 在高频地波超视距雷达系统中，电离层杂波作为一种时变、非均匀、非高斯的复杂杂波，其抑制方法一直是困扰国内外的研究难点。针对传统杂波抑制方法对电离层杂波的处理能力单一、普适性差的问题，该文开展了杂波智能分类抑制处理方法的研究，通过对电离层杂波的成因与特性分析，提出了一种基于杂波聚类与贪婪策略的电离层杂波智能处理方法，对电离层杂波进行分类分情况处理。实验分析表明该方法对电离层杂波的抑制性能优于典型传统算法。
- 智能杂波处理 /
- 高频地波超视距雷达 /
- 杂波分类 /
- 杂波抑制 /
- 电离层杂波
Abstract: Clutter is a term used for unwanted echoes in electronic systems, particularly in reference to radars. Such echoes are typically returned from ground, sea, rain, animals/insects, chaff, and atmospheric turbulences, and can cause serious performance issues with radar systems. Ionospheric clutter is a time-varying, nonstationary, and non-Gaussian complex clutter in High-Frequency Surface-Wave Radar (HFSWR) system and its suppression is a daunting task. Extensive research on intelligent classification systems and suppression techniques of ionospheric clutter was conducted to solve the universal problem of single clutter suppression algorithm. After a complete analysis of the characteristics of ionospheric clutter, the present work proposes an intelligent ionospheric clutter processing method based on clustering and greedy algorithms for the classification and suppression of ionospheric clutter. Experimental results showed that the proposed method has a better performance than the traditional algorithm in suppressing ionospheric clutter.
- Intelligent clutter processing /
- High-Frequency Surface-Wave Radar (HFSWR) /
- Clutter cluster /
- Clutter suppression /
- Ionospheric clutter

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图 1 电离层结构示意图

Figure 1. Structure of the ionosphere

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图 2 典型地波雷达的几种主要路径

Figure 2. Several main paths of the HFSWR

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图 3 电离层杂波特性

Figure 3. Features of ionospheric clutter

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图 4 典型电离层杂波示意图

Figure 4. Typical ionospheric clutter

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图 5 传统的电离层杂波分类抑制处理框架流程图

Figure 5. Traditional ionospheric clutter classification and suppression processing framework

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图 6 实测数据RD谱

Figure 6. RDP for measured data

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图 7 实测数据电离层杂波分类结果

Figure 7. Clutter classification results for measured data

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图 8 单一算法对电离层杂波的抑制

Figure 8. Ionospheric clutter suppression using single algorithm

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图 9 电离层杂波智能抑制处理流程框架

Figure 9. Ionospheric clutter intelligent suppression framework

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图 10 实测数据结果

Figure 10. Results for measured data

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图 11 不同电离层杂波抑制算法对比

Figure 11. Comparison of different ionospheric clutter suppression algorithms

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表 1 典型电离层杂波特性

Table 1. Characteristics of typical ionospheric clutter

杂波类型	功率	小波尺度	方向性	空域同质性	距离域相关性
能量聚集型强方向性	聚集	与目标相异	集中	同质	相关
点状	聚集	与目标相似	分散	异质	非相关
空域同分布	分散	与目标相异	分散	同质	非相关
距离域相关	分散	与目标相异	分散	异质	相关
类目标	分散	与目标相似	集中	异质	非相关

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表 2 K-means算法聚类后样本的特性统计

Table 2. Characteristic statistics after K-means algorithm clustering

类型	特性A	特性B	特性C	特性D	特性E
A	0	0.26	0.21	0.29	0.25
B	0.44	0.85	0.15	0.41	1.00
C	0.29	0	0.23	1.00	1.00
D	1.00	0.02	0.88	0.04	0.03
E	1.00	0.60	0.09	0.79	0
F	0	0.64	1.00	0.81	0.22

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表 3 CSKM算法聚类后样本的特性统计

Table 3. Characteristic statistics after CSKM algorithm clustering

类型	特性A	特性B	特性C	特性D	特性E
A	1.00	0.07	0.90	1.00	0.86
B	0.99	1.00	0.11	0.18	0.14
C	0.15	0	0.11	1.00	0.30
D	0.09	0	0.08	0	1.00
E	0.13	1.00	1.00	0.07	0.06
F	0	0.04	0	0	0

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表 4 聚类结果有效性指标

Table 4. Validity index of clustering results

算法	DBI	DI
K-means	0.9916	0.2500
CSKM	0.6336	0.3333

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表 5 K-means算法聚类后样本的特性统计

Table 5. Characteristic statistics after K-means algorithm clustering

类型	功率聚集性	小波尺度相似性	方向集中性	空域同质性	距离域相关性
A	0.76	1.00	0.69	0.68	0.79
B	0.78	0.34	0.80	0	0
C	0	0.13	0	0.12	0.16
D	1.00	0	0	0.44	0.87
E	0.50	0	1.00	0.61	0.73
F	0	0.80	0	0.86	0.99

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表 6 CSKM算法聚类后样本的特性统计

Table 6. Characteristic statistics after CSKM algorithm clustering

类型	功率聚集性	小波尺度相似性	方向集中性	空域同质性	距离域相关性
A	0.73	0	0.77	0.87	0.86
B	0.83	1.00	0.10	0.15	0.12
C	0.09	0.55	0.10	1.00	0.78
D	0.12	0	0.13	0	1.00
E	0.14	1.00	0.92	0.18	0.15
F	0.14	0.03	0	0	0.10

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表 7 聚类结果有效性指标

Table 7. Validity index of clustering results

类型	DBI	DI
K-means	1.3976	0.2500
CSKM	0.8224	0.2500

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表 8 目标在不同类型杂波处各算法处理后的信杂比

Table 8. Target SCR after different algorithms processed

	DBF	SSSRB	WOPF	CS-GSC	RA-JDL	N-GSC	本文方法
类型A (dB)	5.00	18.03	14.46	5.84	3.51	14.74	18.40
类型B (dB)	5.00	16.46	19.71	4.67	13.20	15.90	19.55
类型C (dB)	5.00	10.12	9.65	16.93	7.30	9.20	16.64
类型D (dB)	5.00	7.31	1.70	10.25	14.10	6.20	14.07
类型E (dB)	5.00	15.90	13.78	12.12	12.41	17.46	18.01
平均SCR (dB)	5.00	13.56	11.86	9.96	10.10	12.70	17.33

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