超视距雷达中射频干扰仿真与距离-多普勒图检测方法

doi:10.12263/DZXB.20210402

电子学报 ›› 2022, Vol. 50 ›› Issue (5): 1174-1180.DOI: 10.12263/DZXB.20210402

超视距雷达中射频干扰仿真与距离-多普勒图检测方法

罗忠涛¹, 严美慧¹, 卢琨², 夏杭¹

^1.重庆邮电大学通信与信息工程学院, 重庆 400065
^2.南京电子技术研究所, 江苏南京 210013

收稿日期:2021-03-28 修回日期:2021-10-28 出版日期:2022-05-25
- 作者简介:
- 罗忠涛男,1984年生于四川省隆昌市. 现为重庆邮电大学通信与信息工程学院副教授.研究方向为统计信号处理、数字图像处理与机器学习.E-mail: luozt@cqupt.edu.cn
  严美慧女,1996年出生于江西省赣州市.现为重庆邮电大学通信与信息工程学院研究生.研究方向为雷达信号处理、图像分析与机器学习.E-mail: 1264212873@qq.com
  卢琨男,1977年出生于广西省桂林市.现为南京电子技术研究所研究员级高级工程师.研究方向为超视距雷达系统设计与信息处理.E-mail: mimimomoba@gmail.com
  夏杭男,1994年出生于贵州省安顺市.现为重庆邮电大学通信与信息工程学院研究生.研究方向为雷达信号处理、数字图像处理与机器学习.E-mail: 1790095607@qq.com
- 基金资助:
- 国家自然科学基金 (61701067)

Radio Frequency Interference Simulation and Detection in the Range-Doppler Map for Over-the-Horizon Radar

LUO Zhong-tao¹, YAN Mei-hui¹, LU Kun², XIA Hang¹

^1.School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
^2.Nanjing Research Institute of Electronics Technology, Nanjing, Jiangsu 210013, China

Received:2021-03-28 Revised:2021-10-28 Online:2022-05-25 Published:2022-06-18
- Supported by:
- National Natural Science Foundation of China (61701067)

摘要/Abstract

摘要：

超视距雷达存在射频干扰(Radio Frequency Interference, RFI)问题，尤以遍布全距离-多普勒(Range-Doppler, RD)图的宽带RFI影响最大.本文给出宽带RFI的仿真方法和基于RD图的RFI检测方法.首先，基于自回归滑动平均模型仿真宽带RFI，模拟实测RFI的时频特性和RD图形态.其次，基于图像分类思想，研究RD图宽带RFI检测器，通过提取RD图纹理特征，运用模式识别，实现RD图有无宽带RFI的分类.图库设计以仿真RFI数据的RD图库作训练集，以实测数据的RD图库作测试集，识别算法以K近邻为例.实验仿真3种纹理特征、5种距离度量及有无海杂波等多种组合，检测性能统计表明，适当设计的干扰识别率普遍能达到96%以上，验证了本文所提的干扰仿真与检测方法的有效性.

关键词: 超视距雷达, 距离-多普勒图, 射频干扰仿真, 干扰检测, 纹理特征, K近邻

Abstract:

Over-the-horizon radar(OTHR) is often threatened by radio frequency interference(RFI), among which the wideband RFI spreading all over the range-Doppler(RD) map is the worst. This paper proposes the methods for RFI simulation and wideband RFI detection based on the RD map. Firstly, based on the auto-regressive and moving-average model, the RFI signal is simulated and can imitate the real RFI characteristics in the time-frequency domain and the RD map. Secondly, by the idea of image classification, the wideband RFI detector is designed based on the RD map. Its guideline is: based on the RD maps, extract their texture features and use pattern recognition algorithms to classify whether the RD map has any wideband RFI. For the RD image datasets, the simulated RFI data is used for the training dataset, while the real data is used for the testing dataset. The K-nearest neighbor(KNN) algorithm is employed as an example for classification. The experiments investigate three kinds of texture features, five kinds of distance measurements, and whether the sea clutter is cancelled. The results show the accuracy of a proper design is generally higher than 96%, which demonstrates the effectiveness of the proposed methods for RFI simulation and detection.

Key words: over-the-horizon radar, range-Doppler map, radio frequency interference simulation, interference detection, texture features, K-nearest neighbor

中图分类号:

TN958.93

罗忠涛, 严美慧, 卢琨, 等. 超视距雷达中射频干扰仿真与距离-多普勒图检测方法[J]. 电子学报, 2022, 50(5): 1174-1180.

Zhong-tao LUO, Mei-hui YAN, Kun LU, et al. Radio Frequency Interference Simulation and Detection in the Range-Doppler Map for Over-the-Horizon Radar[J]. Acta Electronica Sinica, 2022, 50(5): 1174-1180.

图/表 7

图1 实测与仿真RFI对比图

图2 基于RD图的干扰检测器

图3 去海杂波前后的RD灰度图(a)无RFI图 (b)有RFI图　 (c)无海杂波的RFI图

图4 训练集与测试集设计方案

表1 LBPQ,Ru2参数与海杂波的识别率

算法	有海杂波/无海杂波（%）
算法	$L B P 8, 1 u 2$	$L B P 8, 2 u 2$	$L B P 8, 3 u 2$	$L B P 8, 4 u 2$	$L B P 16,1 u 2$	$L B P 16,2 u 2$	$L B P 16,3 u 2$	$L B P 16,4 u 2$
欧氏	98.0/98.0	99.5/99.0	99.5/99.5	99.5/99.0	98.0/98.0	99.0/99.0	99.5/99.5	99.5/99.0
标欧	97.5/98.0	98.0/100	96.0/98.5	91.5/95.5	98.5/98.5	98.5/100	100/99.0	98.0/99.0
余弦	98.0/98.0	99.5/99.0	99.5/99.5	99.5/99.0	98.0/98.0	99.0/99.0	99.0/99.5	99.5/99.0
卡方	98.0/97.0	99.0/98.5	99.5/99.5	100/99.0	97.5/97.5	99.0/98.0	99.5/98.5	100/98.5
马氏	81.0/77.0	88.5/92.0	90.0/93.0	80.5/87.0	91.5/68.5	97.0/95.0	87.5/93.0	91.0/93.0

表1 LBPQ,Ru2参数与海杂波的识别率

算法	有海杂波/无海杂波（%）
算法	$L B P 8, 1 u 2$	$L B P 8, 2 u 2$	$L B P 8, 3 u 2$	$L B P 8, 4 u 2$	$L B P 16,1 u 2$	$L B P 16,2 u 2$	$L B P 16,3 u 2$	$L B P 16,4 u 2$
欧氏	98.0/98.0	99.5/99.0	99.5/99.5	99.5/99.0	98.0/98.0	99.0/99.0	99.5/99.5	99.5/99.0
标欧	97.5/98.0	98.0/100	96.0/98.5	91.5/95.5	98.5/98.5	98.5/100	100/99.0	98.0/99.0
余弦	98.0/98.0	99.5/99.0	99.5/99.5	99.5/99.0	98.0/98.0	99.0/99.0	99.0/99.5	99.5/99.0
卡方	98.0/97.0	99.0/98.5	99.5/99.5	100/99.0	97.5/97.5	99.0/98.0	99.5/98.5	100/98.5
马氏	81.0/77.0	88.5/92.0	90.0/93.0	80.5/87.0	91.5/68.5	97.0/95.0	87.5/93.0	91.0/93.0

表2 HOG参数与海杂波的识别率

算法	有海杂波/无海杂波（%）
算法	单元格8×8	单元格 16×16	单元格 32×32	单元格 128×128
欧氏	97.0/97.0	97.5/97.5	98.0/98.0	97.5/97.5
标欧	96.0/97.0	97.5/97.0	98.5/98.0	97.5/97.5
余弦	97.0/97.0	97.5/97.5	98.0/98.0	97.5/97.5
卡方	96.0/96.5	96.5/97.0	97.5/98.5	98.0/97.5
马氏	80.0/57.5	75.0/81.5	66.0/62.0	91.5/95.0

表3 GLCM参数与海杂波的识别率

θ=0°

θ平均

87.5/93.5

49.0/69.5

94.0/94.5

82.5/78.5

92.5/94.0

83.0/77.0

93.0/94.0

83.0/76.5

标欧

θ=0°

θ平均

90.0/95.0

51.0/75.0

90.0/96.5

53.5/82.5

92.5/96.0

67.0/84.0

92.0/96.5

74.0/84.5

余弦

θ=0°

θ平均

88.5/93.5

52.0/68.0

95.0/94.0

81.5/82.5

95.5/94.5

82.5/72.5

96.0/94.0

85.0/73.0

卡方

θ=0°

θ平均

91.0/94.0

53.5/65.0

94.0/94.5

83.0/78.5

94.0/94.0

81.5/74.5

93.5/94.0

81.5/75.5

马氏

θ=0°

θ平均

86.5/88.5

76.0/78.5

89.0/93.5

64.5/73.0

88.0/94.5

66.5/72.5

86.5/93.5

67.5/80.0

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超视距雷达中射频干扰仿真与距离-多普勒图检测方法

Radio Frequency Interference Simulation and Detection in the Range-Doppler Map for Over-the-Horizon Radar

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