基于频谱形状的低复杂度雷达信号分类

doi:10.11959/j.issn.1000-0801.2022011

Abstract

Abstract:

In order to solve the problems of high computational complexity, low recognition accuracy of low signal to noise ratio (SNR) environment and low fidelity of simulation data in radar signal modulation recognition, a low complexity radar signal classification algorithm based on spectrum shape was proposed.Signal spectrum was normalized, feature parameters were extracted by spectrum sampling method, and then machine learning classification model was trained.The test results of the data generated by the radar signal source show that the classification accuracy of Barker code, Frank code, LFM code, BPSK, QPSK modulation and conventional radar signals is more than 90% (SNR≥3 dB).The algorithm has low computational complexity, can adapt to the change of signal parameters, and has good generalization.

Key words: spectrum shape, low complexity, feature extraction, spectrum sampling

CLC Number:

TN971.+1

Liang YIN, Rui LIN, Xiaolei WANG, Yuliang YAO, Lin ZHOU, Yuan HE. Low complexity radar signal classification based on spectrum shape[J]. Telecommunications Science, 2022, 38(1): 25-35.

Figures/Tables 13

调制方式	φ (t)	相位说明
常规雷达信号	$ϕ (t) = 2 π f_{0} t$	常数
BPSK调制	$ϕ (t) = 2 π f_{0} t + π C_{d} (k) + ϕ_{0}$	C_d(k)取值二进制数0或1
QPSK调制	$ϕ (t) = 2 π f_{0} t + π C_{d} (k) + ϕ_{0}$	C_d(k)取值{1/4,3/4,5/4,7/4}或{0,1/2,1,3/2}
Barker码调制	$ϕ (t) = 2 π f_{0} t + θ_{n}$	θ_n为Barker码相位，取0或π
LFM调制	$ϕ (t) = 2 π [(f_{0} - \frac{B}{2}) t + \frac{k}{2} t^{2} + ϕ_{0}]$	常数
Frank码调制	$ϕ (t) = 2 π f_{0} t + ϕ_{p, q}$	$ϕ_{p, q} = \frac{2 π}{M} (p - 1) (q - 1), q = 1, \dots, M; p = 1, \dots, M$

	特征提取算法	算法复杂度	抗噪性
文献[12]	伪Wigner-Ville分布（PWD）、Choi-Williams分布（CWD）	$\frac{N}{2} lb N + 2 N$	3 dB
文献[13]	短时傅里叶变换（STFT）	$\frac{N H}{2} lb H + N H$	0 dB
文献[14]	Wigner-Ville分布	$\frac{N}{2} lb N + 2 N$	0 dB
文献[15]	Morlet小波	$\frac{N^{3} H}{2} lb H + 2 N H$	2 dB
文献[8]	瞬时特征/FFT	NlbN	7 dB
本文	FFT	NlbN	3 dB

References 16

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数据	脉冲宽度/μs	脉冲周期/μs	占空比	载频/GHz	采样率/MHz
第一组	1	10.3	9.7%	2	50
第二组	2	10.3	19.4%	2	50

实验组数	特征向量	分类器	训练集	样本数量
1	峰度、偏度、人工采样点	浅层神经网络	第一组	每种调制1 000个，共6 000个样本
2	下采样的频谱数据	浅层神经网络	第一组	每种调制1 000个，共6 000个样本
3	下采样的频谱数据	浅层神经网络	第一组及第二组	每种调制1 000个，共6 000个样本

Low complexity radar signal classification based on spectrum shape

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	Pulse	QPSK	BPSK	Frank	Barker	LFM
Pulse	98	3	3	0	0	0
QPSK	1	61	33	0	0	0
BPSK	1	36	64	2	0	0
Frank	0	0	0	98	0	0
Barker	0	0	0	0	100	0
LFM	0	0	0	0	0	100

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