基于多级跳线残差网络的雷达辐射源识别

doi:10.12305/j.issn.1001-506X.2022.07.10

Abstract

Abstract:

Aiming at the problem of complex radar emitter recognition, a recognition method based on time-frequency feature extraction and multi-level jumper residual network (MLJ-RN) is proposed. Firstly, the smoothed pseudo Wigner-Ville distribution (SPWVD) of emitter signals is calculated, a time-frequency image is generated to express the essential characteristics of the signal, and the image is preprocessed to retain the subtle differences of the signal features. Then a residual unit connected by multi-level jumpers is designed, a multi-level jumper residual network is constructed on this basis, the fine features of the adjacent convolution layers of the time-frequency image are learned and recognized, and the random gradient descent method is used to train the residual network. Finally, through the adjustment of the network, the parameters of the network are optimized to enhance the deep feature extraction ability of the signal. The simulation results show that when the signal-to-noise ratio (SNR) is －5 dB, the overall recognition probability of the method for 12 types of radar emitter signals reaches 95.1%, which verifies the effectiveness of the method in identifying radar signals at low SNRs.

Key words: time frequency feature, emitter recognition, deep learning, multi-level jumper

CLC Number:

TN971

Limin ZHANG, Kaiwen TAN, Wenjun YAN, Yuyuan ZHANG. Radar emitter recognition based on multi-level jumper residual network[J]. Systems Engineering and Electronics, 2022, 44(7): 2148-2156.

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References 29

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调制方式	信号模型	参数
CW	$s(t)=A(t) \mathrm{e}^{\mathrm{j} 2 \pi f_{c} t}$	载频f_c
LFM	$s(t)=A(t) \mathrm{e}^{\mathrm{j} 2 \pi\left(f_{c} t-50 B f_{s} t^{2}\right)}$	带宽B
BFSK	$s(t)=A(t) \mathrm{e}^{\mathrm{j} 2 \pi f_{1} t}+A(t) \mathrm{e}^{\mathrm{j} 2 \pi f_{2} t}$	载频f₁, f₂
QFSK	$s(t)=\sum\limits_{i=1}^{4} A(t) \mathrm{e}^{\mathrm{j} 2 \pi f_{i} t}$	载频f_i
P1码-P4码	$s(t)=A(t) \mathrm{e}^{\mathrm{j}\left(2 \pi f_{c} t+\varphi_{m, n}+\theta\right)}$	载频f_c
2ASK	$s(t)=A(t) \mathrm{e}^{\mathrm{j} 2 \pi f_{c} t}$	载频f_c
QPSK	$s(t)=\sum\limits_{i=1}^{N} A(t) \mathrm{e}^{\mathrm{j}\left(2 \pi f_{c} t+\varphi_{m, n}\right)}$	相位φ_m,n 载频f_c
BPSK	$s(t)=\sum\limits_{i=1}^{N} A(t) \mathrm{e}^{\mathrm{j}\left(2 \pi f_{c} t+\pi C_{n}\right)}$	巴克码组C_n
BPSK+LFM	$s(t)=\sum\limits_{i=1}^{N} A(t) \mathrm{e}^{\mathrm{j}\left(2 \pi f_{c} t+\pi C_{n}-50 B f_{s} t^{2}\right)}$	带宽B 巴克码组C_n

码型	相位φ_m,n
P1	$\varphi_{m, n}=-\frac{\pi}{N}(N-(2 n-1))((n-1) N+m-1)$,$m=1,2, \cdots, N ; n=1,2, \cdots, N$
P2	$\varphi_{m, n}=\frac{\pi}{2 N}(N-(2 n-1))^{2}$,$n=1,2, \cdots, N,(N 为偶数)$
P3	$\varphi_{m}=\frac{\pi(m-1)^{2}}{N^{2}}, m=1,2, \cdots, N^{2}$
P4	$\varphi_{m}=\frac{\pi(m-1)^{2}}{N^{2}}-\pi(m-1)$,$m=1,2, \cdots, N^{2}$
QPSK	$\varphi_{m, n}=\frac{\pi}{8}(m-1)(n-1), m, n=1,2, \cdots, 16$

网络模型	空间复杂度
MLJ-RN	13.4×10⁶
ResNet-101	42.6×10⁶
AlexNet	52.0×10⁶
VGG-16	138.3×10⁶
GoogLeNet	10.3×10⁶

网络模型	每轮迭代时间	平均识别时间
MLJ-RN	116	15
ResNet-101	297	21
AlexNet	48	9
VGG-16	427	38
GoogLeNet	131	14

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Radar emitter recognition based on multi-level jumper residual network

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