Unknown Signal Incremental Recognition Based on Multi-Flow ConvNeXt Network and Mahalanobis Distance Metric

doi:10.16183/j.cnki.jsjtu.2022.426

Abstract

Abstract:

In order to solve the problem that the signal recognition technology based on deep learning network cannot currently realize the incremental recognition of unknown signals, a method for incremental recognition of such unknown signals, based on the combination of the multi-flow ConvNeXt network and Mahalanobis distance metric (MDM) is proposed. First, the improved multi-flow ConvNeXt network is used to extract the attribute features of signals. Then, the MDM judgement method is used to detect unknown signals, and apply the binary classification for known and unknown signals. Finally, the parameters of the model is automatically updated according to the increasing number of unknown signals. In such way, the model has the ability of self-evolution, and it has the ability to recognize incrementally more types of unknown signals.The simulation results show that the average recognition rate of unknown signals is more than 97%.

Key words: unknown signal, multi-flow ConvNeXt network, Mahalanobis distance metric, incremental recognition

CLC Number:

TN911.3

XIAO Yihan, LIU Xubin, YU Xiangzhen, ZHAO Zhongkai. Unknown Signal Incremental Recognition Based on Multi-Flow ConvNeXt Network and Mahalanobis Distance Metric[J]. Journal of Shanghai Jiao Tong University, 2024, 58(4): 481-491.

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配置	型号	参数
处理器及内存	Intel(R) Xeon(R) Silver 4110	2.1 GHz 32 GB内存
操作系统	Ubuntu 18.04.1	64位
显卡	NVIDIA Tesla T4	15 GB显存
语言	Python	3.7.9
框架	Pytorch	1.7.1
运算平台	CUDA	10.1

类别	预测的已知信号	预测的未知信号
真实的已知信号	T_P	F_N
真实的未知信号	F_P	T_N

α₁	T_P	F_N	T_N	F_P	R_TK	R_TU
0.00	0	3 600	800	0	0	1
0.33	2 664	936	798	2	0.740	0.998
0.66	3 500	100	791	9	0.972	0.989
1.00	3 564	36	504	296	0.990	0.630

实际信号	预测信号										识别率/%
实际信号	8PSK	AM-DSB	BPSK	CPFSK	PAM4	QAM16	QAM64	QPSK	WBFM	未知信号	识别率/%
8PSK	385	0	1	0	0	1	0	1	0	12	96.3
AM-DSB	0	213	0	0	0	0	0	0	167	20	53.3
BPSK	2	0	374	0	0	0	0	2	0	22	93.5
CPFSK	0	0	0	394	0	0	0	0	0	6	98.5
PAM4	0	0	0	0	391	0	0	0	0	9	97.8
QAM16	8	0	0	0	0	342	23	2	0	25	85.5
QAM64	3	0	0	0	0	31	339	1	0	26	84.8
QPSK	0	0	1	0	0	0	2	386	0	11	96.5
WBFM	0	57	0	0	0	0	0	0	324	19	81.0
AM-SSB	0	0	0	0	0	0	1	0	0	399	99.8
GFSK	0	0	0	0	0	0	0	0	8	392	98.0

实际信号	预测信号
实际信号	未知1	未知2	未知3	未知4	未知5	未知6
8PSK	4	3	1	3	0	1
AM-DSB	10	5	0	2	3	0
BPSK	2	8	3	7	2	0
CPFSK	0	3	3	0	0	0
PAM4	0	3	5	1	0	0
QAM16	7	9	6	2	1	0
QAM64	1	6	12	2	4	1
QPSK	0	1	8	0	2	0
WBFM	0	13	0	3	2	1
AM-SSB	395	3	0	0	1	0
GFSK	2	386	1	2	1	0