基于DSGD的分布式电磁目标识别

doi:10.12305/j.issn.1001-506X.2023.10.04

Abstract

Abstract:

Distributed electromagnetic target identification aims to realize traditional centralized electromagnetic target identification by using technologies such as distributed optimization and distributed computing. The distributed optimization method combines the distributed computing architecture to realize the distributed solution to the optimization problem, and realizes the mapping from the problem information and data to the optimal target identification model in a distributed manner. This paper uses the decentralized stochastic gradient descent, which is a classical distributed optimization method, to establish a distributed computing architecture and a distributed electromagnetic target identification method for electromagnetic target identification. Based on the actual electromagnetic signal data, the effectiveness of the proposed algorithm is verified. When the performance of the distributed electromagnetic target identification algorithm and the centralized identification algorithm remains above 90%, the single node training time decreases by more than 50%, which significantly improves the training efficiency.

Key words: electromagnetic target identification, distributive mode, decentralized, stochastic gradient descent, consistency constraints

CLC Number:

TN971.1

Hongan WANG, Da HUANG, Wei ZHANG, Ye PAN, Xiangfeng WANG, Huaizong SHAO, Jie GU. Distributed electromagnetic target identification based on decentrallized stochastic gradient descent[J]. Systems Engineering and Electronics, 2023, 45(10): 3024-3031.

Figures/Tables 9

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References 31

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训练方法	优化器	损失函数	学习率	批量大小
集中式	SGD	交叉熵	0.01	128
分布式	DSGD	交叉熵	0.01	128

训练方法	单节点样本数量	神经网络	平均训练时间/s	内存占用/G	训练集准确率/%	测试集准确率/%
集中式	126 000	ResNet101	647	4.15	99.97	99.94
集中式	126 000	MLP-mixer	371	4.51	99.80	98.47
分布式(3节点)	42 000	ResNet101	316	1.36	99.60	93.96
分布式(3节点)	42 000	MLP-mixer	157	1.52	92.58	91.93
分布式(4节点)	31 500	ResNet101	229	1.02	99.29	93.55
分布式(4节点)	31 500	MLP-mixer	106	1.14	93.26	92.79
分布式(5节点)	25 200	ResNet101	158	0.82	99.45	92.83
分布式(5节点)	25 200	MLP-mixer	67	0.91	92.40	91.81

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Distributed electromagnetic target identification based on decentrallized stochastic gradient descent

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