基于EfficientNet的多通道雷达目标微动特征分类方法

doi:10.12305/j.issn.1001-506X.2024.09.11

Abstract

Abstract:

To solve the problem on low classification accuracy caused by the difficulty of extracting features of micro-motion targets, such as pedestrians, vehicles, and drones in low-altitude radar monitoring scenarios, a multi-channel radar target micro-motion feature classification method based on EfficientNet is proposed. Firstly, a multi-energy singular value decomposition method is proposed to suppress clutter and noise, and enhance the micro-motion characteristics of the target based on the energy distribution differences between the clutter, target, and noise signals. Secondly, the multi-channel EfficientNet model is designed to combine time-frequency information features in radar sum and difference channels, and further fuse multi-channel micro-motion features to achieve accurate target classification. Finally, the effectiveness of the proposed method is verified through experiments using radar measured target data. The results show that compared with other methods, the proposed method significantly improves classification accuracy with low model complexity.

Key words: multi-channel radar, classification of micro-motion targets, EfficientNet, singular value decomposition

CLC Number:

TN959.1

Xiaoyi WANG, Yunhua LUO, Zhongjun YU, Hao SUN, Xiaobei WANG. Multi-channel radar target micro-motion feature classification method based on EfficientNet[J]. Systems Engineering and Electronics, 2024, 46(9): 2988-2998.

Figures/Tables 20

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阶段	模块	输出尺寸	层数
1	Conv3×3	56×56×32	1
2	MBConv1, k3×3	56×56×16	1
3	MBConv6, k5×5	28×28×24	2
4	MBConv6, k5×5	14×14×40	2
5	MBConv6, k3×3	7×7×80	3
6	MBConv6, k5×5	7×7×112	3
7	MBConv6, k5×5	3×3×192	4
8	MBConv6, k3×3	3×3×320	1
9	Conv1×1&Pooling&FC	1×k	1

名称	设置参数
批量大小	16
优化器	随机梯度下降
优化器学习率	0.01
损失函数	交叉熵损失函数

实验	分类方法	特征	通道数	准确率/%
1	SVM	时域	1	74.19
2	MESVD-SM-SVM	时域	1	78.26
3	MESVD-SM-SVM	频域	1	74.46
4	MESVD-SM-SVM	时频域	1	78.26
5	MESVD-SM-SVM	多域	1	80.43
6	MESVD-SM-SVM	多域	2	83.70
7	STFT-EfficientNet	微多普勒	1	96.52
8	MESVD-SM-EfficientNet	微多普勒	1	97.61
9	MESVD-SM-EfficientNet	微多普勒	2	99.13

序号	尺寸	通道	准确率/%	FLOPs(×10⁹)/次
1	56×56	1	93.70	0.03
2	112×112	1	97.61	0.11
3	224×224	1	97.61	0.41
4	56×56	2	96.96	0.07
5	112×112	2	99.13	0.23
6	224×224	2	98.91	0.83

序号	网络模型	准确率/%	参数量(×10⁶)	FLOPs(×10⁹)/次
1	AlexNet	91.09	61.60	0.41
2	VggNet19	89.13	144.20	10.0
3	Resnet50	98.70	26.10	2.18
4	EfficientNet	99.13	5.80	0.23

Multi-channel radar target micro-motion feature classification method based on EfficientNet

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