基于一维堆叠池化融合卷积自编码器的HRRP目标识别方法

doi:10.12305/j.issn.1001-506X.2021.12.15

Abstract

Abstract:

Aiming at the problem of feature extraction and recognition in high resolution range profile (HRRP) target recognition, a recognition method based on one-dimensional stacked pooling fusion convolutional autoencoder (1D SPF-CAE) is proposed in this paper. Firstly, a one-dimensional pooling fusion convolutional autoencoder (1D PF-CAE) is constructed. In the encoding stage, the maximum pooling and average pooling are used to extract different encoding features and fuse them to extract the structural features of HRRP. Then, multiple 1D PF-CAEs are stacked to form 1D SPF-CAE. Finally, the network is fine-tuned using label data to realize HRRP target recognition. And the AdaBound algorithm is used to optimize network training for improving the recognition performance. The experimental results based on the simulated data of the target in the middle part of the trajectory show that the method has strong feature extraction capability, and has high accuracy and robustness for HRRP target recognition.

Key words: radar automatic target recognition, high resolution range profile, convolution autoencoder, feature extraction, pooling fusion

CLC Number:

TP183

Guoling ZHANG, Chongming WU, Rui LI, Jie LAI, Qian XIANG. HRRP target recognition method based on one-dimensional stacked pooling fusion convolutional autoencoder[J]. Systems Engineering and Electronics, 2021, 43(12): 3533-3541.

Figures/Tables 13

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References 24

1	LIAO K , SI J X , ZHU F Q , et al. Radar HRRP target recognition based on concatenated deep neural networks[J]. IEEE Access, 2018, 6, 29211- 29218. doi: 10.1109/ACCESS.2018.2842687
2	LI L , LIU Z , LI T . Radar high resolution range profile recognition via multi-SV method[J]. Journal of Systems Engineering and Electronics, 2017, 28 (5): 879- 889. doi: 10.21629/JSEE.2017.05.07
3	LIU J , FANG N , XIE Y J , et al. Scale-space theory-based mutil-scale feature for aircraft classification using HRRP[J]. Electronics Letters, 2016, 52 (6): 475- 477. doi: 10.1049/el.2015.3583
4	李龙, 刘峥. 基于核主分量相关判别分析特征提取方法的目标HRRP识别[J]. 电子与信息学报, 2018, 40 (1): 173- 180.
	LI L , LIU Z . Kernel principal component correlation and discrimination analysis feature extraction method for target HRRP recognition[J]. Journal of Electronics & Information Technology, 2018, 40 (1): 173- 180.
5	ZHOU D Y . Radar target HRRP recognition based on reconstructive and discriminative dictionary learning[J]. Signal Processing, 2016, 126, 52- 64. doi: 10.1016/j.sigpro.2015.12.006
6	GUO Y , XIAO H T , KAN Y Z , et al. Learning using privileged information for HRRP-based radar target recognition[J]. IET Signal Processing, 2018, 12 (2): 188- 197. doi: 10.1049/iet-spr.2016.0625
7	PAN M , JIANG J , KONG Q P , et al. Radar HRRP target recognition based on T-SNE segmentation and discriminant deep belief network[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (9): 1609- 1613. doi: 10.1109/LGRS.2017.2726098
8	杨予昊, 孙晶明, 虞盛康, 等. 基于卷积神经网络的高分辨距离像目标识别[J]. 现代雷达, 2017, 39 (12): 28- 32.
	YANG Y H , SUN J M , YU S K , et al. High resolution range profile target recognition based on convolutional neural network[J]. Modern Radar, 2017, 39 (12): 28- 32.
9	FENG B , CHEN B , LIU H W . Radar HRRP target recognition with deep networks[J]. Pattern Recognition, 2017, 61, 379- 393. doi: 10.1016/j.patcog.2016.08.012
10	ZHAO F X , LIU Y X , HUO K , et al. Radar HRRP target recognition based on stacked autoencoder and extreme learning machine[J]. Sensors, 2018, 18 (1): 173- 187.
11	CHEN M , SHI X B , ZHANG Y , et al. Deep features learning for medical image analysis with convolutional autoencoder neural network[J]. IEEE Trans.on Big Data, 2017, doi: 10.1109/TBDATA.2017.2717439
12	HUANG H , HU X T , ZHAO Y , et al. Modeling task fMRI data via deep convolutional autoencoder[J]. IEEE Trans.on Medical Imaging, 2017, 37 (7): 1551- 1561.
13	LUO W , LI J , YANG J , et al. Convolutional sparse autoencoders for image classification[J]. IEEE Trans.on Neural Networks, 2018, 29 (7): 3289- 3294.
14	DU B , XIONG W , WU J , et al. Stacked convolutional denoising auto-encoders for feature representation[J]. IEEE Trans.on Cybernetics, 2017, 47 (4): 1017- 1027. doi: 10.1109/TCYB.2016.2536638
15	LUO L, XIONG Y, LIU Y, et al. Adaptive gradient methods with dynamic bound of learning rate[EB/OL]. [2020-07-24]. https://arxiv.org/abs/1902.09843?context=cs.LG.
16	HOU B R , YAN R Q . Convolutional auto-encoder model for finger-vein verification[J]. IEEE Trans.on Instrumentation & Measurement, 2020, 69 (5): 2067- 2074.
17	严国萍, 陈禹, 李雨冲, 等. 基于一维堆叠卷积自编码器的分布式应变裂缝检测[J]. 计算机系统应用, 2020, 29 (1): 144- 150.
	YAN G P , CHEN Y , LI Y C , et al. Distributed strain crack detection based on one-dimensional stacked convolutional autoencoder[J]. Computer Systems & Applications, 2020, 29 (1): 144- 150.
18	张海利, 王普, 高学金, 等. 基于批次图像化的卷积自编码故障监测方法[J]. 控制与决策, 2021, 36 (6): 1361- 1367.
	ZHANG H L , WANG P , GAO X J , et al. Fault detection of batch image-based convolutional autoencoder[J]. Control and Decision, 2021, 36 (6): 1361- 1367.
19	罗畅, 王洁, 王鹏飞, 等. 卷积自编码器中粗粒度池化特征提取研究[J]. 电子学报, 2017, 45 (10): 2390- 2401. doi: 10.3969/j.issn.0372-2112.2017.10.012
	LUO C , WANG J , WANG P F , et al. Coarse-grained pooled features learning in convolutional autoencoders[J]. Acta Electronica Sinica, 2017, 45 (10): 2390- 2401. doi: 10.3969/j.issn.0372-2112.2017.10.012
20	WAHLBECK K , TUUNAINEN A , AHOKAS A , et al. Dropout rates in randomised antipsychotic drug triais[J]. Psychopharmacology, 2001, 155 (3): 230- 233. doi: 10.1007/s002130100711
21	BENGIO Y , LAMBLIN P , DAN P , et al. Greedy layer-wise training of deep networks[J]. Proceedings of the Advances in Neural Information Processing Systems, 2006, 19, 153- 160.
22	VINCENT P , LAROCHELLE H , LAJOIE I , et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11 (12): 3371- 3408.
23	LE Q V, NGIAM I, COATES A, et al. On optimization methods for deep learning[C]//Proc. of the 28th International Conference on Machine Learning, 2011.
24	张国令, 王晓丹, 李睿, 等. 基于栈式降噪稀疏自编码器的极限学习机[J]. 计算机工程, 2020, 46 (9): 61- 67.
	ZHANG G L , WANG X D , LI R , et al. Stacked denoising sparse autoencoder based extreme learning machine[J]. Computer Engineering, 2020, 46 (9): 61- 67.

网络层	输入	核数	核/窗口	步长	输出
C1	(256×1)×1	32	3×1	1	(256×1)×32
C1	(256×1)×1	32	3×1	1	(256×1)×32
P1_max	(256×1)×32	—	2×1	2	(128×1)×32
P1_avg	(256×1)×32	—	2×1	2	(128×1)×32
P1	—	—	—	—	(128×1)×64
C2	(128×1)×64	48	3×1	1	(128×1)×48
C2	(128×1)×48	48	3×1	1	(128×1)×48
P2_max	(128×1)×48	—	2×1	2	(64×1)×48
P2_avg	(128×1)×48	—	2×1	2	(64×1)×48
P2	—	—	—	—	(64×1)×96
C3	(64×1)×96	64	3×1	1	(64×1)×64
C3	(64×1)×64	64	3×1	1	(64×1)×64
P3_max	(64×1)×64	—	2×1	2	(32×1)×64
P3_avg	(64×1)×64	—	2×1	2	(32×1)×64
P3	—	—	—	—	(32×1)×128
Flatten	(32×1)×128	—	—	—	4 096×1
全连接	4 096×1	—	—	—	400×1
Softmax	400×1	—	—	—	5×1

数据集	平均池化	最大池化	平均+最大池化
A	95.45	95.91	96.49
B	94.89	95.27	95.84
C	94.02	93.83	94.92

堆叠层数	各层卷积核数	识别准确率/%
1	32	93.86
2	32-48	95.63
3	32-48-64	96.49
4	32-48-64-80	96.57
5	32-48-64-80-96	96.01
6	32-48-64-80-96-112	95.14

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HRRP target recognition method based on one-dimensional stacked pooling fusion convolutional autoencoder

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