基于双谱-谱图特征和深度卷积神经网络的HRRP目标识别方法

doi:10.3969/j.issn.1001-506X.2020.08.09

Abstract

Abstract:

Aiming at the key problem of effective representation and extraction of features in radar high resolution range profile (HRRP) target recognition, a recognition method based on bispectrum-spectrogram feature and deep convolution neural network (DCNN) is proposed. Firstly, this method extracts the bispectrum-spectrogram feature representation of HRRP as the input of the CNN. Then, the deep and essential features are extracted by the network training and the radar targets can be recognized. Finally, the recognition results of different feature representations are compared. The experimental results of the satellite target measured data show that the method can recognize radar target effectively and accurately, and the bispectrum-spectrogram feature has better recognition accuracy and noise robustness than other commonly used HRRP feature representations.

Key words: radar automatic target recognition (RATR), high resolution range profile (HRRP), bispectrum-spectrogram feature, deep convolution neural network (DCNN)

CLC Number:

TN957.51

Wang LU, Yasheng ZHANG, Can XU, Caiyong LIN. HRRP target recognition method based on bispectrum-spectrogram feature and deep convolutional neural network[J]. Systems Engineering and Electronics, 2020, 42(8): 1703-1709.

Figures/Tables 9

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References 16

1	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
2	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
3	GUO Y , XIAO H T , KAN Y Z , et al. Learning using privileged information for HRRP-based radar target recognition[J]. IET Signal Processing, 2017, 12 (2): 188- 197.
4	GUO Y , XIAO H T . Genetic algorithm-tuned adaptive pruning SVDD method for HRRP-based radar target recognition[J]. International Journal of Remote Sensing, 2018, 39 (11): 3407- 3428. doi: 10.1080/01431161.2018.1441566
5	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
6	郭尊华, 李少洪. 基于积谱的雷达目标高分辨率距离像识别[J]. 北京航空航天大学学报, 2006, 32 (4): 404- 406. doi: 10.3969/j.issn.1001-5965.2006.04.008
	GUO Z H , LI S H . Product spectrum based target recognition using high resolution radar range profiles[J]. Journal of Beijing University of Aeronautics and Astronautics, 2006, 32 (4): 404- 406. doi: 10.3969/j.issn.1001-5965.2006.04.008
7	WAN J W, CHEN B, XU B, et al. Convolutional neural networks for radar HRRP target recognition and rejection[J]. EURASIP Journal on Advances in Signal Processing, 2019.DOI: 10.1186/s13634-019-0603-y.
8	但波, 姜永华, 李敬军, 等. 雷达高分辨距离像自适应角域划分方法[J]. 系统工程与电子技术, 2014, 36 (11): 2178- 2185. doi: 10.3969/j.issn.1001-506X.2014.11.11
	DAN B , JIANG Y H , LI J J , et al. Adaptive angular-sector segmentation method for radar HRRP[J]. Systems Engineering and Electronics, 2014, 36 (11): 2178- 2185. doi: 10.3969/j.issn.1001-506X.2014.11.11
9	MAHDI H , MOJTABA R , MOHAMMAD M N . A new polynomial segment model for radar target recognition using Gibbs sampling approach[J]. IET Signal Processing, 2017, 11 (3): 285- 294.
10	殷和义, 郭尊华. 一维卷积神经网络用于雷达高分辨率距离像识别[J]. 电讯技术, 2018, 58 (10): 11- 16.
	YIN H Y , GUO Z H . Radar HRRP target recognition with one-dimensional CNN[J]. Telecommunication Engineering, 2018, 58 (10): 11- 16.
11	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): 24- 28.
	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): 24- 28.
12	LU C Y , ZHANG S C , XU S C , et al. A deep learning-based satellite target recognition method using radar data[J]. Sensors, 2019, 19 (9): 2008- 2031. doi: 10.3390/s19092008
13	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.
14	WEI Y , ZHAO Y , LU C , et al. Cross-modal retrieval with CNN visual features: a new baseline[J]. IEEE Trans.on Cybernetics, 2017, 47 (2): 449- 460.
15	SUN X , WU P , HOI S C H . Face detection using deep learning: an improved faster RCNN approach[J]. Neurocomputing, 2018, 299, 42- 50. doi: 10.1016/j.neucom.2018.03.030
16	陈昌孝, 何明浩, 朱元清, 等. 基于双谱分析的雷达辐射源个体特征提取[J]. 系统工程与电子技术, 2008, 30 (6): 1046- 1049. doi: 10.3321/j.issn:1001-506X.2008.06.011
	CHEN C X , HE M H , ZHU Y Q , et al. Specific emitter features extraction based on bispectrum and Walsh transform[J]. Systems Engineering and Electronics, 2008, 30 (6): 1046- 1049. doi: 10.3321/j.issn:1001-506X.2008.06.011

特征表示	阶段数目	平均识别率/%						总体识别率/%
特征表示	阶段数目	No.1	No.2	No.3	No.4	No.5	No.6	总体识别率/%
双谱-谱图特征	1	96.5	95.5	97.3	99.6	99.9	99.1	98.0
	2	97.2	96.2	97.8	99.6	99.9	99.2	98.3
	3	97.3	96.7	97.5	99.6	99.9	99.1	98.2
谱图特征	1	93.1	92.2	95.9	99.2	99.9	96.8	96.2
	2	94.0	91.8	95.5	99.2	99.9	97.2	96.2
	3	94.4	92.3	95.7	99.7	99.9	97.4	96.5
双谱特征	1	76.6	73.9	82.0	98.7	93.2	91.4	86.2
	2	83.7	77.1	87.9	99.5	96.7	94.8	89.6
	3	79.5	75.0	85.8	99.5	96.8	95.6	88.7

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[13]	LI Bin, LI Hui. HRRP feature extraction based on mixtures of probabilistic principal component analysis [J]. Systems Engineering and Electronics, 2017, 39(1): 1-7.
[14]	WU Jia-ni, CHEN Yong-guang, FENG De-jun, WANG Xue-song. Target recognition for polarimetric HRRP based on pre-classification and model matching [J]. Systems Engineering and Electronics, 2016, 38(9): 1969-1974.
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HRRP target recognition method based on bispectrum-spectrogram feature and deep convolutional neural network

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