采用多特征联合学习的噪声稳健HRRP识别方法

doi:10.3969/j.issn.1001-2400.2018.04.011

Abstract

Abstract:

In order to improve the recognition performance under low signal-to-noise ratio (SNR) conditions, a novel method is proposed for radar target high range resolution profiles (HRRP). This method achieves good recognition performance based on multi-feature joint learning for noisy HRRPs. The framework of this method is constructed based on sparse representation and low-rank representation, which are applied to extract the local and global features of target HRRPs. In the training stage, a feature extraction dictionary is produced based on the joint learning structured analytical discriminative dictionary method to improve the recognition performance. The cancellation method is implemented for noise suppression in the testing stage. Experimental results on the measured HRRP data demonstrate that the proposed method can significantly improve the overall recognition performance for HRRP testing samples under relatively low SNR conditions with a satisfactory real-time ability.

Key words: target recognition, high resolution range profile, noise robust, sparse representation, low-rank representation, dictionary learning

LI Long;LIU Zheng. Noise-robust multi-feature joint learning HRRP recognition method[J].Journal of Xidian University, 2018, 45(4): 57-62.

References

［1］李龙, 刘峥. 基于训练特征空间分布的雷达地面目标鉴别器设计［J］. 电子与信息学报, 2016, 88(4): 950-957.
LI Long, LIU Zheng. Identifier for Radar Ground Target Based on Distribution of Space of Training Features［J］. Journal of Electronics and Information Technology, 2016, 88(4): 950-957.
［2］ DING B Y, WEN G J, HUANG X, et al. Target Recognition in Synthetic Aperture Radar Images via Matching of Attributed Scattering Centers［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 1(7): 3334-3347.
［3］ WANG J Q, LI Y H, CHEN K. Radar High-resolution Range Profile Recognition via Geodesic Weighted Sparse Representation［J］. IET Radar, Sonar and Navigation, 2015, 9(1): 75-83.
［4］ PAN M, DU L, WANG P H, et al. Noise-robust Modification Method for Gaussian-based Models with Application to Radar HRRP Recognition［J］. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 558-562.
［5］ DU L, WANG P H, LIU H W, et al. Bayesian Spatiotemporal Multitask Learning for Radar HRRP Target Recognition［J］. IEEE Transactions on Signal Processing, 2011, 59(7): 3182-3196.
［6］ DU L, HE H, ZHAO L, et al. Noise Robust Radar HRRP Target Recognition Based on Scatterer Matching Algorithm［J］. IEEE Sensors Journal, 2016, 16(6): 1743-1753.
［7］ WANG L F, HUANG Z H, GONG Y C, et al. Ensemble Based Deep Networks for Image Super-resolution［J］. Pattern Recognition, 2017, 68(7): 191-198.
［8］孔繁锵, 卞陈鼎, 李云松, 等. 非凸稀疏低秩约束的高光谱解混方法［J］. 西安电子科技大学学报, 2016, 43(6): 116-121.
KONG Fanqiang, BIAN Chending, LI Yunsong, et al. Hyperspectral Unmixing Method Based on the Non-convex Sparse and Low-rank Constraints［J］. Journal of Xidian University, 2016, 43(6): 116-121.
［9］ WANG J, SHI D M, CHENG D S, et al. LRSR: Low-rank-sparse Representation for Subspace Clustering［J］. Neurocomputing, 2016, 214: 1026-1037.
［10］ DING B Y, WEN G J, HUANG X, et al. Data Augmentation by Multilevel Reconstruction Using Attributed Scattering Center for SAR Target Recognition［J］. IEEE Geoscience and Remote Sensing Letters, 2017, 14(6): 979-983.
［11］ ZHANG Y P, XIANG M, YANG B. Low-rank Preserving Embedding［J］. Pattern Recognition, 2017, 70(3): 112-125.
［12］ ZAMBELLI M, DEMIRISY Y. Online Multimodal Ensemble Learning Using Self-learned Sensorimotor Representations［J］. IEEE Transactions on Cognitive and Developmental Systems, 2017, 9(2): 113-126.

[1]	LI Peng,FENG Cunqian,XU Xuguang,TANG Zixiang. Ballistic target fretting classification network based on Bayesian optimization [J]. Journal of Xidian University, 2021, 48(5): 139-148.
[2]	LU Yan,LIAO Guisheng,HUANG Qingxiang. Cross-camera moving target tracking algorithm based on sparse representation [J]. Journal of Xidian University, 2021, 48(2): 197-204.
[3]	XU Bin,ZHANG Yongshun,ZHANG Qin,WANG Fuping,ZHENG Guimei. Radar HRRP target recognition based on the multiplicative RNN model [J]. Journal of Xidian University, 2021, 48(2): 49-54.
[4]	GUO Zekun,TIAN Long,HAN Ning,WANG Penghui,LIU Hongwei,CHEN Bo. Radar HRRP based few-shot target recognition with CNN-SSD [J]. Journal of Xidian University, 2021, 48(2): 7-14.
[5]	ZHAO Hui,ZHANG Le,LIU Yingli,ZHANG Jing,ZHANG Tianqi. Optimization of the low coherence and high robustness observation matrix [J]. Journal of Xidian University, 2019, 46(6): 171-178.
[6]	HE Wangpeng,HU Jie,CHEN Binqiang,LI Cheng,GUO Baolong. Sparsity-induced resonance demodulation method for blade crack detection [J]. Journal of Xidian University, 2019, 46(6): 75-80.
[7]	YANG Lei,YUE Yunze,LI Pucheng,ZHANG Tao,YANG Huan. Sparse representation of large dynamic range SAR imaging for multiple ground moving targets [J]. Journal of Xidian University, 2019, 46(5): 31-40.
[8]	WANG Hongyan,QIU Helei,PEI Tengda. Visual tracking method using discriminant dictionary learning [J]. Journal of Xidian University, 2019, 46(4): 150-158.
[9]	JIA Hairong,WANG Weimei,WANG Yan,PEI Junhua. Speech enhancement based on discriminative joint sparse dictionary alternate optimization [J]. Journal of Xidian University, 2019, 46(3): 74-81.
[10]	XU Bin,CHEN Bo,LIU Jiaqi,WANG Penghui,LIU Hongwei. Radar HRRP target recognition by the bidirectional LSTM model [J]. Journal of Xidian University, 2019, 46(2): 29-34.
[11]	YU Lihong,ZHAO Jiaxiang. Design of sparse MIMO equalizers using least angle regression [J]. Journal of Xidian University, 2019, 46(1): 73-78.
[12]	WANG Chuanchuan,ZENG Yonghu,FU Weihong,WANG Liandong. Estimation algorithm for an underdetermined mixing matrix based on maximum density point searching [J]. Journal of Xidian University, 2019, 46(1): 106-111.
[13]	LIU Shuai;MA Wenping;YANG Shuyuan;CHEN Puhua. Multispectral imagery denoising using hierarchical dictionary learning with spatial-spectral information [J]. Journal of Xidian University, 2017, 44(4): 174-180.
[14]	GUO Xin;JIA Hairong;WANG Dong. Speech enhancement using the improved K-SVD algorithm by subspace [J]. Journal of Xidian University, 2016, 43(6): 109-115.
[15]	KONG Fanqiang;BIAN Chending;LI Yunsong;GUO Wenjun. Hyperspectral unmixing method based on the non-convex sparse and low-rank constraints [J]. Journal of Xidian University, 2016, 43(6): 116-121.

Noise-robust multi-feature joint learning HRRP recognition method

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 10