Open-set HRRP Target Recognition Method Based on Joint Dynamic Sparse Representation

LIU Shengqi; ZHANG Huiqiang; TENG Shuhua; QU Shuang; WU Zhongjie

doi:10.11999/JEIT221284

Volume 45 Issue 11

Nov. 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(11): 4101-4109

LIU Shengqi, ZHANG Huiqiang, TENG Shuhua, QU Shuang, WU Zhongjie. Open-set HRRP Target Recognition Method Based on Joint Dynamic Sparse Representation[J]. Journal of Electronics & Information Technology, 2023, 45(11): 4101-4109. doi: 10.11999/JEIT221284

Citation:

LIU Shengqi, ZHANG Huiqiang, TENG Shuhua, QU Shuang, WU Zhongjie. Open-set HRRP Target Recognition Method Based on Joint Dynamic Sparse Representation[J]. Journal of Electronics & Information Technology, 2023, 45(11): 4101-4109. doi: 10.11999/JEIT221284

Citation:

PDF( 3423 KB)

Open-set HRRP Target Recognition Method Based on Joint Dynamic Sparse Representation

doi: 10.11999/JEIT221284

1.
National Key Laboratory of Science and Technology on Automatic Target Recognition, National University of Defense Technology, Changsha 410073, China
2.
College of Electronic Information, Hunan First Normal University, Changsha 410205, China

Funds: The National Natural Science Foundation of China (62001486, 62201587), Hunan Provincial Natural Science Foundation Project (2023JJ0185), The Key Scientific Research Project of Hunan Provincial Department of Education (22A0640)

Received Date: 2022-10-10
Rev Recd Date: 2023-10-12

Available Online: 2023-10-18

Publish Date: 2023-11-28

Abstract

Abstract

Focusing on the issue of multi-view High-Resolution Range Profile (HRRP) target recognition in an open set, a novel method based on Joint Dynamic Sparse Representation (JDSR) is presented. First, JDSR is used to solve the reconstruction error of multi-view HRRP on the over completed dictionary. The reconstruction error trails of matched and unmatched categories are modeled using Extreme Value Theory (EVT), and subsequently, the open-set recognition problem is transformed into a hypothesis test problem. The reconstruction error is used to determine candidate classes during the identification phase. The matched and nonmatched class scores are obtained based on the confidence level of the tail distribution, and their weighted sum is used to decide whether the inputs are from nonlibrary categories or candidate classes. The input HRRPs are obtained from the same target and can be used as useful information to improve recognition performance. The proposed method can effectively use such prior information for performance enhancement under the open-set condition. Moreover, performance can remain robust under multiview data acquisition scenarios. The HRRP data generated from MSTAR chips are used for the identification experiments, and the results reveal that the proposed method performs considerably better than some state-of-the-art methods.
- Open-Set Recognition (OSR),
- Joint Dynamic Sparse Representation (JDSR),
- Extreme value theory,
- High-Resolution Range Profile (HRRP)

FullText(HTML)

References(21)

References

[1]	付哲泉, 李尚生, 李相平, 等. 基于高效可扩展改进残差结构神经网络的舰船目标识别技术[J]. 电子与信息学报, 2020, 42(12): 3005–3012. doi: 10.11999/JEIT190913 FU Zhequan, LI Shangsheng, LI Xiangping, et al. Ship target recognition based on highly efficient scalable improved residual structure neural network[J]. Journal of Electronics & Information Technology, 2020, 42(12): 3005–3012. doi: 10.11999/JEIT190913
[2]	贺丰收, 何友, 刘准钆, 等. 卷积神经网络在雷达自动目标识别中的研究进展[J]. 电子与信息学报, 2020, 42(1): 119–131. doi: 10.11999/JEIT180899 HE Fengshou, HE You, LIU Zhunga, et al. Research and development on applications of convolutional neural networks of radar automatic target recognition[J]. Journal of Electronics & Information Technology, 2020, 42(1): 119–131. doi: 10.11999/JEIT180899
[3]	GÖRNITZ N, LIMA L A, MÜLLER K R, et al. Support vector data descriptions and k-means clustering: One class?[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(9): 3994–4006. doi: 10.1109/TNNLS.2017.2737941
[4]	PÉREZ G J, SANTIBÁÑEZ M, VALDOVINOS R M, et al. On-line learning with reject option[J]. IEEE Latin America Transactions, 2018, 16(1): 279–286. doi: 10.1109/TLA.2018.8291485
[5]	GEIFMAN Y and EL-YANIV R. Selective classification for deep neural networks[C]. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 4885–4894.
[6]	SCHEIRER W J, DE REZENDE ROCHA A, SAPKOTA A, et al. Toward open set recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(7): 1757–1772. doi: 10.1109/TPAMI.2012.256
[7]	SCHEIRER W J, JAIN L P, and BOULT T E. Probability models for open set recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(11): 2317–2324. doi: 10.1109/TPAMI.2014.2321392
[8]	JAIN L P, SCHEIRER W J, and BOULT T E. Multi-class open set recognition using probability of inclusion[C]. Proceedings of the 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 393–409. doi: 10.1007/978-3-319-10578-9_26.
[9]	ZHANG He and PATEL V M. Sparse representation-based open set recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1690–1696. doi: 10.1109/TPAMI.2016.2613924
[10]	BENDALE A and BOULT T. Towards open world recognition[C]. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 1893–1902. doi: 10.1109/CVPR.2015.7298799.
[11]	SADHUKHAN P. Can reverse nearest neighbors perceive unknowns?[J]. IEEE Access, 2020, 8: 6316–6343. doi: 10.1109/ACCESS.2019.2963471
[12]	SHU Lei, XU Hu, and LIU Bing. DOC: Deep open classification of text documents[C]. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 2017: 2911–2916. doi: 10.18653/v1/D17-1314.
[13]	OZA P and PATEL V M. C2AE: Class conditioned auto-encoder for open-set recognition[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019: 2302–2311. doi: 10.1109/CVPR.2019.00241.
[14]	ZENG Zhiqiang, SUN Jinping, XU Congan, et al. Unknown SAR target identification method based on feature extraction network and KLD–RPA joint discrimination[J]. Remote Sensing, 2021, 13(15): 2901. doi: 10.3390/rs13152901
[15]	RUDD E M, JAIN L P, SCHEIRER W J, et al. The extreme value machine[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3): 762–768. doi: 10.1109/TPAMI.2017.2707495
[16]	PAPPADÀ R, PERRONE E, DURANTE F, et al. Spin-off extreme value and Archimedean copulas for estimating the bivariate structural risk[J]. Stochastic Environmental Research and Risk Assessment, 2016, 30(1): 327–342. doi: 10.1007/s00477-015-1103-8
[17]	FALK M and STUPFLER G. An offspring of multivariate extreme value theory: The max-characteristic function[J]. Journal of Multivariate Analysis, 2017, 154: 85–95. doi: 10.1016/j.jmva.2016.10.007
[18]	刘盛启. 基于高分辨距离像的特征提取与识别增强技术研究[D]. [博士论文], 国防科学技术大学, 2016. LIU Shengqi. Research on feature extraction and recognition performance enhancement algorithms based on high range resolution profile[D]. [Ph. D. dissertation], National University of Defense Technology, 2016.
[19]	AL-LABADI L and ZAREPOUR M. Two-sample Kolmogorov-Smirnov test using a Bayesian nonparametric approach[J]. Mathematical Methods of Statistics, 2017, 26(3): 212–225. doi: 10.3103/S1066530717030048
[20]	刘振. 基于稀疏表示的图像分类若干新方法研究[D]. [博士论文], 江南大学, 2021. doi: 10.27169/d.cnki.gwqgu.2021.001957. LIU Zhen. Research on new methods of image classification via sparse representation[D]. [Ph. D. dissertation], Jiangnan University, 2021. doi: 10.27169/d.cnki.gwqgu.2021.001957.
[21]	缪吴霞. SAR图像回波反演及典型目标特征提取方法研究[D]. [硕士论文], 哈尔滨工业大学, 2019. doi: 10.27061/d.cnki.ghgdu.2019.001165. MIAO Wuxia. Research on SAR image echo inversion and typical target featuer extraction method[D]. [Master dissertation], Harbin Institute of Technology, 2019. doi: 10.27061/d.cnki.ghgdu.2019.001165.