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

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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

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

doi: 10.11999/JEIT221284 cstr: 32379.14.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 (2023JJ30185), 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: 2023-11-10

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)

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References(21)

References

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