Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion

Zhang, Wei; Yang, Jian; Li, Qiang; Lin, Jingran; Shao, Huaizong; Sun, Guomin

doi:10.3390/rs15010121

Open AccessArticle

Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion

by

Wei Zhang

^1,2,

Jian Yang

³,

Qiang Li

¹,

Jingran Lin

¹,

Huaizong Shao

¹ and

Guomin Sun

^1,*

¹

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

²

Science and Technology on Electronic Information Control Laboratory, Chengdu 610036, China

³

Northern Institute of Electronic Equipment of China, Beijing 100089, China

^*

Author to whom correspondence should be addressed.

Remote Sens. 2023, 15(1), 121; https://doi.org/10.3390/rs15010121

Submission received: 7 November 2022 / Revised: 12 December 2022 / Accepted: 16 December 2022 / Published: 26 December 2022

(This article belongs to the Special Issue Radar Techniques and Imaging Applications)

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Abstract

Electromagnetic data annotation is one of the most important steps in many signal processing applications, e.g., radar signal deinterleaving and radar mode analysis. This work considers cooperative electromagnetic data annotation from multiple reconnaissance receivers/platforms. By exploiting the inherent correlation of the electromagnetic signal, as well as the correlation of the observations from multiple receivers, a low-rank matrix recovery formulation is proposed for the cooperative annotation problem. Specifically, considering the measured parameters of the same emitter should be roughly the same at different platforms, the cooperative annotation is modeled as a low-rank matrix recovery problem, which is solved iteratively either by the rank minimization method or the maximum-rank decomposition method. A comparison of the two methods, with the traditional annotation method on both the synthetic and real data, is given. Numerical experiments show that the proposed methods can effectively recover missing annotations and correct annotation errors.

Keywords: data annotation completion; radar reconnaissance data; low-rank matrix recovery

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MDPI and ACS Style

Zhang, W.; Yang, J.; Li, Q.; Lin, J.; Shao, H.; Sun, G. Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion. Remote Sens. 2023, 15, 121. https://doi.org/10.3390/rs15010121

AMA Style

Zhang W, Yang J, Li Q, Lin J, Shao H, Sun G. Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion. Remote Sensing. 2023; 15(1):121. https://doi.org/10.3390/rs15010121

Chicago/Turabian Style

Zhang, Wei, Jian Yang, Qiang Li, Jingran Lin, Huaizong Shao, and Guomin Sun. 2023. "Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion" Remote Sensing 15, no. 1: 121. https://doi.org/10.3390/rs15010121

APA Style

Zhang, W., Yang, J., Li, Q., Lin, J., Shao, H., & Sun, G. (2023). Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion. Remote Sensing, 15(1), 121. https://doi.org/10.3390/rs15010121

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Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion

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