IoT-based critical infrastructure enabled radar information fusion. (March 2022)

• Record Type: Journal Article
• Authors: Yang, Jiachen; Zhang, Zhuo; Mao, Wei; Sun, Yiwen; Bao, Yongjun; Song, Houbing
• Is Part Of: Computers & electrical engineering. Volume 98(2022)
• Publication Date: 2022-03
• Subjects: Radar Cross Section -- Parameter estimation -- Deep Learning -- Data fusion -- Attention mechanism; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107723
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: With the development of Internet of things (IoT) and critical infrastructure in modern industry, many technologies are facing serious challenges brought by the change of application. For instance, the real-time radar signal processing is based on the IoT and infrastructure. In particular, parameter estimation based on Radar Cross Section (RCS) from narrow-band radar requires sufficient data and effective algorithm. In this paper, the geometric parameters of the target are estimated by the Deep Learning method. Moreover, considering the difference of the information collected by different radars, a data fusion method based on channel attention mechanism is proposed for multi-site RCS time-series, which greatly improves the performance of parameters estimation.