Cognitive dual coprime frequency diverse array MIMO radar network for target discrimination and main-lobe interference mitigation.

Khan, Umair Hafeez; Basit, Abdul; Khan, Wasim; Jadoon, Muhammad Adeel Khan; Baig, Nauman Anwar

Abstract

The authors propose a novel dual coprime frequency diverse array (FDA) multiple input multiple output (DCFDA-MIMO) radar network design, empowered by cognitive capabilities, aimed at target discrimination and mitigation of interference present in the standalone radar systems. That is, the proposed DCFDA-MIMO design capitalises on the complementary advantages of FDAs for target discrimination and coprime arrays for enhanced resolution, resulting in superior performance. Additionally, the proposed DCFDA-MIMO network employs a 2D multiple signal classification algorithm to achieve high-resolution target localisation. By incorporating cognitive techniques based on the action-perception cycle, the proposed approach demonstrates notable improvements in multiple target detection and tracking accuracy with fewer number of antenna elements as compared to existing techniques. Furthermore, it enhances individual radar beamforming performance for interference suppression and true target detection without prior information.

Citation

KHAN, U.H., BASIT, A., KHAN, W., JADOON, M.A.K. and BAIG, N.A. [2024]. Cognitive dual coprime frequency diverse array MIMO radar network for target discrimination and main-lobe interference mitigation. IET radar, sonar and navigation [online], Early View. Available from: https://doi.org/10.1049/rsn2.12595

• Journal Article Type: Article
• Acceptance Date: May 21, 2024
• Online Publication Date: Jun 12, 2024
• Deposit Date: Jun 24, 2024
• Publicly Available Date: Jun 24, 2024
• Journal: IET radar, sonar and navigation
• Electronic ISSN: 1751-8792
• Publisher: Institution of Engineering and Technology (IET)
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1049/rsn2.12595
• Keywords: Antenna arrays; MIMO radar; Phased array radar; Radar
• Public URL: https://rgu-repository.worktribe.com/output/2299016