Hierarchical fully adaptive radar. Issue 12 (9th November 2018)

• Record Type: Journal Article
• Authors: Mitchell, Adam E.; Smith, Graeme E.; Bell, Kristine L.; Duly, Andrew J.; Rangaswamy, Muralidhar
• Is Part Of: IET radar, sonar & navigation. Volume 12:Issue 12(2018)
• Page Start: 1371
• Page End: 1379
• Publication Date: 2018-11-09
• Subjects: sensor fusion -- adaptive radar -- cognition -- target tracking -- radar signal processing; hierarchical fully adaptive radar -- animal cognition -- cognitive radars -- radar performance -- standard systems -- fully adaptive radar framework -- generalised approach -- single cognitive perception–action cycle -- radar systems -- complex adaptive problems -- multiple perception–action cycles -- hierarchical FAR framework -- HFAR framework -- single‐target tracking -- sensor fusion problem; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2018.5339
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : By emulating the cognitive perception–action cycle believed to be at the core of animal cognition, cognitive radars promise to improve radar performance over standard systems. The fully adaptive radar (FAR) framework provides a generalised approach to implementing a single cognitive perception–action cycle for radar systems, but complex adaptive problems necessitate the interaction of multiple perception–action cycles. This study describes the general form of the hierarchical FAR (HFAR) framework. The HFAR framework is applied to a single‐target tracking, sensor fusion problem, and real‐time experimental results demonstrate the efficacy of the proposed architecture for handling problems of varying scales in a consistent, adaptive fashion.