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Ultra-wideband cognitive interrogator network: Adaptive illumination with active sensors for target localisation

Ultra-wideband cognitive interrogator network: Adaptive illumination with active sensors for target localisation

Chen, Y. and Rapajic, P. (2010) Ultra-wideband cognitive interrogator network: Adaptive illumination with active sensors for target localisation. Communications, IET, 4 (5). pp. 573-584. ISSN 1751 8628 (doi:10.1049/iet-com.2009.0495)

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Abstract

The authors explore the potential application of cognitive interrogator network (COIN) in remote monitoring of mobile subjects in domestic environments, where the ultra-wideband radio frequency identification (UWB-RFID) technique is considered for accurate target localisation. The authors first present the COIN architecture in which the central base station (BS) continuously and intelligently customises the illumination modes of the distributed interrogators in response to the system's changing knowledge of the channel condition and subject movement. Subsequently, the analytical results of the locating probability and time-of-arrival (TOA) estimation uncertainty for a large-scale COIN with randomly distributed active sensors are derived based upon the implemented cognitive intelligence. As an important component to facilitate the adaptive illumination of the environment, the sequential-hypothesis-testing framework is proposed to estimate the tag antenna orientation. Finally, numerical examples are used to demonstrate the key effects of the proposed cognitive schemes on the system performance.

Item Type: Article
Uncontrolled Keywords: cognitive interrogator network (COIN), ultra-wideband radio frequency identification (UWB-RFID), sequential-hypothesis-testing framework
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Pre-2014 Departments: School of Engineering
School of Engineering > Department of Computer & Communications Engineering
Related URLs:
Last Modified: 14 Oct 2016 09:19
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/7651

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