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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.