Cognitive radar for the localization of RFID transponders in dense multipath environments

High-accuracy localization remains a much desired but elusive feature for passive radio transponders as used in radio-frequency identification (RFID). We believe that the principle of cognitive radar can overcome the fundamental physical limitations hindering its implementation. We propose to jointly employ a narrowband radio to interrogate the transponders and an adaptive (ultra) wideband backscatter radio for the target tracking and for actuating, sensing, and learning the radio environment. This paper explores system model and key processing perception-action cycle steps of such a cognitive secondary radar. At its core is a perception-action cycle, which consists of transmitter and receiver-side environment models for representing radio channel conditions and Bayesian trackers for the target states. Multipath is exploited to improve the robustness and to make optimum use of the radar's sensing capabilities. Feedback information is derived from the Cramέr-Rao lower bound on the position error. Initial results are presented as a basic proof of principle.