We propose a novel scheme to improve compressed sensing (CS)-based radio frequency identification (RFID) by\nexploiting multiple measurement vectors. Multiple measurement vectors are obtained by employing multiple receive\nantennas at the reader or by separation into real and imaginary parts. Our problem formulation renders the\ncorresponding signal vectors jointly sparse, which in turn enables the utilization of CS. Moreover, the joint sparsity is\nexploited by an appropriate algorithm.\nWe formulate the multiple measurement vector problem in CS-based RFID and demonstrate how a joint recovery of\nthe signal vectors strongly improves the identification speed and noise robustness. The key insight is as follows:\nMultiple measurement vectors allow to shorten the CS measurement phase, which translates to shortened tag\nresponses in RFID. Furthermore, the new approach enables robust signal support estimation and no longer requires\nprior knowledge of the number of activated tags.
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