Radio Frequency Identification with Compressed Sensing

My work deals with the quick and reliable identification of a multitude of RFID tags that may identify, e.g., products in a store, parts on a conveyor belt, or items in a warehouse. In the widely used frame slotted aloha protocol, the tags randomly choose a slot of a frame to respond in, and only collision-free slots chosen by a single tag can be resolved. In my proposed approach, the tags deliberately respond simultaneously, and collisions are exploited. I explain how this process is cast as a compressed sensing problem, and I demonstrate how the computationally efficient Approximate Message Passing (AMP) algorithm can be employed for recovery.

I will begin with a brief introduction to the topic, followed by the basic problem formulation. I then continue by presenting an advanced Bayesian AMP approach that strongly improves the recovery by exploiting prior knowledge. The advanced approach is based on the multiple measurement vector problem that incorporates joint sparsity. Finally, I present proof-of-concept measurements that demonstrate the viability of my approach. Overall, compressed sensing RFID features a strongly increased identification speed and noise robustness.