RFID tag acquisition via compressed sensing

Exploiting joint sparsity in compressed sensing-based RFID

“…These algorithms enable efficient recovery with low computational complexity, while maintaining excellent recovery performance. Note that the CS-RFID schemes presented in [9][10][11] utilize computationally demanding convex optimization algorithms, while the schemes in [12][13][14] employ an efficient AMP algorithm that iteratively solves the least absolute shrinkage and selection operator (LASSO) [24]. In this work, we utilize a powerful extension of the AMP algorithm-termed BASSAMP and introduced in [15]-that allows to leverage prior knowledge and joint sparsity.…”

“…All CS-based identification protocols [9][10][11][12][13][14] share a common problem formulation that can be interpreted as a compressed sensing measurement similar to (1). Let us recapitulate how this formulation is obtained.…”

“…Our goal is to recover x from z knowing S, because the locations of the nonzero entries in x tell us which signatures have been chosen by the tags. This information is used to directly identify the tags [12,14] or to establish a handshake mechanism in order to read out the tag information in an additional step [9][10][11]13].…”

“…The compressed sensing (CS)-based identification protocols [9][10][11][12][13][14], on the other hand, cope with simultaneously responding tags and exploit collisions. This bears several advantages over FSA-based schemes, as reported in, e.g., [9,14].…”

“…While the number of activated tags is assumed to be known or has to be estimated in an additional step in the current state-of-the-art protocols [9][10][11][12][13][14], we demonstrate how our approach eliminates the need for such prior knowledge by exploiting a novel signal support estimation scheme. Note that we do not present a new protocol but an improvement of the CS formulation that is applicable to all present protocols [9][10][11][12][13][14]. The benefits of our novel formulation comprise a strongly increased identification throughput, an increased noise robustness, and an implicit estimation of the number of activated tags.…”