Name
Abstract | ||
|---|---|---|
We propose a novel scheme to improve compressed sensing (CS)-based radio frequency identification (RFID) by exploiting multiple measurement vectors. Multiple measurement vectors are obtained by employing multiple receive antennas at the reader or by separation into real and imaginary parts. Our problem formulation renders the corresponding signal vectors jointly sparse, which in turn enables the utilization of CS. Moreover, the joint sparsity is exploited by an appropriate algorithm. We formulate the multiple measurement vector problem in CS-based RFID and demonstrate how a joint recovery of the signal vectors strongly improves the identification speed and noise robustness. The key insight is as follows: Multiple measurement vectors allow to shorten the CS measurement phase, which translates to shortened tag responses in RFID. Furthermore, the new approach enables robust signal support estimation and no longer requires prior knowledge of the number of activated tags. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1186/s13639-016-0025-y | EURASIP J. Emb. Sys. |
Keywords | Field | DocType |
Compressed sensing, Approximate message passing, Joint sparsity, Multiple measurement vectors, Backscatter communication, Multiple access | Computer science,Real-time computing,Robustness (computer science),Speech recognition,Computer engineering,Radio-frequency identification,Compressed sensing | Journal |
Volume | Issue | ISSN |
2016 | 1 | 1687-3963 |
Citations | PageRank | References |
1 | 0.36 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Mayer | 1 | 25 | 3.96 |
| Gabor Hannak | 2 | 14 | 4.83 |
| Norbert Goertz | 3 | 316 | 28.94 |