Name
Title | ||
|---|---|---|
Trust, But Verify: Fast and Accurate Signal Recovery From 1-Bit Compressive Measurements |
Abstract | ||
|---|---|---|
The recently emerged compressive sensing (CS) framework aims to acquire signals at reduced sample rates compared to the classical Shannon-Nyquist rate. To date, the CS theory has assumed primarily real-valued measurements; it has recently been demonstrated that accurate and stable signal acquisition is still possible even when each measurement is quantized to just a single bit. This property enables the design of simplified CS acquisition hardware based around a simple sign comparator rather than a more complex analog-to-digital converter; moreover, it ensures robustness to gross nonlinearities applied to the measurements. In this paper we introduce a new algorithm—restricted-step shrinkage (RSS)—to recover sparse signals from 1-bit CS measurements. In contrast to previous algorithms for 1-bit CS, RSS has provable convergence guarantees, is about an order of magnitude faster, and achieves higher average recovery signal-to-noise ratio. RSS is similar in spirit to trust-region methods for nonconvex optimization on the unit sphere, which are relatively unexplored in signal processing and hence of independent interest. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TSP.2011.2162324 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
1-bit cs,sparse signal,classical shannon-nyquist rate,cs acquisition hardware,gross nonlinearities,cs theory,stable signal acquisition,complex analog-to-digital converter,accurate signal recovery,signal processing,1-bit compressive measurements,1-bit cs measurement,quantization,compressed sensing,signal detection,trust region,optimization,signal to noise ratio,convergence,hardware | Convergence (routing),Signal processing,Mathematical optimization,Detection theory,Computer science,Sampling (signal processing),Robustness (computer science),Quantization (signal processing),RSS,Compressed sensing | Journal |
Volume | Issue | ISSN |
59 | 11 | 1053-587X |
Citations | PageRank | References |
69 | 2.70 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jason N. Laska | 1 | 1179 | 59.51 |
| Zaiwen Wen | 2 | 934 | 40.20 |
| Wotao Yin | 3 | 5038 | 243.92 |
| Richard G. Baraniuk | 4 | 5053 | 489.23 |