Name
Abstract | ||
|---|---|---|
In this study, the authors consider the identification of auto-regressive (AR) models for time-series from one-bit quantised observation sequences. The only available information is the fact that the samples of the time-series are lower or higher than a threshold of quantisation. This threshold may be different from zero. An identification algorithm is presented and analysed. A recursive formulation is proposed, an extension for the identification of a non-linear time-series is also proposed. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1049/iet-spr.2019.0152 | Iet Signal Processing |
Keywords | Field | DocType |
time series,autoregressive processes,quantisation (signal) | Mathematical optimization,Algorithm,Binary data,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 1 | 1751-9675 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathieu Pouliquen | 1 | 17 | 8.63 |
| Romain Auber | 2 | 0 | 0.34 |
| Eric Pigeon | 3 | 35 | 8.22 |
| Olivier Gehan | 4 | 14 | 4.47 |
| Mohammed M'saad | 5 | 123 | 15.08 |
| Pierre Alexandre Chapon | 6 | 0 | 0.34 |
| Sebastien Moussay | 7 | 0 | 0.34 |