Name
Abstract | ||
|---|---|---|
We consider the zero-crossing rate (ZCR) of a Gaussian process and establish a property relating the lagged ZCR (LZCR) to the corresponding normalized autocorrelation function. This is a generalization of Kedem's result for the lag-one case. For the specific case of a sinusoid in white Gaussian noise, we use the higher-order property between lagged ZCR and higher-lag autocorrelation to develop an iterative higher-order autoregressive filtering scheme, which stabilizes the ZCR and consequently provide robust estimates of the lagged autocorrelation. Simulation results show that the autocorrelation estimates converge in about 20 to 40 iterations even for low signal-to-noise ratio. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/LSP.2014.2322588 | Signal Processing Letters, IEEE |
Keywords | Field | DocType |
Gaussian noise,autoregressive processes,correlation theory,estimation theory,filtering theory,iterative methods,signal denoising,Gaussian process,contraction mapping approach,iterative higher order autoregressive filtering scheme,lagged autocorrelation,normalized autocorrelation function,robust estimation,white Gaussian noise,zero crossing rate,Contraction mapping,frequency estimation,lagged ZCR,lagged autocorrelation,zero-crossing rate (ZCR) | Autoregressive model,Mathematical optimization,Autocorrelation technique,Gaussian random field,Gaussian process,Partial autocorrelation function,Gaussian noise,Additive white Gaussian noise,Mathematics,Autocorrelation | Journal |
Volume | Issue | ISSN |
21 | 9 | 1070-9908 |
Citations | PageRank | References |
2 | 0.37 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chandra Sekhar Seelamantula | 1 | 48 | 7.95 |
| Ravi R. Shenoy | 2 | 6 | 2.14 |