Name
Abstract | ||
|---|---|---|
It has been shown earlier that the problem of multichannel autoregressive moving average (ARMA) parameter estimation can be tackled in a computationally efficient way by converting the given process into an equivalent scalar, periodic ARMA process. The authors present methods used to compute the Cramer-Rao bound associated with the identification of the scalar ARMA equivalent of a given multichannel ARMA process. The elements of matrix are obtained by a few very simple operations like periodic AR filtering of certain downsampled versions of the input and output sequences and then cross-correlating the filter outputs. The filter is easily obtainable from the model equation and is common for all the parameters |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1109/78.275631 | Signal Processing, IEEE Transactions |
Keywords | Field | DocType |
filtering and prediction theory,matrix algebra,parameter estimation,signal processing,stochastic processes,time series,Cramer-Rao bound,autoregressive moving average,cross correlation,downsampling,identification,input sequences,matrix elements,model equation,multichannel ARMA parameter estimation,output sequences,periodic AR filtering,scalar periodic ARMA process,signal processing | Cramér–Rao bound,Autoregressive–moving-average model,Matrix (mathematics),Control theory,Scalar (physics),Filter (signal processing),Algorithm,Stochastic process,Speech recognition,Input/output,Estimation theory,Mathematics | Journal |
Volume | Issue | ISSN |
42 | 2 | 1053-587X |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Chakraborty | 1 | 52 | 8.40 |
| Prasad, S. | 2 | 11 | 3.44 |