Name
Abstract | ||
|---|---|---|
In this paper, we present two new cumulant-based methods for time-varying AR parameters estimation: a batch-type evolutive method and an adaptive gradient-type algorithm. The evaluation of these techniques is performed through simulations on synthetic non-Gaussian signals contaminated by an additive, zero-mean, white Gaussian noise. We compare them to their autocorrelation-based counterparts. The obtained results show, using an appropriate criterion, the superiority of our cumulant-based evolutive method over both its autocorrelation-based version and the proposed cumulant-based gradient-type algorithm at the expense of more computational complexity. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1016/0165-1684(94)90127-9 | Signal Processing |
Keywords | Field | DocType |
cumulant,ar model | Gradient method,Autoregressive model,Mathematical optimization,Cumulant,Gaussian,Estimation theory,Additive white Gaussian noise,Mathematics,Computational complexity theory,Autocorrelation | Journal |
Volume | Issue | ISSN |
39 | 1-2 | 0165-1684 |
Citations | PageRank | References |
4 | 0.62 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M'hamed Bakrim | 1 | 6 | 1.71 |
| Driss Aboutajdine | 2 | 589 | 88.82 |
| Mohamed Najim | 3 | 149 | 32.29 |