Name
Abstract | ||
|---|---|---|
A dynamic programming algorithm and a suboptimal but computationally efficient method for estimation of a chaotic signal in white Gaussian noise are proposed. The nonlinear map is assumed known so that only the initial condition need be estimated. Computer simulations confirm that both approaches produce efficient estimates at high signal-to-noise ratios |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/78.403367 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Gaussian noise,chaos,computational complexity,dynamic programming,interference (signal),maximum likelihood detection,maximum likelihood estimation,white noise,chaotic signal estimation,dynamic programming algorithm,high signal-to-noise ratios,initial condition,nonlinear map,suboptimal but computationally efficient method,white Gaussian noise | Dynamic programming,Signal processing,Mathematical optimization,Nonlinear system,White noise,Initial value problem,Chaotic,Gaussian noise,Additive white Gaussian noise,Mathematics | Journal |
Volume | Issue | ISSN |
43 | 8 | 1053-587X |
Citations | PageRank | References |
25 | 4.70 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Kay | 1 | 309 | 40.73 |
| V. Nagesha | 2 | 114 | 20.92 |