Name
Abstract | ||
|---|---|---|
According to the main shortcoming of the traditional Maximum Likelihood Estimation(MLE) based algorithm for its high computational complexity, a new fast wind vector retrieval algorithm for SeaWinds Scatterometer is derived in this paper. The new fast algorithm adopts wind speed standard deviation instead of objective function as its criterion for searching possible wind vector solutions, which leads to lower complexity than traditional MLE algorithm. In order to further reduce retrieval computations, the new algorithm is implemented by a two-step method. First step accomplishes a coarse searching for likely wind vector solutions, while the second step functions as a fine adjustment for each coarse solution. Using some SeaWinds L2A and corresponding L2B data, the new algorithm is validated. The results indicate good performance and high retrieval accuracy of the new algorithm for experiment data. © 2005 IEEE. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/IGARSS.2005.1526546 | IGARSS |
Keywords | Field | DocType |
computational complexity,polarization,azimuth,maximum likelihood estimate,maximum likelihood estimation,remote sensing,objective function,wind speed,geographic information systems,standard deviation,information retrieval | Geographic information system,Wind speed,Computer science,Remote sensing,Azimuth,Scatterometer,Standard deviation,Step function,Computation,Computational complexity theory | Conference |
Volume | Issue | ISBN |
5 | null | 0-7803-9050-4 |
Citations | PageRank | References |
2 | 0.50 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuetong Xie | 1 | 8 | 4.88 |
| Yu Fang | 2 | 2 | 0.50 |
| Xiaoxiang Chen | 3 | 2 | 0.50 |
| Kehai Chen | 4 | 43 | 16.34 |