Name
Abstract | ||
|---|---|---|
We present a d-dimensional exponential analysis algorithm that offers a range of advantages compared to other methods. The technique does not suffer the curse of dimensionality and only needs O((d + 1)n) samples for the analysis of an n-sparse expression. It does not require a prior estimate of the sparsity n of the d-variate exponential sum. The method can work with sub-Nyquist sampled data and offers a validation step, which is very useful in low SNR conditions. A favorable computation cost results from the fact that d independent smaller systems are solved instead of one large system incorporating all measurements simultaneously. So the method easily lends itself to a parallel execution. Our motivation to develop the technique comes from 2-D and 3-D radar imaging and is therefore illustrated on such examples. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1137/19M1278004 | SIAM JOURNAL ON SCIENTIFIC COMPUTING |
Keywords | DocType | Volume |
exponential analysis,parametric method,multidimensional,sparse model,sparse data,inverse problems | Journal | 42 |
Issue | ISSN | Citations |
3 | 1064-8275 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Annie Cuyt | 1 | 161 | 41.48 |
| Yuan Hou | 2 | 1 | 1.04 |
| Ferre Knaepkens | 3 | 1 | 0.36 |
| Wen-shin Lee | 4 | 182 | 15.67 |