Abstract
ISAR imaging based on the 2D linear prediction uses the l2 norm minimization of the prediction error to obtain 2D autoregressive (AR) model coefficients. However, this approach causes many spurious peaks in the resulting image. In this study, a new ISAR imaging method based on the 2D sparse AR modeling of backscattered data is proposed. The 2D model coefficients are obtained by the l2- norm minimization of the prediction error penalized by the l1 norm of the prediction coefficient vector. The resulting 2D prediction coefficient vector is sparse, and its use yields radar images with reduced side lobes compared to the classical l2- norm minimization.
| Original language | English |
|---|---|
| Title of host publication | 2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014 |
| Publisher | European Signal Processing Conference, EUSIPCO |
| Pages | 1751-1755 |
| Number of pages | 5 |
| ISBN (Electronic) | 9780992862619 |
| Publication status | Published - 10 Nov 2014 |
| Event | 22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, Portugal Duration: 1 Sept 2014 → 5 Sept 2014 |
Publication series
| Name | European Signal Processing Conference |
|---|---|
| ISSN (Print) | 2219-5491 |
Conference
| Conference | 22nd European Signal Processing Conference, EUSIPCO 2014 |
|---|---|
| Country/Territory | Portugal |
| City | Lisbon |
| Period | 1/09/14 → 5/09/14 |
Bibliographical note
Publisher Copyright:© 2014 EURASIP.
Keywords
- autoregressive modeling
- linear prediction
- radar imaging
- regularization
- sparsity