Towards compressed sensing for ground-to-air monostatic radar
Recently, it is shown that the fundamental problem of rangeDoppler estimation can be solved efficiently by compressed sensing (CS) from single-pulse radar return. The performance of CS radar particularly degrade significantly with noise and hence the primary concern is to determine the regime where...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2016
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/81224 http://hdl.handle.net/10220/40678 |
| _version_ | 1824454960619716608 |
|---|---|
| author | Dauwels, Justin Kannan, Srinivasan |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Dauwels, Justin Kannan, Srinivasan |
| author_sort | Dauwels, Justin |
| collection | NTU |
| description | Recently, it is shown that the fundamental problem of rangeDoppler estimation can be solved efficiently by compressed sensing (CS) from single-pulse radar return. The performance of CS radar particularly degrade significantly with noise and hence the primary concern is to determine the regime where the operation of CS radar is satisfactory. Most of the studies on CS radar are often conducted under unrealistic conditions where the SNR is much higher than in typical radar applications (e.g., SNR > 5dB). In this paper, we investigate how to improve the CS reconstruction by using coherent integration over N pulses. We consider two scenarios: i) coherent integration is performed before CS reconstruction; ii) coherent integration is performed after CS reconstruction. We provide numerical results for both scenarios, and demonstrate that a proportional reduction in reconstruction error is obtained if coherent integration is carried out before CS reconstruction, corresponding to an effective gain of SNRg = 10 log10 N. On the other hand, when coherent integration is performed after applying CS reconstruction to single pulse radar returns, there is negligible gain. Both observations can be explained by the fact that CS reconstruction exhibits a threshold phenomenon with regard to SNR. By boosting the effective SNR through coherent integration, one can obtain more reliable CS reconstruction. |
| first_indexed | 2025-02-19T03:30:37Z |
| format | Conference Paper |
| id | ntu-10356/81224 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:30:37Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/812242019-12-06T14:25:55Z Towards compressed sensing for ground-to-air monostatic radar Dauwels, Justin Kannan, Srinivasan School of Electrical and Electronic Engineering 3rd SONDRA workshop on EM modeling, New Concepts and Signal Processing for Radar Detection and Remote Sensing Remote sensing Recently, it is shown that the fundamental problem of rangeDoppler estimation can be solved efficiently by compressed sensing (CS) from single-pulse radar return. The performance of CS radar particularly degrade significantly with noise and hence the primary concern is to determine the regime where the operation of CS radar is satisfactory. Most of the studies on CS radar are often conducted under unrealistic conditions where the SNR is much higher than in typical radar applications (e.g., SNR > 5dB). In this paper, we investigate how to improve the CS reconstruction by using coherent integration over N pulses. We consider two scenarios: i) coherent integration is performed before CS reconstruction; ii) coherent integration is performed after CS reconstruction. We provide numerical results for both scenarios, and demonstrate that a proportional reduction in reconstruction error is obtained if coherent integration is carried out before CS reconstruction, corresponding to an effective gain of SNRg = 10 log10 N. On the other hand, when coherent integration is performed after applying CS reconstruction to single pulse radar returns, there is negligible gain. Both observations can be explained by the fact that CS reconstruction exhibits a threshold phenomenon with regard to SNR. By boosting the effective SNR through coherent integration, one can obtain more reliable CS reconstruction. Accepted version 2016-06-14T07:48:06Z 2019-12-06T14:25:55Z 2016-06-14T07:48:06Z 2019-12-06T14:25:55Z 2013 2013 Conference Paper Dauwels, J., & Kannan, S. (2013). Towards compressed sensing for ground-to-air monostatic radar. 3rd SONDRA workshop on EM modeling, New Concepts and Signal Processing for Radar Detection and Remote Sensing. https://hdl.handle.net/10356/81224 http://hdl.handle.net/10220/40678 171265 en © 2013 3rd SONDRA Workshop on EM Modeling, New Concepts and Signal Processing for Radar Detection and Remote Sensing. This is the author created version of a work that has been peer reviewed and accepted for publication by 3rd SONDRA Workshop on EM Modeling, New Concepts and Signal Processing for Radar Detection and Remote Sensing, 3rd SONDRA Workshop on EM Modeling, New Concepts and Signal Processing for Radar Detection and Remote Sensing. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. 4 p. application/pdf |
| spellingShingle | Remote sensing Dauwels, Justin Kannan, Srinivasan Towards compressed sensing for ground-to-air monostatic radar |
| title | Towards compressed sensing for ground-to-air monostatic radar |
| title_full | Towards compressed sensing for ground-to-air monostatic radar |
| title_fullStr | Towards compressed sensing for ground-to-air monostatic radar |
| title_full_unstemmed | Towards compressed sensing for ground-to-air monostatic radar |
| title_short | Towards compressed sensing for ground-to-air monostatic radar |
| title_sort | towards compressed sensing for ground to air monostatic radar |
| topic | Remote sensing |
| url | https://hdl.handle.net/10356/81224 http://hdl.handle.net/10220/40678 |
| work_keys_str_mv | AT dauwelsjustin towardscompressedsensingforgroundtoairmonostaticradar AT kannansrinivasan towardscompressedsensingforgroundtoairmonostaticradar |