Abstract
Much work has been done designing transmit waveforms for target identification, classification, and detection. In addition, these have also been studied in both single and multiple-antenna scenarios. In this work, we study the construction of a waveform when multiple radar sensors are used to image a target scene. The scene is assumed to have a prior distribution given by a Compound Gaussian (CG) - a model that has proven very useful in the field of image processing. Waveform optimization is done with the objective of optimizing mutual information, while reconstruction was performed using sparsity based reconstruction techniques. In our work, the waveform is tailored for a particular target of interest in the scene while suppressing the clutter. Using our waveform techniques, we demonstrate statistically significant improvements in the quality of the reconstructed image in peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). We validate our algorithms using the MSTAR database.
| Original language | English (US) |
|---|---|
| Title of host publication | Radar Sensor Technology XXIII |
| Editors | Kenneth I. Ranney, Armin Doerry |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510626713 |
| DOIs | |
| State | Published - Jan 1 2019 |
| Event | Radar Sensor Technology XXIII 2019 - Baltimore, United States Duration: Apr 15 2019 → Apr 17 2019 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 11003 |
| ISSN (Print) | 0277-786X |
| ISSN (Electronic) | 1996-756X |
Conference
| Conference | Radar Sensor Technology XXIII 2019 |
|---|---|
| Country | United States |
| City | Baltimore |
| Period | 4/15/19 → 4/17/19 |
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
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A compound Gaussian-based waveform design approach for enhanced target detection in multistatic radar imaging. / Idriss, Zacharie; Raj, Raghu G.; Narayanan, Ram Mohan.
Radar Sensor Technology XXIII. ed. / Kenneth I. Ranney; Armin Doerry. SPIE, 2019. 110031C (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11003).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A compound Gaussian-based waveform design approach for enhanced target detection in multistatic radar imaging
AU - Idriss, Zacharie
AU - Raj, Raghu G.
AU - Narayanan, Ram Mohan
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Much work has been done designing transmit waveforms for target identification, classification, and detection. In addition, these have also been studied in both single and multiple-antenna scenarios. In this work, we study the construction of a waveform when multiple radar sensors are used to image a target scene. The scene is assumed to have a prior distribution given by a Compound Gaussian (CG) - a model that has proven very useful in the field of image processing. Waveform optimization is done with the objective of optimizing mutual information, while reconstruction was performed using sparsity based reconstruction techniques. In our work, the waveform is tailored for a particular target of interest in the scene while suppressing the clutter. Using our waveform techniques, we demonstrate statistically significant improvements in the quality of the reconstructed image in peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). We validate our algorithms using the MSTAR database.
AB - Much work has been done designing transmit waveforms for target identification, classification, and detection. In addition, these have also been studied in both single and multiple-antenna scenarios. In this work, we study the construction of a waveform when multiple radar sensors are used to image a target scene. The scene is assumed to have a prior distribution given by a Compound Gaussian (CG) - a model that has proven very useful in the field of image processing. Waveform optimization is done with the objective of optimizing mutual information, while reconstruction was performed using sparsity based reconstruction techniques. In our work, the waveform is tailored for a particular target of interest in the scene while suppressing the clutter. Using our waveform techniques, we demonstrate statistically significant improvements in the quality of the reconstructed image in peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). We validate our algorithms using the MSTAR database.
UR - http://www.scopus.com/inward/record.url?scp=85072599813&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072599813&partnerID=8YFLogxK
U2 - 10.1117/12.2522428
DO - 10.1117/12.2522428
M3 - Conference contribution
AN - SCOPUS:85072599813
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Radar Sensor Technology XXIII
A2 - Ranney, Kenneth I.
A2 - Doerry, Armin
PB - SPIE
ER -