| Paper ID | SAM-10.6 |
| Paper Title |
FUNDAMENTAL TRADE-OFFS IN NOISY SUPER-RESOLUTION WITH SYNTHETIC APERTURES |
| Authors |
Sina Shahsavari, Jacob Millhiser, Piya Pal, University of California, San Diego, United States |
| Session | SAM-10: Sparse Array Design and Processing |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Sensor Array and Multichannel Signal Processing: [RAS-SARI] Synthetic aperture radar/sonar and imaging |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
This paper concerns understanding the fundamental performance benefits of co-array based super-resolution techniques in low SNR settings. Proper sensor placement in the form of a sparse nested array allows the generation of difference co-arrays with large virtual apertures that offer higher resolution. However, the performance of super-resolution aperture synthesis techniques is known to degrade in presence of large noise levels. The main contribution of this paper is to rigorously establish that nested arrays provide lower Cram\'er-Rao bounds than a ULA (with the same number of sensors) in the low SNR regime, and therefore can lead to better resolvability of closely spaced sources. Numerical experiments are performed to validate theoretical claims, including demonstration of MUSIC spectra with two closely-spaced sources using ULA and nested arrays in various SNR settings. |
