| Paper ID | SAM-11.2 |
| Paper Title |
NON-ITERATIVE BLIND CALIBRATION OF NESTED ARRAYS WITH ASYMPTOTICALLY OPTIMAL WEIGHTING |
| Authors |
Amir Weiss, Weizmann Institute of Science, Israel; Arie Yeredor, Tel-Aviv University, Israel |
| Session | SAM-11: Array Calibration and Performance Analysis |
| 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: [SAM-CALB] Array calibration |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Blind calibration of sensors arrays (without using calibration signals) is an important, yet challenging problem in array processing. While many methods have been proposed for "classical" array structures, such as uniform linear arrays, not as many are found in the context of the more "modern" sparse arrays. In this paper, we present a novel blind calibration method for 2-level nested arrays. Specifically, and despite recent contradicting claims in the literature, we show that the Least-Squares (LS) approach can in fact be used for this purpose with such arrays. Moreover, the LS approach gives rise to optimally-weighted LS joint estimation of the sensors' gains and phases offsets, which leads to more accurate calibration, and in turn, to higher accuracy in subsequent estimation tasks (e.g., direction-of-arrival). Our method, which can be extended to K-level arrays (K>2), is superior to the current state of the art both in terms of accuracy and computational efficiency, as we demonstrate in simulation. |
