“Blind” calibration of vector sensors whose dipole/loop triads deviate from their nominal gains/phases/orientations/locations
Wong, Kainam Thomas
Date of Issue2017
School of Electrical and Electronic Engineering
An electromagnetic vector sensor consists of a triad of electric dipoles in orthogonal orientation, plus another triad of similarly arranged magnetic loops, all in spatial collocation. This electromagnetic vector sensor has been used in a series of algorithms to estimate the incident sources' directions-of-arrival and polarizations. However, these algorithms have presumed the dipole triads and the loop triads of perfect ideality in their gain/phase responses, their orientations, and locations. Such idealization is rarely (if ever) attained in actual field deployment. Instead, the nonidealities need to be calibrated, often blindly with no training signal impinging from any prior known direction-of-arrival at any prior known polarization. For such a scenario, this work proposes a new algorithm for direction finding, for polarization estimation, and for “blind” calibration (a.k.a. “self-calibration,” “autocalibration,” or “unaided calibration”) of all above nonidealities. This new algorithm is orders-of-magnitude computationally simpler than maximum likelihood estimation. This reduction in complexity is achieved here by exploiting the electromagnetic vector sensor's quintessential array manifold and by a judicious breakdown of the originally high-dimensional problem (of estimating the directions-of-arrival, polarizations, and the antenna nonidealities) into suitably chosen/sequenced low-dimensional subproblems. This proposed algorithm is first in the open literature to exploit the electromagnetic vector-sensor's quintessential array manifold for “blind” calibration of all above mentioned nonidealities simultaneously. Monte Carlo simulations verify this proposed algorithm's effectiveness in “blind” calibration and this algorithm's orders-of-magnitude computational efficiency over the maximum likelihood approach.
Array Signal Processing
Array Signal Processing
© 2017 American Geophysical Union (AGU). This paper was published in Radio Science and is made available as an electronic reprint (preprint) with permission of American Geophysical Union (AGU). The published version is available at: [http://dx.doi.org/10.1002/2017RS006340]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.