“Blind” calibration of vector sensors whose dipole/loop triads deviate from their nominal gains/phases/orientations/locations

Abstract

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.

The Direction-of-Arrival and Polarization Estimation Using Coprime Array: A Reconstructed Covariance Matrix Approach

“…Thus, the PSA has played an important role in signal processing in recent decades [3][4][5][6][7][8]. Many DOA and polarization estimation using dipole triads algorithms have been proposed [9][10][11][12][13][14][15][16][17][18][19]. Polarized multiple signal classification (MUSIC) [20,21] and the polarized signal parameters via rotational invariance technique (ESPRIT) [22,23] are two major approaches.…”