Multisource Self-calibration for Sensor Arrays

TL;DR

This paper introduces a novel weighted alternating least squares (WALS) algorithm for calibrating sensor arrays with direction-dependent gains and multiple sources, including unknown source locations, demonstrating efficiency and rapid convergence.

Contribution

The paper develops a new WALS-based calibration method for arbitrary sensor array geometries with identical elements, extending to unknown source locations with subspace fitting techniques.

Findings

Algorithms converge within two iterations at low SNR

Methods are asymptotically statistically efficient

Effective for arrays with direction-dependent gains and multiple sources

Abstract

Calibration of a sensor array is more involved if the antennas have direction dependent gains and multiple calibrator sources are simultaneously present. We study this case for a sensor array with arbitrary geometry but identical elements, i.e. elements with the same direction dependent gain pattern. A weighted alternating least squares (WALS) algorithm is derived that iteratively solves for the direction independent complex gains of the array elements, their noise powers and their gains in the direction of the calibrator sources. An extension of the problem is the case where the apparent calibrator source locations are unknown, e.g., due to refractive propagation paths. For this case, the WALS method is supplemented with weighted subspace fitting (WSF) direction finding techniques. Using Monte Carlo simulations we demonstrate that both methods are asymptotically statistically efficient and converge within two iterations even in cases of low SNR.