Self-Calibration of Acoustic Scalar and Vector Sensor Arrays

• Published in: IEEE transactions on signal processing Vol. 71; pp. 1 - 14
• Main Authors: Ramamohan, Krishnaprasad Nambur, Chepuri, Sundeep Prabhakar, Comesana, Daniel Fernandez, Leus, Geert
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustics; Algorithms; Anechoic chambers; Arrays; Calibration; Direction of arrival; Direction-of-arrival estimation; Estimation; Iterative methods; Linear arrays; Manifolds; Measurement uncertainty; Parameter identification; Self calibration; Sensor arrays; Sensors; Signal processing algorithms
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2022.3214383

In this work, we consider the self-calibration problem of joint calibration and direction-of-arrival (DOA) estimation using acoustic sensor arrays. Unlike many previous iterative approaches, we propose solvers that can be readily used for both linear and non-linear arrays for jointly estimating the sensor gain, phase errors, and the source DOAs. We derive these algorithms for both the conventional element-space and covariance data models. We focus on sparse and regular arrays formed using scalar sensors as well as vector sensors. The developed algorithms are obtained by transforming the underlying non-linear calibration model into a linear model, and subsequently by using convex relaxation techniques to estimate the unknown parameters. We also derive identifiability conditions for the existence of a unique solution to the self-calibration problem. To demonstrate the effectiveness of the developed techniques, numerical experiments, and comparisons to the state-of-the-art methods are provided. Finally, the results from an experiment that was performed in an anechoic chamber using an acoustic vector sensor array are presented to demonstrate the usefulness of the proposed self-calibration techniques.