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Mathematical Problems in Engineering
Volume 2018, Article ID 1219670, 9 pages
https://doi.org/10.1155/2018/1219670
Research Article

A Novel Self-Calibration Method for Acoustic Vector Sensor

1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2National Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China

Correspondence should be addressed to Yao Zhang; nc.ais@oaygnahz

Received 7 August 2017; Revised 16 January 2018; Accepted 31 January 2018; Published 11 March 2018

Academic Editor: Haranath Kar

Copyright © 2018 Yao Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The acoustic vector sensor (AVS) can measure the acoustic pressure field’s spatial gradient, so it has directionality. But its channels may have nonideal gain/phase responses, which will severely degrade its performance in finding source direction. To solve this problem, in this study, a self-calibration algorithm based on all-phase FFT spectrum analysis is proposed. This method is “self-calibrated” because prior knowledge of the training signal’s arrival angle is not required. By measuring signals from different directions, the initial phase can be achieved by taking the all-phase FFT transform to each channel. We use the amplitude of the main spectrum peak of every channel in different direction to formulate an equation; the amplitude gain estimates can be achieved by solving this equation. In order to get better estimation accuracy, bearing difference of different training signals should be larger than a threshold, which is related to SNR. Finally, the reference signal’s direction of arrival can be estimated. This method is easy to implement and has advantage in accuracy and antinoise. The efficacy of this proposed scheme is verified with simulation results.