Performance analysis and DOA estimation method over acoustic vector sensor array in the presence of polarity inconsisten
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Performance analysis and DOA estimation method over acoustic vector sensor array in the presence of polarity inconsistency Weidong Wang1
· Qunfei Zhang1 · Wentao Shi1 · Weijie Tan1
Received: 20 August 2019 / Revised: 12 December 2019 / Accepted: 14 February 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper investigates the direction of arrival (DOA) estimation performance in the presence of polarity inconsistency in uniform acoustic vector sensor (AVS) linear array. We analyze the influence of polarity bias on beampattern directivity of the AVS array. The analysis results show that the polarity bias leads to asymptotically biased estimation. Then, the analytical expression for the asymptotic bias based on classical beamforming is derived in the presence of polarity error. Moreover, to improve the DOA estimation performance in the presence of polarity inconsistency, a polarity calibration method is proposed. Numerical simulations reveal the effectiveness and superiority of the proposed calibration method when the polarity error satisfies with the uniform distribution and the normal distribution. Keywords Acoustic vector sensor (AVS) array · Plarity inconsistency · Asymptotically biased estimation · Polarity calibration method · Direction of arrival (DOA) estimation
1 Introduction In recent years, acoustic vector sensor (AVS) array processing has drawn much interest in multiple research fields, such as feature extraction, target tracking, acoustic communication, and geoacoustic inversion problem (Zhang et al. 2016; Awad and Wong 2012; Fauziya et al. 2018; Guo et al. 2018; Santos et al. 2010). Compared with a traditional acoustic scalar sensor (ASS), which can extract only the acoustic pressure information, single AVS can
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Wentao Shi [email protected] Weidong Wang [email protected] Qunfei Zhang [email protected] Weijie Tan [email protected]
1
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, People’s Republic of China
123
Multidimensional Systems and Signal Processing
simultaneously measure pressure and two or three orthogonal particle velocity components of the acoustic field at a point to space. By using the additional information about acoustic particle velocity, the aperture of the AVS array can be expanded, and the parameter estimation performance can be enhanced. Moreover, the AVS array can provide unambiguous DOA estimation compared to the ASS array (Hawkes and Nehorai 2001; Kitchens 2008). Therefore, it should outperform the ASS array in terms of the parameter estimation accuracy. In Nehorai and Paldi (1994), the AVS array model was first introduced to the signal processing field, and the expression of performance bound on the DOA estimation was also derived. Since then, many advanced ASS array techniques, such as classical beamforming (CBF), minimum variance distortion-less response (MVDR), multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance technique (ESPRI
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