Acoustic source localization in mixed field using spherical microphone arrays

Huang, Qinghua; Wang, Tong

doi:10.1186/1687-6180-2014-90

Cited by 18 publications

(6 citation statements)

References 45 publications

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“…In [188], the problem of localization of wideband sources in the NF of the sensor array has been addressed. Moreover, in [189], a two-step algorithm is developed to localize the mixed FF and NF acoustic sources using a N ′ -order spherical microphone array [190,191] in a free-field environment. The algorithm proposed in [189] can localize multiple wideband mixed-field sources.…”

Section: Wideband Signalsmentioning

confidence: 99%

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

Molaei,

Zakeri,

Andargoli

et al. 2024

IEEE Access

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Direction-of-arrival (DOA) estimation plays a crucial role in array signal processing across various domains, including radar, sonar, wireless communications, and seismic exploration. However, traditional DOA techniques often assume either far-field (FF) or near-field (NF) propagation, limiting their applicability in scenarios involving mixed-field sources. DOA estimation and localization in scenarios involving mixed NF and FF sources is a complex and dynamic field that has garnered significant research attention in recent years. This multifaceted and evolving area holds promise for addressing challenges in radar, wireless communications, and acoustic sensing applications. This review paper provides a comprehensive overview of the methodologies, techniques, and advancements in this domain. We categorize existing methodologies, discussing their advantages and limitations. Furthermore, we delve into the mathematical modeling of mixed-field sources and essential signal processing techniques for parameter estimation. Special attention is given to technical issues such as aperture loss, computational complexity, and hardware considerations. The paper discusses the various sources of noise in the mentioned scenario and highlights the importance of modeling noise accurately for effective estimation. It also explores different scenarios and assumptions considered in the literature, ranging from non-Gaussian and nonstationary noise environments to scenarios involving multipath propagation and unknown mutual coupling effects. A detailed examination of the statistical approaches used in DOA estimation and localization reveals a diverse range of methods, including higher-order statistics and second-order statistics, each with its own advantages and applications. A comparative evaluation of various approaches highlights their performance in terms of estimation accuracy, resolution, aperture loss and computational efficiency. This provides insights into the trade-offs involved in choosing between different approaches. The review also identifies promising future research directions, such as the exploration of advanced signal processing techniques like compressive sensing and deep learning, exact NF modeling, estimation based on one-bit measurements, the integration of polarization diversity, employing metasurface antennas, tracking parameters, and the utilization of full-wave or experimental data for a more realistic representation of the challenges. By reviewing advances in methodologies and techniques, as well as outlining future research directions aimed at addressing the complexities of mixed-field scenarios, this paper paves the way for the development of more robust and reliable localization systems capable of handling real-world complexities.

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Section: Wideband Signalsmentioning

confidence: 99%

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

Molaei,

Zakeri,

Andargoli

et al. 2024

IEEE Access

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“…The advantage of ESPRIT is its low computational complexity.Many researchers proposed improved methods for different arrays, such as Unitary ESPRIT (U-ESPRIT), Spherical harmonics ESPRIT (SHESPRIT), Two-stage decoupled approach (TSDA) and so on [11][12][13][14] is still insufficient compared to MUSIC algorithm. MUSIC algorithm has high estimation performance.…”

Section: Introductionmentioning

confidence: 99%

Two‐Dimensional DOA Estimation Using One‐Dimensional Search for Spherical Arrays

2019

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“…In this paper, a spherical array is used to track the moving sources in 3-D space because the array is 3D symmetric structure and can capture high-order sound field information [28][29][30]. Besides, comparing with linear array, spherical array can capture the twodimension information of signal rather than singleangle information.…”

Section: Introductionmentioning

confidence: 99%

2-D DOA tracking using variational sparse Bayesian learning embedded with Kalman filter

Huang

et al. 2018

EURASIP J. Adv. Signal Process.

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In this paper, we consider the 2-D direction-of-arrival (DOA) tracking problem. The signals are captured by a uniform spherical array and therefore can be analyzed in the spherical harmonics domain. Exploiting the sparsity of source DOAs in the whole angular region, we propose a novel DOA tracking method to estimate the source locations and trace their trajectories by using the variational sparse Bayesian learning (VSBL) embedded with Kalman filter (KF). First, a transition probabilities (TP) model is used to build the state transition process, which assumes that each source moves to its adjacent grids with equal probability. Second, the states are estimated by KF in the variational E-step of the VSBL and the variances of the state noise and measurement noise are learned in the variational M-step of the VSBL. Finally, the proposed method is extended to deal with the off-grid tracking problem. Simulations show that the proposed method has higher accuracy than VSBL and KF methods.

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Acoustic source localization in mixed field using spherical microphone arrays

Cited by 18 publications

References 45 publications

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

Two‐Dimensional DOA Estimation Using One‐Dimensional Search for Spherical Arrays

2-D DOA tracking using variational sparse Bayesian learning embedded with Kalman filter

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