Real-valued robust DOA estimation method for uniform circular acoustic vector sensor arrays based on worst-case performance optimization

Shi, Shengguo; Liu, Ying; Zhu, Zhaojie; Shi, Jie

doi:10.1016/j.apacoust.2018.12.014

Cited by 22 publications

(5 citation statements)

References 29 publications

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“…literature [9]- [19]. These algorithms are generally designed with the use of specific but regular array geometries, e.g., linear [9]- [12], circular [13], [14], rectangular [15]- [17], Lshaped [18], and cross-shaped [19]. They are, of course, unable to work properly for an arbitrary array configuration.…”

Section: Many Advanced Algorithms Have Been Reported In the Openmentioning

confidence: 99%

Particle-Velocity Coarray Augmentation for Direction Finding with Acoustic Vector Sensors

Shu

Truong

2022

Circuits Syst Signal Process

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In this paper, the problem of passive direction finding is addressed using an acoustic vector sensor array (AVS), which may be deployed either in free space or near a reflecting boundary. Building upon the 4 × 1 vector field measured by an AVS, the particle-velocity coarray augmentation (PVCA) is proposed to admit the underdetermined direction finding using the spatial difference coarray derived from the vectorization of the array covariance matrix. Unlike the widely used spatial coarray Toeplitz recovery technique, the PVCA is applicable to arbitrary array geometries and imposes no reduction of the spatial difference coarray aperture. For the array located at or near a reflecting boundary, the PVCA allows resolving up to 13 sources, while for the array located in free space, the PVCA can identify 9 sources at most. By applying to the systematically designed nonuniform arrays, such as coprime arrays and nested arrays, the PVCA can be coupled with the spatial smoothing technique to get the number of resolvable sources multiplied. Finally, the efficacy of the PVCA is verified by numerical simulations.

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Section: Many Advanced Algorithms Have Been Reported In the Openmentioning

confidence: 99%

Particle-Velocity Coarray Augmentation for Direction Finding with Acoustic Vector Sensors

Shu

Truong

2022

Circuits Syst Signal Process

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“…For the extended Multiple-Sources Multiple-Sensors (MSMS) scenario, Hawkes and Nehorai considered in [17] both the conventional and the minimum-variance distortionless response beamforming DOAs estimates to demonstrate the improvement attained by using AVSs rather than traditional pressure sensors. Following this work, an abundance of methods have been proposed for various specific scenarios (whether for array-or signal-related properties), such as linear [18], circular [19]- [21], sparse [22]- [24] and nested [25]- [27] arrays, coherent signals [28]- [30], one-bit measurements [31] and a variety of others [32]- [34]. However, all these methods require perfect or (at least) partial prior knowledge of the array configuration, or, equivalently 1 , of the steering vectors parametric structure, in particular w.r.t.…”

Section: A Previous Work: Doa Estimation With Avssmentioning

confidence: 99%

Blind Direction-of-Arrival Estimation in Acoustic Vector-Sensor Arrays via Tensor Decomposition and Kullback-Leibler Divergence Covariance Fitting

Weiss

2020

Preprint

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A blind Direction-of-Arrivals (DOAs) estimate of narrowband signals for Acoustic Vector-Sensor (AVS) arrays is proposed. Building upon the special structure of the signal measured by an AVS, we show that the covariance matrix of all the received signals from the array admits a natural lowrank 4-way tensor representation. Thus, rather than estimating the DOAs directly from the raw data, our estimate arises from the unique parametric Canonical Polyadic Decomposition (CPD) of the observations' Second-Order Statistics (SOSs) tensor. By exploiting results from fundamental statistics and the recently re-emerging tensor theory, we derive a consistent blind CPDbased DOAs estimate without prior assumptions on the array configuration. We show that this estimate is a solution to an equivalent approximate joint diagonalization problem, and propose an ad-hoc iterative solution. Additionally, we derive the Cramér-Rao lower bound for Gaussian signals, and use it to derive the iterative Fisher scoring algorithm for the computation of the Maximum Likelihood Estimate (MLE) in this particular signal model. We then show that the MLE for the Gaussian model can in fact be used to obtain improved DOAs estimates for non-Gaussian signals as well (under mild conditions), which are optimal under the Kullback-Leibler divergence covariance fitting criterion, harnessing additional information encapsulated in the SOSs. Our analytical results are corroborated by simulation experiments in various scenarios, which also demonstrate the considerable improved accuracy w.r.t. Zhang et al.'s state-of-theart blind estimate [1] for AVS arrays, reducing the resulting root mean squared error by up to more than an order of magnitude.

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“…Maximum likelihood (ML) methods can achieve good performance, but they are unable to balance estimation accuracy against computational time [9]. In recent years, sparse reconstruction (SR)-based methods have gained much attention [10][11][12][13][14][15][16][17]. Among them, sparse Bayesian learning (SBL) is a representative one.…”

Section: Introductionmentioning

confidence: 99%

A Sparse Bayesian Learning Method for Direction of Arrival Estimation in Underwater Maneuvering Platform Noise

Wang,

Zhao,

Qiu

et al. 2023

JMSE

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The underwater maneuvering platform generates self-noise when sailing, which shows spatial directionality to the arrays fixed on the platform. In this paper, it is called spatially colored noise (SCN). The direction of arrival (DOA) estimation results are often influenced by this self-noise, leading to a decrease in estimation accuracy and to the appearance of spurious peaks. To resolve this problem, a sparse Bayesian learning (SBL) method adapted to underwater maneuvering platform noise is proposed in this paper. The SBL framework with unknown SCN is established first. Then, the SCN covariance matrix is estimated by projecting the received data covariance matrix into the noise subspace, and the DOA estimation results are finally obtained through multiple iterations. The simulation results show that the proposed method avoids spurious peaks, and compared to the existing methods, the proposed method achieves a higher accuracy in the case of low SNRs and small snapshot numbers. The sea trial data processing results show that the proposed method provides lower and flatter noise spectrum levels without spurious peaks.

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Real-valued robust DOA estimation method for uniform circular acoustic vector sensor arrays based on worst-case performance optimization

Cited by 22 publications

References 29 publications

Particle-Velocity Coarray Augmentation for Direction Finding with Acoustic Vector Sensors

Particle-Velocity Coarray Augmentation for Direction Finding with Acoustic Vector Sensors

Blind Direction-of-Arrival Estimation in Acoustic Vector-Sensor Arrays via Tensor Decomposition and Kullback-Leibler Divergence Covariance Fitting

A Sparse Bayesian Learning Method for Direction of Arrival Estimation in Underwater Maneuvering Platform Noise

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