Source Number Detectability by an Acoustic Vector Sensor Linear Array and Performance Analysis

Wu, Yanqun; Hu, Zhengliang; Luo, Hong; Hu, Yongming

doi:10.1109/joe.2013.2291629

Cited by 19 publications

(8 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…of Computer Science and Applied Mathematics, Faculty of Mathematics and Computer Science, Weizmann Institute of Science, 234 Herzl Street, Rehovot 7610001, Israel, e-mail: amir.weiss@weizmann.ac.il. localization and tracking [9], [10], battlefield acoustics classification [11], and underwater acoustic communication [12] and source enumeration [13], to name but a few.…”

Section: Introductionmentioning

confidence: 99%

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

Weiss

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Weiss

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on the linear independence of the array manifold matrix, the maximum number of identifiable sources for under-sampled, oversampled and critically-sampled AVS Uniform Linear Arrays (ULAs) is discussed in Ref. [21]. The extension of DOA estimation in the presence of coherent sources using AVS arrays was considered in Ref.…”

Section: Introductionmentioning

confidence: 99%

Uniaxial Acoustic Vector Sensors for direction-of-arrival estimation

Ramamohan

Comesaña

Leus

2018

Journal of Sound and Vibration

View full text Add to dashboard Cite

In this paper, a specific reduced-channel Acoustic Vector Sensor (AVS) is proposed comprising one omni-directional microphone and only one particle velocity transducer, such that it can have an arbitrary orientation. Such a reduced transducer configuration is referred to as a Uniaxial AVS (U-AVS). The DOA performance of an array of U-AVSs is analyzed through its beampattern and compared to conventional configurations. It is shown that the U-AVS array beampattern results in an asymptotically biased estimate of the source location and it can be varied by choosing the orientation angles of the particle velocity transducers. Analytical expressions for the asymptotic bias of classical beamforming are proposed and verified both numerically as well as experimentally for Uniform Linear Arrays (ULAs). Furthermore, the Cramér-Rao Bound (CRB) and Mean Square Error (MSE) expressions are derived for a U-AVS array under a single source scenario and they are numerically evaluated for ULA. The implications of changing the orientations of the U-AVSs in the array on the MSE are discussed as well.

show abstract

“…Detecting wideband source number is one of the hot and difficult problems in the area of array signal processing; meanwhile, it is widely used in many fields, such as radio monitoring [1][2][3][4][5], acoustic signal processing [6][7][8][9][10], and Internet of Things [11][12][13][14]; the corresponding methods are also being studied by a lot of scholars all the time. Generally speaking, knowing number of signals is also the premier condition of estimating direction of arrival (DOA).…”

Section: Introductionmentioning

confidence: 99%

Detection of Wideband Signal Number Based on Bootstrap Resampling

Zhen

2016

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

Knowing source number correctly is the precondition for most spatial spectrum estimation methods; however, many snapshots are needed when we determine number of wideband signals. Therefore, a new method based on Bootstrap resampling is proposed in this paper. First, signals are divided into some nonoverlapping subbands; apply coherent signal methods (CSM) to focus them on the single frequency. Then, fuse the eigenvalues with the corresponding eigenvectors of the focused covariance matrix. Subsequently, use Bootstrap to construct the new resampling matrix. Finally, the number of wideband signals can be calculated with obtained vector sequences according to clustering technique. The method has a high probability of success under low signal to noise ratio (SNR) and small number of snapshots.

show abstract

Source Number Detectability by an Acoustic Vector Sensor Linear Array and Performance Analysis

Cited by 19 publications

References 38 publications

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

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

Uniaxial Acoustic Vector Sensors for direction-of-arrival estimation

Detection of Wideband Signal Number Based on Bootstrap Resampling

Contact Info

Product

Resources

About