Near-field/far-field azimuth and elevation angle estimation using a single vector hydrophone

Tichavský, Petr; Wong, Kainam Thomas; Zoltowski, M.D.

doi:10.1109/78.960397

Cited by 168 publications

(113 citation statements)

References 25 publications

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“…The basic principle of the subspace-based parameter estimation algorithms, such as ESPRIT, is to separate the signal and the noise, into the different subspaces (i.e. the signal subspace and the noise subspace), which are derived from the data covariance matrix [64]. It follows that the number of incident sources should be less than the maximal rank of the data covariance Figure 5 The RMSE of the direction-cosines estimates versus inter-sensor spacing Δ/l.…”

Section: Resultsmentioning

confidence: 99%

Enhanced "vector-cross-product" direction-finding using a constrained sparse triangular-array

Yuan

2012

EURASIP J. Adv. Signal Process.

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A new configuration of sparse array is proposed in this article to estimate the direction-of-arrivals (DOAs) and polarizations of multiple sources. This constrained sparse array is composed of a dipole-triad, a loop-triad, and a single antenna, which can be a dipole, a loop, or a scalar-sensor. These three units comprise a triangular geometry in the space. This geometry creatively synergizes the conventional interferometry method based on the spatial phase-delay across displaced antennas, and the "vector-cross-product" based on Poynting-vector estimator to enhance the DOA estimation accuracy. The investigated algorithm based on this configuration adopts the "vectorcross-product" DOA estimator to provide the coarse estimate and then derives the fine estimate by extracting the inter-sensor phase factors in the sparse array. Following this, the disambiguation approach is adapted to derive the unambiguous estimate, and this estimate is also fine in estimation resolution. The proposed configuration can extend the array aperture and also reduce the mutual coupling. The significant performance of the proposed sparse array composition is demonstrated by Monte Carlo simulations when the inter-sensor spacing far exceeds a half-wavelength.

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Section: Resultsmentioning

confidence: 99%

Enhanced "vector-cross-product" direction-finding using a constrained sparse triangular-array

Yuan

2012

EURASIP J. Adv. Signal Process.

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“…The traditional solution is usually done with sensor arrays, but as the vector sensor has complete acoustic information, this task can been done with a single sensor [1]. Two simple algorithms for estimation the source direction of arrival (DOA) with vector sensor and the mean-square angular error (MSAE) bound is introduced in [2]; the beamforming and Capon direction estimators have been applied to vector sensor in [3]; a maximum likelihood DOA estimator is derived in [4]; and the underdetermined DOA estimation using vector sensor has been recently appear in [5].…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Near-Field Source Localization Using Acoustic Vector Sensor

Xiao¹,

Jin-fang²,

Zhang³

2017

Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)

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To avoid 2-D search, a two-stage algorithm for passively estimating the azimuth and range of a near-field source using an acoustic vector sensor is proposed. This algorithm utilizes two particle velocity channels to estimate the azimuth, and then combines the pressure channel to estimate the range. The Cramé r-Rao bound (CRB) on the estimation errors of the source azimuth is derived. Compared with the traditional method and CRB, The proposed algorithm is computationally efficient and has the similar performance with 2-D MUSIC algorithm. Simulation results are provided to demonstrate the high-efficient quality of this method.

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“…III, instead of the spatially collocated acoustic vectorsensor in Ref. 12. The only change in the data-model of (30) is that now a k ¼ a gen (h k , / k ) of (13).…”

Section: A Complete Algorithm To Demonstrate Sec Iii's Proposed mentioning

confidence: 99%

“…III. Section IV will show how to adopt this new scheme to an eigen-based parameter-estimation algorithm, in the case of one acoustic vector-sensor, using 12 as a concrete example. Section V will do the same, but for the case of multiple acoustic vector-sensors, using 14 as a concrete example.…”

Section: This Work's Contributionsmentioning

confidence: 99%

Azimuth-elevation direction finding using a microphone and three orthogonal velocity sensors as a non-collocated subarray

Song

Wong

2013

The Journal of the Acoustical Society of America

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An acoustic vector-sensor consists of three identical but orthogonally oriented acoustic particle-velocity sensors, plus a pressure sensor-all spatially collocated in a point-like geometry. At any point in space, this tri-axial acoustic vector-sensor can sample an acoustic wavefield as a 3 Â 1 vector, instead of simply as a scalar of pressure. This vector, after proper self-normalization, would indicate the incident wave-field's propagation direction, and thus the incident emitter's azimuth-elevation direction-of-arrival. This "self-normalization" direction-of-arrival estimator is predicated on the spatial-collocation among the three particle-velocity sensors and the pressure-sensor. This collocation constriction is relaxed here by this presently proposed idea, to realize a spatially distributed acoustic vector-sensor, allowing its four component-sensors to be separately located. This proposed scheme not only retains the algorithmic advantages of the aforementioned "self-normalization" direction-ofarrival estimator, but also will significantly extend the spatial aperture to improve the direction-finding accuracy by orders of magnitude.

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Near-field/far-field azimuth and elevation angle estimation using a single vector hydrophone

Cited by 168 publications

References 25 publications

Enhanced "vector-cross-product" direction-finding using a constrained sparse triangular-array

Enhanced "vector-cross-product" direction-finding using a constrained sparse triangular-array

Two-Stage Near-Field Source Localization Using Acoustic Vector Sensor

Azimuth-elevation direction finding using a microphone and three orthogonal velocity sensors as a non-collocated subarray

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