Source Localization Using Vector Sensor Array in a Multipath Environment

Rahamim, D.; Tabrikian, Joseph; Shavit, R.

doi:10.1109/tsp.2004.836456

Cited by 140 publications

(109 citation statements)

References 30 publications

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“…At the beginning of each outer loop,Θ (0) k is determined with (16) and (21) (see lines 5-7). After that, we sequentially redetermineΘ 1 ,Θ 2 , · · · ,Θ k and iteratively update these DOA estimates until convergence is achieved (see lines [8][9][10][11][12][13][14][15][16][17][18].…”

Section: Implementation Of Aop-doamentioning

confidence: 99%

“…Recall from (16) and (18) that, at the ith iteration, AOP-DOA updates the kth DOA estimate as follows:…”

Section: Relationship Between Aop-doa and Ap-mlementioning

confidence: 99%

“…However, the algorithm of redundancy averaging leads to biased DOA estimates [14], and the FBSS algorithm suffers from performance degradation due to the array aperture loss [15], [16]. On the other hand, iterative algorithms, including the alternating projection (AP) technique [17], [18] and the modified Gauss-Newton technique [19] are investigated to circumvent the multivariate nonlinear optimization procedure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DOA Estimation of Uncorrelated, Partly Correlated and Coherent Signals Using Alternating Oblique Projection

Hou¹,

Mao²

2017

RADIOENGINEERING

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show abstract

Section: Implementation Of Aop-doamentioning

confidence: 99%

“…Recall from (16) and (18) that, at the ith iteration, AOP-DOA updates the kth DOA estimate as follows:…”

Section: Relationship Between Aop-doa and Ap-mlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DOA Estimation of Uncorrelated, Partly Correlated and Coherent Signals Using Alternating Oblique Projection

Hou¹,

Mao²

2017

RADIOENGINEERING

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show abstract

“…[22,23] provide an algorithm based on sparse ULA, consisting of sensors which can measure all six electrical and magnetic components. [24] provide several algorithms to solve the correlation problem with the array of vector sensors. In [25,26], the polynomial rooting algorithm was adopted in polarization sensitive arrays.…”

Section: Introductionmentioning

confidence: 99%

A Fast DOA Estimation Algorithm Based on Polarization MUSIC

Guo¹,

Mao²,

Li³

et al. 2015

RADIOENGINEERING

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Abstract.A fast DOA estimation algorithm developed from MUSIC, which also benefits from the processing of the signals' polarization information, is presented. Besides performance enhancement in precision and resolution, the proposed algorithm can be exerted on various forms of polarization sensitive arrays, without specific requirement on the array's pattern. Depending on the continuity property of the space spectrum, a huge amount of computation incurred in the calculation of 4-D space spectrum is averted. Performance and computation complexity analysis of the proposed algorithm is discussed and the simulation results are presented. Compared with conventional MUSIC, the proposed algorithm has considerable advantage in aspects of precision and resolution, with a low computation complexity proportional to a conventional 2-D MUSIC.

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“…A comprehensive model for vector-sensor array processing has been proposed in [8], and direction/polarization estimation is analyzed in [9]. A maximum-likelihood estimation (MLE) of polarization parameters has been discussed in [10] and [11], whereas in [12] the authors presented a method for incorporating signal polarization in Capon's minimum variance estimator. Subspace-based methods, which are known to be computational efficient but still consistent, have been also proposed: a MUSIC-based approach is discussed in [13], and ESPRIT-based direction/polarization estimations are found in [14] for polarized signals with two-component sensors, in [15] and [16] using a sparse array of electromagnetic vector-sensors, and in [17] using a single electromagnetic vector sensor.…”

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confidence: 99%

Seismic Velocity and Polarization Estimation for Wavefield Separation

Donno

Nehorai

Spagnolini

2008

IEEE Trans. Signal Process.

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Abstract-We address the problem of estimating the shape parameters of seismic wavefields using linear arrays of three-component (3C) vector sensors with uncertain acquisition geometry. The goal is to separate the different seismic waves, which is of practical need for oil exploration and geophysics. We present a parametric model for multiple wideband polarized signals received by an array of three-component sensors with positional and rotational calibration errors, and derive the Cramér-Rao lower bounds on the performance of the model parameters for both the exact physical model and the model with uncertain acquisition geometry. We propose a method for jointly estimating the velocity and polarization parameters based on the shift-invariance properties of multiple wavefields impinging on the linear array. We then remove the interfering surface waves by using a beamforming filter designed to exploit the velocity and polarization diversity of the different seismic waves, after clustering of the shape-parameter estimates. Examples using simulated and experimental data illustrate the applicability of the proposed methodology.Index Terms-Array signal processing, Cramér-Rao bound, velocity/polarization estimation, vector-sensor calibration errors, vector-sensor broadband beamforming.

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Source Localization Using Vector Sensor Array in a Multipath Environment

Cited by 140 publications

References 30 publications

DOA Estimation of Uncorrelated, Partly Correlated and Coherent Signals Using Alternating Oblique Projection

DOA Estimation of Uncorrelated, Partly Correlated and Coherent Signals Using Alternating Oblique Projection

A Fast DOA Estimation Algorithm Based on Polarization MUSIC

Seismic Velocity and Polarization Estimation for Wavefield Separation

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