Maximum-likelihood bearing estimation with partly calibrated arrays in spatially correlated noise fields

Stoica, Petre; Viberg, Mats; Wong, K.M.; Wu, Qiang

doi:10.1109/78.492542

Cited by 39 publications

(23 citation statements)

References 18 publications

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“…The sensitivity of high-resolution parametric estimation methods to spatially correlated noise has been reported for example in [54,55]. Several approaches have been proposed to address the source localization problem in the presence of an unknown noise covariance [8,[56][57][58][59][60][61][62][63].…”

Section: Direction Estimation In Underwater Acousticsmentioning

confidence: 99%

Covariance Matching Estimation Techniques for Array Signal Processing Applications

Ottersten

Stoica

Roy

1998

Digital Signal Processing

360

265

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Section: Direction Estimation In Underwater Acousticsmentioning

confidence: 99%

Covariance Matching Estimation Techniques for Array Signal Processing Applications

Ottersten

Stoica

Roy

1998

Digital Signal Processing

360

265

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“…When Stoica et al published their work in [2], Weiss and Friedlander also considered the same problem in [4] in the same year. However, different from the Stoica's work [2], Weiss's approach is able to resolve more sources.…”

Section: B Weiss's Approachmentioning

confidence: 90%

“…In [1] and [2], Stoica et al considered the problem of using a partly calibrated array for maximum likelihood (ML) bearing estimation of signals buried in unknown correlated noise fields. It is assumed that the array contains some calibrated sensors, whose number is required to be larger than the number of signals impinging on the array, and also that the noise in the calibrated sensors is uncorrelated with the noise in the other sensors.…”

Section: A Maximum Likelihood (Ml) Methodsmentioning

confidence: 99%

A review on direction finding in partly calibrated arrays

Liao

Chan

2014

2014 19th International Conference on Digital Signal Processing

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It is known that sensor arrays in certain practical applications may be incompletely calibrated. As a result, the response of some sensor elements is poorly known or even unknown. This class of arrays is usually referred to as partly calibrated arrays. During the past several years, the problem of direction finding in partly calibrated arrays has received great research interest. This paper aims to provide a timely review on this area. Several classical and recently developed direction finding algorithms for this kind of arrays, such as rank-reduction (RARE) estimator and ESPRIT-like method, are presented. As a conclusion, a brief discussion on the existing methods are given and the open research problems on direction finding in partly calibrated arrays are outlined for reference purposes.

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“…Note that J Ψ in (36) is identical to J Ψ as defined by (11), which related the parameter vector θ Ψ to the P × P matrix Ψ via vect(Ψ) = J Ψ θ Ψ . Similarly, we can define a P × P matrix…”

Section: B Krylov-based Methods For Direction Of Descentmentioning

confidence: 99%

Complex Factor Analysis and Extensions

Sardarabadi

Veen

2018

IEEE Trans. Signal Process.

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Abstract-Many subspace-based array signal processing algorithms assume that the noise is spatially white. In this case the noise covariance matrix is a multiple of the identity and the eigenvectors of the data covariance matrix are not affected by the noise. If the noise covariance is an unknown arbitrary diagonal (e.g., for an uncalibrated array), the eigenvalue decomposition leads to incorrect subspace estimates and it has to be replaced by a more general "Factor Analysis" decomposition (FA), which then reveals all relevant information. We consider this data model and several extensions where the noise covariance matrix has a more general structure, such as banded, sparse, block-diagonal, and cases where we have multiple data covariance matrices that share the same noise covariance matrix. Starting from a nonlinear weighted least squares formulation, we propose new estimation algorithms for both classical FA and its extensions. The optimization is based on Gauss-Newton gradient descent. Generally, this leads to an iteration involving the inversion of a very large matrix. Using the structure of the problem, we show how this can be reduced to the inversion of a matrix with dimension equal to the number of unknown noise covariance parameters. This results in new algorithms that have faster numerical convergence and lower complexity compared to several maximum-likelihood based algorithms that could be considered state-of-the-art. The new algorithms scale well to large dimensions and can replace eigenvalue decompositions in many applications even if the noise can be assumed to be white.

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Maximum-likelihood bearing estimation with partly calibrated arrays in spatially correlated noise fields

Cited by 39 publications

References 18 publications

Covariance Matching Estimation Techniques for Array Signal Processing Applications

Covariance Matching Estimation Techniques for Array Signal Processing Applications

A review on direction finding in partly calibrated arrays

Complex Factor Analysis and Extensions

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