Inherent limitations in the localization of a coherently scattered source

Goldberg, J.; Messer, H.

doi:10.1109/78.735321

Cited by 34 publications

(19 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As it can be observed from the figures, the proposed method has a better performance even if there is an error in the knowledge of the antenna array geometry. Simulation Example 4: Desired Signal Steering Vector Mismatch due to Coherent Local Scattering [33]: In this example, the desired signal steering vector is distorted by local scattering effects so that the actual steering vector is formed by five signal paths as , where corresponds to the direct path and correspond to the coherently scattered paths. The th path is modeled as a plane wave impinging on the array from the direction .…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Indeed, interferences may have a rank-one or multi-rank covariance matrix. Specifically, an interference source with a rank-one covariance matrix corresponds to either a plane wave source or a locally coherent scattered source [33]. An interference source with a multi-rank covariance matrix corresponds, for example, to a locally incoherent scattered source [32].…”

Section: New Beamforming Problem Formulationmentioning

confidence: 99%

“…Steering vector of an interference with a locally coherent scattered source can be expressed as [33] ( 20) where corresponds to the direct path of the interference and correspond to the coherently scattered paths. Here is a plane wave impinging on the array from the direction which is fixed for different snapshots.…”

Section: New Beamforming Problem Formulationmentioning

confidence: 99%

See 2 more Smart Citations

Robust Adaptive Beamforming Based on Steering Vector Estimation With as Little as Possible Prior Information

Khabbazibasmenj

Vorobyov

Hassanien

2012

IEEE Trans. Signal Process.

245

262

View full text Add to dashboard Cite

A general notion of robustness for robust adaptive beamforming (RAB) problem and a unified principle for minimum variance distortionless response (MVDR) RAB techniques design are formulated. This principle is to use standard MVDR beamformer in tandem with an estimate of the desired signal steering vector found based on some imprecise prior information. Differences between various MVDR RAB techniques occur only because of the differences in the assumed prior information and the corresponding signal steering vector estimation techniques. A new MVDR RAB technique, which uses as little as possible and easy to obtain imprecise prior information, is developed. The objective for estimating the steering vector is the maximization of the beamformer output power, while the constraints are the normalization condition and the requirement that the estimate does not converge to any of the interference steering vectors and their linear combinations. The prior information used is only the imprecise knowledge of the antenna array geometry and angular sector in which the actual steering vector lies. Mathematically, the proposed MVDR RAB is expressed as the well known non-convex quadratically constrained quadratic programming problem with two constraints, which can be efficiently and exactly solved. Some new results for the corresponding optimization problem such as a new algebraic way of finding the rank-one solution from the general-rank solution of the relaxed problem and the condition under which the solution of the relaxed problem is guaranteed to be rank-one are derived. Our simulation results demonstrate the superiority of the proposed method over other previously developed RAB techniques.Index Terms-Output power maximization, robust adaptive beamforming (RAB), quadratically constrained quadratic programming (QCQP), semi-definite programming (SDP) relaxation, steering vector estimation.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: New Beamforming Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Robust Adaptive Beamforming Based on Steering Vector Estimation With as Little as Possible Prior Information

Khabbazibasmenj

Vorobyov

Hassanien

2012

IEEE Trans. Signal Process.

245

262

View full text Add to dashboard Cite

show abstract

“…If the condition (19) is verified then the modelling mismatch introduced in (6) by using point source steering vector does not introduce an estimation error. Despite the fact that the sensor coordinates are deterministic, one can give a statistical interpretation, xy = x y means that the sensors' the abscissa and ordinates are uncorrelated, and x 2 − (x) 2 = y 2 − (y) 2 means that the standard deviation on abscissa and ordinate are the same.…”

Section: Array Geometry For Musicmentioning

confidence: 95%

“…Firstly, one can note that the condition to obtain an isotropic array in the scenario of point source proposed in [11] is identical to (19). Note that (19) has been established in the presence of a model error due to the sources dispersion and an infinite number of snapshots to estimate the covariance matrix whereas the expression in [11] corresponds to the point sources case and the error is due to a finite number of snapshots.…”

Section: Link With the Cramer-rao Boundmentioning

confidence: 99%

Array geometry impact on music in the presence of spatially distributed sources

Xiong¹,

Picheral²,

Marcos³

2017

Digital Signal Processing

View full text Add to dashboard Cite

The MUltiple SIgnal Classification (MUSIC) estimator has been widely studied for a long time for its high resolution capability in the domain of the direction of arrival (DOA) estimation, with the sources assumed to be point. However, when the actual sources are spatially distributed with angular dispersion, the performance of the conventional MUSIC is degraded. In this paper, the impact of the array geometry on the DOA estimation of spatially distributed sources impinging on a sensor array is considered. Taking into account a coherently distributed source model, we establish closed-form expressions of the MUSIC-based DOA estimation error as a function of the positions of the array sensors in the presence of model errors due to the angular dispersion of the signal sources. The impact of the array geometry is studied and particular array designs are proposed to make DOA estimation more robust to source dispersion. The analytical results are validated by numerical simulations.

show abstract

Robustness Issues in Sensor Array Processing

Gershman

2010

Handbook on Array Processing and Sensor Networks

View full text Add to dashboard Cite

Inherent limitations in the localization of a coherently scattered source

Cited by 34 publications

References 5 publications

Robust Adaptive Beamforming Based on Steering Vector Estimation With as Little as Possible Prior Information

Robust Adaptive Beamforming Based on Steering Vector Estimation With as Little as Possible Prior Information

Array geometry impact on music in the presence of spatially distributed sources

Robustness Issues in Sensor Array Processing

Contact Info

Product

Resources

About