Bearing estimation in a Ricean channel .I. Inherent accuracy limitations

Fuks, G.; Goldberg, J.; Messer, H.

doi:10.1109/78.917797

Cited by 23 publications

(17 citation statements)

References 13 publications

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“…The SINR will be plotted versus Dy and the latter is normalised to the array null-to-null beamwidth. Local scattering: in this case, the steering vector can be written as [11] …”

Section: Numerical Illustrationsmentioning

confidence: 99%

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Steering vector errors and diagonal loading

Vincent¹,

Besson²

2004

IEE Proc., Radar Sonar Navig.

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Diagonal loading is one of the most widely used and effective methods to improve robustness of adaptive beamformers. The authors consider its application to the case of steering vector errors, i.e. when there exists a mismatch between the actual steering vector of interest and the presumed one. More precisely, the problem addressed is that of optimally selecting the loading level with a view to maximising the signal-to-interference-plus-noise ratio in the presence of steering vector errors. First, an expression is derived for the optimal loading for a given steering vector error and it is shown that this loading is negative. Next, random steering errors are considered and the optimal loading is averaged with respect to the probability density function of the steering vector errors, yielding a very simple expression for the average optimal loading. Numerical simulations attest to the validity of the analysis and show that diagonal loading with the derived optimal loading factor provides a performance close to optimum.

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“…The SINR will be plotted versus Dy and the latter is normalised to the array null-to-null beamwidth. Local scattering: in this case, the steering vector can be written as [11] …”

Section: Numerical Illustrationsmentioning

confidence: 99%

“…the spatial signature of the line of sight component. The covariance matrix of the errors is given by [11] …”

Section: Numerical Illustrationsmentioning

confidence: 99%

Steering vector errors and diagonal loading

Vincent¹,

Besson²

2004

IEE Proc., Radar Sonar Navig.

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show abstract

“…In the latter case, the entries of mismatch vector become correlated and C δ is not diagonal. Another example is signal propagation through a Ricean channel, where the covariance matrix of the mismatch vector may depend on the spatial distribution of the scatterers [12].…”

Section: Robust Adaptive Beamforming Via Probability-constrained Optimentioning

confidence: 99%

“…We consider the scenario with Ricean propagation medium where the mismatch vector δ can be modelled as [1], [12] …”

Section: Simulationsmentioning

confidence: 99%

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Robust adaptive beamforming using probability-constrained optimization

Vorobyov

Rong

2005

IEEE/SP 13th Workshop on Statistical Signal Processing, 2005

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Recently, robust minimum variance (MV) beamforming which optimizes the worst-case performance has been proposed in [1], [2]. The worst-case approach, however, might be overly conservative in practical applications. In this paper, we propose a more flexible approach that formulates the robust adaptive beamforming problem as a probability-constrained optimization problem with homogeneous quadratic cost function. Unlike the general probability-constrained problem which can be nonconvex and NP-hard, our problem can be reformulated as a convex nonlinear programming (NLP) problem, and efficiently solved using interiorpoint methods. Simulation results show an improved robustness of the proposed beamformer as compared to the existing state-of-the-art robust adaptive beamforming techniques.

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Direct emitter geolocation under local scattering

Bar-Shalom

Weiss

2015

Signal Processing

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Bearing estimation in a Ricean channel .I. Inherent accuracy limitations

Cited by 23 publications

References 13 publications

Steering vector errors and diagonal loading

Steering vector errors and diagonal loading

Robust adaptive beamforming using probability-constrained optimization

Direct emitter geolocation under local scattering

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