Adaptive eigen-projection beamforming algorithms for 1D and 2D antenna arrays

Foutz, J.; Spanias, Andreas; Bellofiore, S.; Balanis, C.A.

doi:10.1109/lawp.2003.811322

Cited by 22 publications

(4 citation statements)

References 7 publications

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“…To reduce the implementation complexity, the optimization problem (1) can be converted into an unconstrained one, leading to the well-known GSC. The weight vector is computed by (2) where is the quiescent filter, which is matched to the desired signal spatial signature, is an weight vector updated through an adaptive algorithm [1], [11], and is an blocking matrix such that and , where is an appropriate-sized identity matrix. Although GSC enjoys many advantages from some practical perspectives, it is still subject to the performance tradeoff between the convergence rate and the steady-state SINR output.…”

Section: Problem Formulationmentioning

confidence: 99%

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A Novel GSC Beamformer Using a Combination of Two Adaptive Filters for Smart Antenna Array

Sun

Wang²,

Tian

2012

Antennas Wirel. Propag. Lett.

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A novel affine combination of two adaptive filters is proposed to enhance the performance of a generalized sidelobe canceller (GSC) in smart antenna array processing. The proposed method exploits a mixing vector, rather than a scalar as considered in current studies, to weight the coefficients of each component filter in such a way that the overall performances, such as convergence speed and the steady-state performance, of conventional GSC are improved. Attributing to more degrees of freedom (DOFs) offered by the mixing vector, the combined GSC presents significant performance improvement over current approaches. The optimal design as well as adaptive implementation of the overall combined GSC filter is developed with detailed theoretical analysis on the convergence rate and the steady-state excess mean-square error. The effectiveness of the proposed method is demonstrated by simulation studies.Index Terms-Adaptive combination, adaptive filter, antenna array, generalized sidelobe canceller (GSC).

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Section: Problem Formulationmentioning

confidence: 99%

“…Although some works have been concentrated on improving the adaptive algorithms [2], [3], a challenging problem remains unsolved by conventional techniques that there is a tradeoff between the convergence speed and the performance at the steady state.…”

Section: Introductionmentioning

confidence: 99%

A Novel GSC Beamformer Using a Combination of Two Adaptive Filters for Smart Antenna Array

Sun

Wang²,

Tian

2012

Antennas Wirel. Propag. Lett.

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“…In these techniques, robustness to steering vector uncertainty is increased at the expense of a reduction in noise and interference suppression. Recently, some developments based on worst-case performance optimization [17,18] and subspace projection [19][20][21][22] were proposed. The worst-case approaches ensure that the response of the beamformer is above a given level for all steering vectors whose distance to the presumed steering vector is less than a certain distance.…”

Section: Introductionmentioning

confidence: 99%

A recursive Bayesian beamforming for steering vector uncertainties

Han

Zhang

2013

EURASIP J. Adv. Signal Process.

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A recursive Bayesian approach to narrowband beamforming for an uncertain steering vector of interest signal is presented. In this paper, the interference-plus-noise covariance matrix and signal power are assumed to be known. The steering vector is modeled as a complex Gaussian random vector that characterizes the level of steering vector uncertainty. Applying the Bayesian model, a recursive algorithm for minimum mean square error (MMSE) estimation is developed. It can be viewed as a mixture of conditional MMSE estimates weighted by the posterior probability density function of the random steering vector given the observed data. The proposed recursive Bayesian beamformer can make use of the information about the steering vector brought by all the observed data until the current short-term integration window and can estimate the mean and covariance of the steering vector recursively. Numerical simulations show that the proposed beamformer with the known signal power and interference-plus-noise covariance matrix outperforms the linearly constrained minimum variance, subspace projection, and other three Bayesian beamformers. After convergence, it has similar performance to the optimal Max-SINR beamformer with the true steering vector.

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“…Spanias and Foutz [6] developed algorithms that involve eigenspace projections in select sub-spaces. These are enhanced 1-D and 2-D [13] versions of the eigenspace projection algorithm [7] developed originally for system identification. Basically, the projections of the gradient algorithm in different subspaces allow the use of distinct step sizes that have the ability to control the convergence speed in each sub-space.…”

Section: Introductionmentioning

confidence: 99%

Channel equalization using the G-Probe

Panayiotou

Spanias

Tsakalis

2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)

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In this paper, we describe the gradient-with-projection-onoptimal-bounding-ellipsoid (G-Probe) algorithm, for use in channel equalization. The G-Probe is compared to the NLMSbased equalizer in the case where the transmitted signal is attenuated by strong and periodic fading, and the channel undergoes structured interference at the same time. In such scenarios small perturbations in the channel may cause the NLMS parameters to drift, leading to large instantaneous errors referred to as burst phenomena. The G-Probe provides both MSE and instantaneous steady-state error performance guarantees resulting in reduced error bursts. This paper provides simulation results in the performance of G-Probe for a linear ISI channel with AWGN and its performance in a channel with periodic fading. Finally, the paper provides comparison results of GProbe against three adaptive algorithms; the OBE, NLMS, and RLS.

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Adaptive eigen-projection beamforming algorithms for 1D and 2D antenna arrays

Cited by 22 publications

References 7 publications

A Novel GSC Beamformer Using a Combination of Two Adaptive Filters for Smart Antenna Array

A Novel GSC Beamformer Using a Combination of Two Adaptive Filters for Smart Antenna Array

A recursive Bayesian beamforming for steering vector uncertainties

Channel equalization using the G-Probe

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