A Robust Adaptive Beamformer Based on Worst-Case Semi-Definite Programming

Yu, Zhu Liang; Gu, Zhenghui; Zhou, Jianjiang; Li, Yuanqing; Ser, Wee; Er, Meng Hwa

doi:10.1109/tsp.2010.2058107

Cited by 57 publications

(47 citation statements)

References 24 publications

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“…The population size N p = 20 gives the best values for maximum function evaluations of 50000 and maximum generations of 2500. The performance of the proposed algorithm is compared with algorithms in the literature such as RR [31], SQP [38,39], RCB [13] and IRCB [37]. The proposed algorithm is evaluated with no error in geometry and 50% uniform error in the geometry and compared with RR, SQP, RCB and IRCB algorithms.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Differential Evolution Approach for Robust Adaptive Beamforming Based on Joint Estimation of Look Direction and Array Geometry

Mallipeddi¹,

Lie²,

Suganthan³

et al. 2011

PIER

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Abstract-The performance of traditional beamformers tends to degrade due to inaccurate estimation of covariance matrix and imprecise knowledge of array steering vector.The inaccurate estimation of covariance matrix can be attributed to limited data samples and the presence of desired signal in the training data. The mismatch between the actual and presumed steering vectors can be due to the error in the position (geometry) and/or in the look direction estimate. In this paper, we propose a differential evolution (DE) based robust adaptive beamforming that is able to achieve near optimal performance even in the presence of geometry error. Initially, we estimate an optimal steering vector by maximizing and minimizing the signal power in and out of the desired signal's angular range, respectively. Then, we estimate the look direction and reconstruct the covariance matrix. Based on the obtained steering vector, estimate for look direction and reconstructed covariance matrix, near optimal output SINR, can be obtained with the increase in the input SNR without observing any saturation even in the presence of geometry error. Numerical simulations are presented to demonstrate the efficacy of the proposed algorithm.

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Section: Simulation Resultsmentioning

confidence: 99%

A Differential Evolution Approach for Robust Adaptive Beamforming Based on Joint Estimation of Look Direction and Array Geometry

Mallipeddi¹,

Lie²,

Suganthan³

et al. 2011

PIER

View full text Add to dashboard Cite

show abstract

“…The performance of adaptive beamformers degrades with these imperfections. Thus, various robust adaptive beamforming techniques have been proposed in the past decades ( [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21], and many references therein). One popular and very effective approach to process the steering vector error is based on the principle of worst-case performance optimization [4].…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive beamforming using an iterative FFT algorithm

et al. 2014

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“…However, at least two reasons prevent adaptive beamformer to achieve the desired performance. Firstly, the weights cannot be continually updated due to limited computational resources available in many applications [1] . Secondly, the environment will change as the jammers vary over time in practice.…”

Section: Introductionmentioning

confidence: 99%

Covariance matrix transforming based moving jammers suppression method

Liao

et al. 2013

2013 International Conference on Radar

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In this paper, an adaptive moving jammers suppression method based on covariance matrix transforming is proposed. According to the knowledge of parameters of the jammers, we construct a transform matrix thus the covariance matrix could be forecasted through iteratively matrix transforming. This method will save many computation resources and ensure the application of adaptive beamforming in largescale arrays. Simulation results demonstrate the effectiveness of the proposed method.

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A Robust Adaptive Beamformer Based on Worst-Case Semi-Definite Programming

Cited by 57 publications

References 24 publications

A Differential Evolution Approach for Robust Adaptive Beamforming Based on Joint Estimation of Look Direction and Array Geometry

A Differential Evolution Approach for Robust Adaptive Beamforming Based on Joint Estimation of Look Direction and Array Geometry

Robust adaptive beamforming using an iterative FFT algorithm

Covariance matrix transforming based moving jammers suppression method

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