Adaptive uncertainty based iterative robust capon beamformer

Lie, Joni Polili; Li, Xiaohui; Ser, Wee; See, Chong-Meng Samson; Lei, Lei

doi:10.1109/icassp.2010.5496306

Cited by 8 publications

(7 citation statements)

References 12 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

Self Cite

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show abstract

“…First, we look at the beampattern plot for the proposed robust beamforming approach as compared to some of the existing robust beamforming approaches at 10 dB SNR. Figure 1 shows the beampattern plot comparison of the proposed approach against the RCB and the IRCB [32] approaches. These are obtained from one of the realization in the simulation.…”

Section: Simulation Resultsmentioning

confidence: 99%

Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

Mallipeddi¹,

Lie²,

Suganthan³

et al. 2011

PIER B

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Abstract-The presence of desired signal in the training data for sample covariance matrix calculation is known to lead to a substantial performance degradation, especially when the desired signal is the dominant signal in the training data. Together with the uncertainty in the look direction, most of the adaptive beamforming solutions are unable to approach the optimal performance. In this paper, we propose an evolutionary algorithm (EA) based robust adaptive beamforming that is able to achieve near optimal performance. The essence of the idea is to shape the array beam response such that it has maximum response in the desired signal's angular range and minimum response in the interferences' angular range. In addition, the approach introduces null-response constraints deduced from the array observation to achieve better interference cancelation performance. As a whole, the proposed optimization is solvable using an improved variant of the differential evolution (DE) algorithm. Numerical simulations are also presented to demonstrate the efficacy of the proposed algorithm.

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“…For the first step, we fixãγ and find the optimalw, which gives rise to a solution likẽ w = (ã H γ R −1 xã γ ) −1 R −1 xã γ . Then for the second step, we insert thew back into (15) and solve the actualãγ. After some simple mathematical operations, the optimization problem is reduced to…”

Section: Wl-rcb1: Robust Against the Whole Uncertainty In The Augmentmentioning

confidence: 99%

“…This way, the uncertainty level is evaluated conservatively by using the upper bound, thus leading to the overestimation of uncertainty. The implication of overestimating the uncertainty is the SINR performance loss from the degradation in the interference suppression capability of the robust beamformer design [13][14][15][16][17].…”

Section: Wl-rcb2: Exploiting the Asv Structure Informationmentioning

confidence: 99%

A robust approach to optimum widely linear MVDR beamformer

Wang¹,

Lie²,

See

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In many array processing, the received signals are nonstationary, or in particular, noncircular. Widely linear minimum variance distortionless response (WL MVDR) beamformers can exploit the noncircularity of received signals and improve the performance of the conventional MVDR beamformer. However, in the optimum WL MVDR beamformer, the array steering vector (ASV) and the signal noncircularity coefficient should be known a priori for the signal of interest. This requirement puts strict limitation to the implementation of this beamformer. We therefore in this paper propose a robust approach to the optimal WL MVDR beamformer that can deal with the uncertainties in the ASV and noncircularity coefficient. Two variants of the proposed approach are developed based on the treatment of the uncertainties. By doing so, the requirement on the exact information is relaxed while the performance improvement can still be obtained. Simulation studies are also provided to illustrate the performance of the proposed approach.

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Adaptive uncertainty based iterative robust capon beamformer

Cited by 8 publications

References 12 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

Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

A robust approach to optimum widely linear MVDR beamformer

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