Robust Adaptive Beamforming for a Class of Gaussian Steering Vector Mismatch

Gu, Yujie; Shi, Zhiguo; Chen, Kang Sheng; Yu, Li

doi:10.2528/pier08010202

Cited by 12 publications

(9 citation statements)

References 27 publications

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“…where w θ is the top ϑ-th individual and ϑ = 0.5N P where N P denotes the number of individuals in [29,30]: T c ∈ [0.1T max , 0.8T max ] and cp ∈ [2,10]. The algorithmic description of the DE algorithm can be summarized as STEP 1: Set the generation count G = 0, and randomly initialize a population of N P individuals P G = {w 1,G , .…”

Section: Solving For Beamformer's Weightmentioning

confidence: 99%

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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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Section: Solving For Beamformer's Weightmentioning

confidence: 99%

“…Traditional adaptive beamforming techniques work effectively only under the assumption that precise knowledge of the desired signal steering vector is known [1][2][3]. Any violation or mismatch between the assumed (nominal) and the actual knowledge will cause a substantial degradation in their performance [4].…”

Section: Introductionmentioning

confidence: 99%

Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

Mallipeddi¹,

Lie²,

Suganthan³

et al. 2011

PIER B

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show abstract

“…Therefore, we should resort the other approaches to estimate the unwrapped phase. In this paper, we exploit the robust beamforming approach [17][18][19][20][21] to estimate the unwrapped phase.…”

Section: Signal Model and Problem Statementmentioning

confidence: 99%

Application of Array Processing Techniques in Multibaseline Insar for High-Resolution Dem Reconstruction

Mao¹,

Liao²

2008

PIER M

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Abstract-This work is concerned with generating the digital elevation models (DEMs) from the SAR images of the region of interest using multiple sensor arrays that are fixed on the distributed satellites. We present an exact estimate of the unwrapped phase and the relationship between the unwrapped phase and the terrain height. The optimum scheme that jointly processes the signals from all sensors is based on the model of the multibaseline joint block vector. The method can simultaneously coregister the SAR images, phase unwrapping and DEM generation in the presence of the large coregistration errors. The performance of our approach is verified by a series of simulation experiments based on the distributed sensor arrays.

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“…In the past, beamforming was studied extensively for desired signal enhancement and interference signals suppression [1][2][3][4]. Given the exact look direction, many traditional wideband beamformers can work effectively and achieve a satisfactory output signal-to-interference-plus-noise ratio (SINR) [5,6].…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Wideband Beamforming Using Probability-Constrained Optimization

Liu¹,

Liu²,

Zhao³

et al. 2014

PIER C

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Abstract-The existing robust narrowband beamformers based on probability-constrained optimization have an excellent performance as compared to several state-of-the-art robust beamforming algorithms. However, they always assume that the steering vector errors are small enough. Without this assumption, we extend the probability-constrained approach to a wideband beamformer. In addition, a novel robust wideband beamformer with frequency invariance constraints is proposed by introducing the response variation (RV) element. Our problems can be reformulated in a convex form as the iterative second order cone programming (SOCP) problem and solved effectively using well-established interior point method. Compared with existing robust wideband beamformers, a more efficient control over the beamformer's response against the steering vector errors is achieved with an improved output signal-to-interference-plus-noise ratio (SINR).

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Robust Adaptive Beamforming for a Class of Gaussian Steering Vector Mismatch

Cited by 12 publications

References 27 publications

Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

Application of Array Processing Techniques in Multibaseline Insar for High-Resolution Dem Reconstruction

Robust Adaptive Wideband Beamforming Using Probability-Constrained Optimization

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