Robust Adaptive Beamforming for Steering Vector Uncertainties Based on Equivalent Doas Method

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

doi:10.2528/pier07102202

Cited by 35 publications

(26 citation statements)

References 22 publications

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“…It is well known that adaptive beamformers can suffer significant performance degradation when the environment, sources, or sensor array is violated and this may cause a mismatch between the assumed and actual signal steering vectors. A lot of algorithms have been proposed to improve the robustness of adaptive beamforming [6][7][8][9]. However, these methods mostly focus on optimizing the direction of arrival (DOA) or signal steering vector only.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Beamforming Based on Particle Filter With Noise Unknown

Liu¹,

Gu²,

Shi³

et al. 2009

PIER

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Abstract-Adaptive beamforming, which uses a weight vector to maximize the signal-to-interference-plus-noise ratio (SINR), is often sensitive to estimation error and uncertainty in the parameters, such as direction of arrival (DOA), steering vector and covariance matrix. Robust beamforming attempts to mitigate this sensitivity and diagonal loading in sample covariance matrix can improve the robustness. In this paper, beamformer based on particle filter (PF) is proposed to improve the robustness by optimizing the diagonal loading factor in sample covariance matrix. In the proposed approach, the level of diagonal loading is regarded as a group of particles and optimized using PF. In order to compute the post probability of particles beyond the knowledge of noise, a simplified cost function is derived first. Then, a statistical approach is developed to decide the level of diagonal loading. Finally, simulations with several frequently encountered types of estimation error are conducted. Results show a better performance of the proposed beamformer than other typical beamformers using diagonal loading. In particular, the prominent advantage of the proposed approach is that it can perform well even noise and error in the steering vector are unknown.

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Section: Introductionmentioning

confidence: 99%

Robust Adaptive Beamforming Based on Particle Filter With Noise Unknown

Liu¹,

Gu²,

Shi³

et al. 2009

PIER

Self Cite

View full text Add to dashboard Cite

show abstract

“…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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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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“…[1][2][3][4][5][6][7][8][9][10][11][12]. The directionsof-arrival (DOA) estimation [13][14][15][16][17][18] of signals impinging on an array of sensors is a fundamental problem in array processing, and many methods have been proposed for its solution. Eigen-decompositionbased methods, including multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT), have high resolution DOA estimation performance, but they fail to work in coherent signal condition.…”

Section: Introductionmentioning

confidence: 99%

Blind Direction of Arrival Estimation of Coherent Sources Using Multi-Invariance Property

Zhang¹,

Yu²,

Feng³

et al. 2008

PIER

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Abstract-Blind direction of arrival (DOA) estimation algorithms of coherent sources using multi-invariance property is presented in this paper. ESPRIT-like algorithm in [23] can estimate DOA of coherent signal, but its performance is without satisfaction. We reconstruct the received signal to form data model with multi-invariance property, and then multi-invariance ESPRIT and multi-invariance MUSIC algorithms for coherent DOA estimation are proposed in this paper. Our proposed algorithms can resolve the DOAs of coherent signals. They have much better DOA estimation performance than ESPRITlike algorithm. Meanwhile they identify more DOAs than ESPRIT-like algorithm. The simulation results demonstrate their validity.

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Robust Adaptive Beamforming for Steering Vector Uncertainties Based on Equivalent Doas Method

Cited by 35 publications

References 22 publications

Robust Adaptive Beamforming Based on Particle Filter With Noise Unknown

Robust Adaptive Beamforming Based on Particle Filter With Noise Unknown

Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

Blind Direction of Arrival Estimation of Coherent Sources Using Multi-Invariance Property

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