Minimum sensitivity based robust beamforming with eigenspace decomposition

Wang, Jun; Zhang, Wei; Liu, Wei

doi:10.1007/s11045-016-0424-1

Cited by 10 publications

(8 citation statements)

References 20 publications

(29 reference statements)

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“…In this section, the performance of the proposed method is evaluated using several beamformers widely used in the literature. One desired signal and two interference signals impinge on the array from directions θ 1 = 5 matrix reconstruction [19] with desired signal steering vector estimation [18] (SRNSV), worst-case-based beamformer (WC) [13], minimum sensitivity eigenspace-based beamformer (MSESB) [12], maximum entropy method (MEPS) [37], tridiagonal loading beamformer (TLBF) [33], and SMI [38]. The angle sector of the desired signal is set to…”

Section: B Beamformer Performancementioning

confidence: 99%

“…However, errors exist in practical antenna arrays due to the effect of the signal-of-interest (SOI) on the training data and a small number of snapshots can severely degrade performance. Several adaptive beamforming algorithms have been proposed to solve these problems including diagonal loading [7]- [9], eigenspace projection [10]- [12], uncertainty constraint [13], [14],…”

Section: Introductionmentioning

confidence: 99%

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Large Direction-of-Arrival Mismatch Correction for Adaptive Beamforming

Chang

Yang

Zhang³

et al. 2022

IEEE Access

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Direction-of-arrival (DOA) mismatch can degrade the performance of adaptive beamforming algorithms. Thus, a projection method is proposed to correct this mismatch. In a beamforming algorithm, the DOA error is usually regarded as a steering vector error which is corrected using a steering vector optimization algorithm. This approach can provide an optimal steering vector but ignores the actual DOA estimate. The proposed algorithm provides correction after DOA estimation but before beamforming to improve both the DOA estimation accuracy and beamforming gain. First, the signal-to-noise ratio (SNR) of the signal is estimated and used to regularize the covariance matrix. Then, an estimated steering vector with DOA close to the true value is determined based on a minimum number of projections. Numerical results are presented to verify the effectiveness of the proposed method for DOA estimation correction. In most cases, this method improves the performance of the beamforming algorithms without changing them.

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Section: B Beamformer Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Large Direction-of-Arrival Mismatch Correction for Adaptive Beamforming

Chang

Yang

Zhang³

et al. 2022

IEEE Access

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“…The paper by Wang et al (2016) presented an enhanced eigenspace-based beamformer (ESB) by using the minimum sensitivity criterion. The proposed beamformer has significantly improved robustness against steering vector errors.…”

Section: Beamforming For Sensor Arraysmentioning

confidence: 99%

Array signal processing and systems

Liao

Madanayake

Agathoklis

2018

Multidim Syst Sign Process

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We are delighted to present this special issue of Multidimensional Systems and Signal Processing on Array Signal Processing and Systems. Sensor arrays play an important role in spatio-temporal signal processing with applications spanning across multiple fields such as electromagnetic, acoustics, ultrasonic and seismic processing systems. This plethora of possible applications has sparked a large number of new theoretical developments and array processing systems in the last few years.In the field of electromagnetic sensor (antenna) arrays, there is much interest in the electronically-steerable radio-frequency (RF) beams that can be obtained from phased-array antennas. In the microwave bands, phased-array antennas take both analog and digital signal processing approaches, and find applications in wireless base-stations, radar sensors, space communications, and radio telescopes. In the emerging mm-wave regime, modern applications of array processing are of paramount importance for wireless communications. In particular, emerging 5G systems based on massive-MIMO basestations depend on array processing to mitigate the effects of high path loss and blockages in an urban environment. Acoustic sensor (microphone) arrays are extremely useful for audio and multimedia applications, including high-fidelity sound recording, immersive multimedia and augmented/virtual reality. Ultrasound sensor arrays find extensive applications in biomedical engineering where they are used for imaging the human body and in structural health monitoring of airframes B Bin Liao

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“…Eigenspace-based methods can improve the robustness of the beamformer by taking advantage of the eigenspace characteristics of the received data. Accroding to the minimum sensitivity principle, the optimal projection of the weighted vector can be obtained by using the sensitivity function in the enhanced eigenspace beamformer [17]. By combining the approximate expression of signal subspace projection and the two-stage beamformer structure, the beamformer can eliminate the subspace projection operation and the element number estimation [18].…”

Section: Introductionmentioning

confidence: 99%

Robust Beamforming Based on Weighted Vector Norm Regularization

Ren

Wang

Zhang³

et al. 2021

IEEE Access

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Steering vector mismatch rapidly degrades the performance of the minimum variance distortionless response beamformer. To solve this problem, a robust beamforming method based on weighted vector norm regularization is proposed. First, the factors affecting the robustness of the beamformer are analyzed. Second, by introducing the weighted vector norm, an optimization problem is constructed to increase the robustness of the beamformer. Furthermore, the regularization coefficient is provided to achieve a balance between the output power and the robustness of the beamformer. Meanwhile, a method of finding the appropriate regularization coefficient is provided. Then, simulations of an irregular arc array are carried out, showing that the proposed method is robust to the snapshot number. Finally, the results and data analysis indicate the effectiveness of the proposed method.

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Minimum sensitivity based robust beamforming with eigenspace decomposition

Cited by 10 publications

References 20 publications

Large Direction-of-Arrival Mismatch Correction for Adaptive Beamforming

Large Direction-of-Arrival Mismatch Correction for Adaptive Beamforming

Array signal processing and systems

Robust Beamforming Based on Weighted Vector Norm Regularization

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