Interference covariance matrix reconstruction via steering vectors estimation for robust adaptive beamforming

Lu, Zhenxing; Li, Yunjie; Gao, Meiguo; Zhang, Yangrui

doi:10.1049/el.2013.2070

Cited by 28 publications

(36 citation statements)

References 8 publications

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“…and then ε/τ = 99λ 1 , through the same way with (22) to (30). In very low SNR case, λ 1 ≈ σ 2 n , therefore the DL level in (19) is about ε/τ = 99σ 2 n . Therefore, the ISRAB equivalents to the DL beamformer with level 99σ 2 n .…”

Section: D) Working Principlementioning

confidence: 79%

Robust adaptive beamforming with enhancing the interference suppression capability

Liu

2019

SIP

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The steering vector mismatch causes signal self-nulling for adaptive beamforming when the training data contain the desired signal component. To prevent signal self-nulling, many beamformers use robust technology, which is usually equivalent to the diagonal loading approach. Unfortunately, the diagonal loading approach achieves better signal enhancement at the cost of losing its interference suppression capability, especially at high input signal-to-noise ratio. In this paper, a novel robust adaptive beamforming method is developed to improve the interference suppression capability. The proposed beamformer is based on the worst-case performance optimization technology with a new estimated steering vector and a special set parameter. Firstly, a subspace which is orthogonal to the interference's steering vector is obtained by using the interference-plus-noise covariance matrix; then a new steering vector which is orthogonal to each interference's steering vector is estimated; finally, the beamformer's weight is solved with the worst-case performance optimization technology with a special set parameter. Theoretical analysis of the interference suppression principle is analyzed in detail, and some simulation results are presented to evaluate the performance of the proposed beamformer.

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Section: D) Working Principlementioning

confidence: 79%

Robust adaptive beamforming with enhancing the interference suppression capability

Liu

2019

SIP

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“…Additionally, the signal-to-interference-plus-noise ratio (SINR) increased significantly by controlling the null in the direction of interferences and directing the strong radiation beam towards the signal of interest. The DM-AIS represents an intelligent solution to the steering mismatch error with a long time being spent in the iteration process Lu [17] presented a steering vector mismatch model, which caused by array calibration error based on MVDR. The proposed algorithm overcomes the array calibration error by reconstructing the covariance matrix.…”

Section: Related Workmentioning

confidence: 99%

Modifying MVDR Beamformer for Reducing Direction-of-Arrival Estimation Mismatch

Abdulrahman

Rahman

Hassnawi³

et al. 2015

Arab J Sci Eng

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The minimum variance distortionless response (MVDR) beamforming algorithm is used in smart antenna design for wireless communication. The operation of MVDR is based on finding the optimum weight to direct the main lobe beam to the desired user location with a unity gain. MVDR is very sensitive to signature vector mismatch. This mismatch occurs due to waveform deformation, local scattering, imperfect array element calibration and element shape distortion, which leads to errors in finding the direction of arrival (DOA) of the signal. In this paper, a new technique to modify the MVDR is presented, modelled and evaluated. The proposed algorithm is named modified MVDR (MMVDR) and is dependent on reconstructing the signature vector (steering vector) and the covariance matrix to introduce accurate beamformer weight by re-localization the reference element to be in the middle of ULA, rather than at one end side. The new reference position partitions the array's elements into two groups around this reference, which leads to treat received signals with identical phase along the array's elements, as well as increasing the degree of freedom to deals with different types of uniform arrays. The evaluation results show that MMVDR outperforms MVDR with respect to beamformer accuracy, system cost, processing time and signal classification to overcome the errors in DOA estimation which occur due to fabrication and calibration errors.

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“…From (9), the jammer suppression capability is enhanced due to the adaptive weight vector projected onto the noise subspace completely. Therefore, the performance of the conventional OP algorithm is always superior to the SMI algorithm.…”

Section: Conventional Op Algorithmmentioning

confidence: 99%

“…Here, Θ is an angular sector in which the desired signal is located [7], [8], [9]. In this paper, we need not estimate the direction of the desired signal 0 θ angle, since the integration interval is chosen as the orthogonal complement sector of 3dB mainlobe beamwidth.…”

Section: Conventional Op Algorithmmentioning

confidence: 99%

Improved orthogonal projection adaptive beamforming by using reconstructed interference covariance matrix

Yang

Yan

Zhang

et al. 2014

2014 International Radar Conference

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When the desired signal is present in the training snapshots, the performance of conventional orthogonal projection (OP) adaptive beamforming degrades severely due to the desired signal cancellation effect. To overcome this deficiency, the improved orthogonal projection (IOP) adaptive beamforming by using reconstructed interference covariance matrix is proposed. In the proposed algorithm, the interference covariance matrix is firstly reconstructed by integrating the Capon spatial spectrum over a region separated from the desired signal direction. Subsequently, the jammer subspace is estimated, and then the adaptive weight vector is calculated using conventional OP algorithm. The simulation results show that the corresponding output signal-to-jammer-plus-noise ratio (SJNR) performance of proposed IOP algorithm is almost same with the optimum beamformer with the desired signal in the training snapshots. Therefore, the proposed IOP algorithm is significantly effective for the actual system.

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Interference covariance matrix reconstruction via steering vectors estimation for robust adaptive beamforming

Cited by 28 publications

References 8 publications

Robust adaptive beamforming with enhancing the interference suppression capability

Robust adaptive beamforming with enhancing the interference suppression capability

Modifying MVDR Beamformer for Reducing Direction-of-Arrival Estimation Mismatch

Improved orthogonal projection adaptive beamforming by using reconstructed interference covariance matrix

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