Sensitivity analysis of MVDR and MPDR beamformers (original) (raw)
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Finite data performance analysis of MVDR beamformer with and without spatial smoothing
IEEE Transactions on Signal Processing, 1992
Recently, the performance of a minimum variance distortionless response (MVDR) beamformer has been extensively studied for the case when a true or asymptotic covariance matrix is available. In practical situations, however, we only have a finite number of snapshots of data from which the array covariance matrix can be estimated. In this paper, we analyze the finite-data performance of this beamformer with and without spatial smoothing, using first-order perturbation theory. In particular, we develop expressions for the mean values of the power gain in any direction of interest, the output power and the norm of the weight-error vector, as a function of the number of snapshots and the number of smoothing steps. We show that, in general, the smoothing, in addition to decorrelating the sources, can also dlleviate the effects of finite-data perturbations.
Performance analysis of the minimum variance beamformer in the presence of steering vector errors
IEEE Transactions on Signal Processing, 1996
We present an analysis of the signal-to-interferenceplus-noise ratio (SINR) at the output of the minimum variance beamformer. The analysis is based on the assumption that the signals and noise are Gaussian and that the number of samples is large compared to the array size, and it yields an explicit expression for the SINR in terms of the different parameters affecting the performance, including signal-to-noise ratio (SNR), interference-to-noise ratio (INR), signal-to-interference ratio (SIR), angular separation between the desired signal and the interference, array size and shape, correlation between the desired signal and the interference, and finite sample size.
Discrete interferences optimum beamformer in correlated signal and interfering noise
International Journal of Electrical and Computer Engineering (IJECE), 2022
This paper introduces a significant special situation where the noise is a collection of D-plane interference signals and the correlated noise of D+1 is less than the number of array components. An optimal beamforming processor based on the minimum variance distortionless response (MVDR) generates and combines appropriate statistics for the D+1 model. Instead of the original space of the N-dimensional problem, the interference signal subspace is reduced to D+1. Typical antenna arrays in many modern communication networks absorb waves generated from multiple point sources. An analytical formula was derived to improve the signal to interference and noise ratio (SINR) obtained from the steering errors of the two beamformers. The proposed MVDR processor-based beamforming does not enforce general constraints. Therefore, it can also be used in systems where the steering vector is compromised by gain. Simulation results show that the output of the proposed beamformer based on the MVDR processor is usually close to the ideal state within a wide range of signal-to-noise ratio and signal-to-interference ratio. The MVDR processor-based beamformer has been experimentally evaluated. The proposed processor-based MVDR system significantly improves performance for large interference white noise ratio (INR) in the sidelobe region and provide an appropriate beam pattern.
2017
The performance of Minimum Variance Distortionless Response (MVDR) beamformer is sensitive to errors such as the steering vector errors, the finite snapshots, and unsatisfactory null-forming level. In this paper, a combination of MVDR with linear antenna arrays (LAAs) for two scanning angles process in the azimuth and elevation are used to illustrate the MVDR performance against error which results in acquiring the desired signal and suppressing the interference and noise. The impact of various parameters, such as the number of elements in the array, space separation between array elements, the number of interference sources, noise power level, and the number of snapshots on the MVDR are investigated. The MVDR performance is evaluated with two important metrics: beampattern of two scanning angles and Signal to Interference plus Noise Ratio (SINR). The results found that the MVDR performance improves as the number of array elements increases. The beampattern relies on the number of e...
Interference cancellation beamforming robust to pointing errors
IET Signal Processing, 2013
The conventional Wiener-Hopf beamformer is subject to substantial performance degradation in the presence of steering vector pointing errors. By removing the effects of the desired signal, the modified Wiener-Hopf beamformer avoids this problem but allows cochannel interferences to pass through in order to maximise the signal-to-noise ratio. In this study, a novel array beamformer is proposed, which not only reduces the effect of pointing errors, but also asymptotically provides complete interference rejection. In particular, the proposed beamformer utilises a vector space projection method and employs a one-step computation for the desired signal power. Using this, the effects of the desired signal can be extracted to form the desired-signal-absent covariance matrix. Thus, a weight vector orthogonal with the interference subspace can be constructed. Numerical results demonstrate the superior performance of the proposed beamformer in the presence of pointing errors relative to other existing approaches such as 'diagonal loading', 'robust Capon' and 'signal subspace projection' beamformers.
Modifying MVDR Beamformer for Reducing Direction-of-Arrival Estimation Mismatch
Arabian Journal for Science and Engineering, 2015
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.
Performance analysis of the minimum variance beamformer
IEEE Transactions on Signal Processing, 1996
We present an analysis of the signal-to-interferenceplus-noise ratio (SINR) at the output of the minimum variance beamformer. The analysis is based on the assumption that the signals and noise are Gaussian and that the number of samples is large compared to the array size, and it yields an explicit expression for the SINR in terms of the different parameters affecting the performance, including signal-to-noise ratio (SNR), interference-to-noise ratio (INR), signal-to-interference ratio (SIR), angular separation between the desired signal and the interference, array size and shape, correlation between the desired signal and the interference, and finite sample size.
Modern Applied Science, 2016
Wireless data traffic is in a continuous growth, and there are increasing demands for wireless systems that provide deep interference suppression and noise mitigation. In this paper, adaptive beamforming (ABF) technique for Smart Antenna System (SAS) based on Minimum Variance Distortionless Response (MVDR) algorithm connected toCircular Antenna Array (CAA) is discussed and analyzed. The MVDR performance is evaluated by varying various parameters; namely the number of antenna elements, space separation between the elements, the number of interference sources, noise power label, and a number of snapshots. LTE networks allocate a spectrum band of 2.6 GHz is used for evaluating the MVDR performance. The MVDR performance is evaluated with two important metrics; beampattern and SINR. Simulation results demonstrate that as the antenna elements increase, the performance of the MVDR improves dramatically. This means the performance of MVDR greatly relies upon the number of the elements. Half...
irnetexplore.ac.in
Adaptive Beamforming approach have provided significant amount of contribution in mitigating interference in wireless communication. This paper presents an Adaptive Beamforming approach using MVDR (Minimum Variance Distortionless Response) for interference suppression and to form beam in the estimated direction. Non-Blind algorithm with MVDR beamforming approach have been proposed in this paper. Simulated results show that the proposed method provides better performance with narrower beamwidth and higher gain.
IEEE Transactions on Signal Processing, 2008
An interesting relationship between the probability-constrained and worst-case optimization based robust minimum variance (MV) beamformers has been discovered. It is shown that both in the cases of circularly symmetric Gaussian and worst-case distributions of the steering vector mismatch, the probability-constrained robust MV beamforming problem can be tightly approximated as a convex second-order cone programming (SOCP) problem. The latter problem is mathematically equivalent to that resulting from the deterministic worst-case approach and, therefore, probability-constrained beamformers can be interpreted and implemented using their deterministic worst-case counterparts. However, an important advantage of the developed probability-constrained MV beamformers with respect to their standard worst-case counterparts is that the former approaches enable to explicitly quantify the parameters of the uncertainty region in terms of the beamformer outage probability.