Robust adaptive beamforming under uncertainty in source direction-of-arrival

This paper studies and discusses robust minimum variance distortionless response (MVDR) beamformer with subarray multistage processing for circular hydrophone arrays, which is an extension of the multistage MVDR beamformer with line microphone arrays for noise reduction. The basic theory is to divide the hydrophones into a certain number of groups with each group only having two hydrophones for MVDR beamforming. After the beamforming, it is equivalent that the number of hydrophones is half of that at the adjoining former stage. Then the grouping and beamfoming processing will be repeated till that there is only one hydrophone. This subarray multistage MVDR beamformer has the following properties in the isotropic noise field: first, it is much more robust than the conventional MVDR beamformer; second, its array gain is lower than that of the conventional MVDR beamformer; moreover, the larger the sensor number in each subarray is, the higher the array gain will be, but with lower white noise gain. By choosing a proper hydrophone number of each subarray, a tradeoff can be made between the array gain and robustness.

Beamforming techniques are becoming popular in a wide spectrum of applications thanks to their promising performance. We consider applying the minimum variance distortionless response (MVDR) beamformer in a dynamic scenario, where the desired speaker, the noise components or microphone array may move. The generalized sidelobe canceler (GSC) structure offers a convenient implementation of the MVDR, allowing to track the noise efficiently. Yet, noise reduction is degraded during speech tracking. We propose a QR Decomposition (QRD) based MVDR beamformer, which allows to track both speech and noise variations in dynamic scenarios without sacrificing noise reduction during speech segments. The proposed method utilizes the QRD algorithm for the implementation of a complete system required for the realization of an MVDR beamformer, which includes the identification of noisy speech or noise-only time-frequency bins, speech tracking and noise tracking. The proposed QRD based MVDR is efficiently implemented, and its overall computational complexity is O(M2), with M being the number of microphones. The advantages of the proposed method over the GSC are exemplified in a synthetically simulated scenario.

Microphone arrays based on the minimum variance distortionless response (MVDR) beamformer are among the most popular for speech enhancement applications. The original MVDR is excessively sensitive to source location and microphone gains. Previous research has made MVDR practical by successfully increasing the robustness of MVDR to source location, and MVDR-based microphone arrays are already commercially available. Nevertheless, MVDR performance is still weak in cases where microphone gain variations are too large, e.g., for circular arrays of directional microphones. In this paper we propose an improved MVDR beamformer which takes into account the effect of sensors (e.g. microphones) with arbitrary, potentially directional responses. Specifically, we form estimates of the relative magnitude responses of the sensors based on the data received at the array and include those in the original formulation of the MVDR beamforming problem. Experimental results on real-world audio data show an average 2.4 dB improvement over conventional MVDR beamforming, which does not account for the magnitude responses of the sensors.

Publication in the conference proceedings of EUSIPCO, Marrakech, Morocco, 2013

In this paper, the direction of arrival (DOA) and adaptive antenna array beamforming for the smart antenna applications are realized by using Minimum Variance Distortionless Response (MVDR) algorithm of DOA estimation. The MVDR supported by joint approximate diagonalization of eigenmatrices (JADE) algorithm is used to estimate the direction of arrivals of coherent signals in non-coherent groups. The generalized steering vectors using JADE algorithm are first estimated, and then the spectrum methods are realized to estimate the DOA of each signal. Root mean square error (RMSE) values are computed in different scenarios for the proposed method as well as JADE-MUSIC and the corresponding results are compared. Although both methods resolved the signals correctly, simulation results show that JADE based MVDR has better performances in the cases of low number of antenna elements and snapshots. The DOAs obtained are then processed using LMS adaptive beamforming algorithm to steer the main lobe of the radiation pattern to desired angles (signals) and the nulls to undesired angles (signals). The simulation results also reveal that adaptive beamforming is successfully done by annihilating the effects of undesired signals significantly.

In contrast with the common delay-and-sum (DAS) beamformer, adaptive beamformers can be used to increase the resolution of acoustic measurement systems without sacrificing sidelobe levels. The non-linear nature of adaptive beamforming methods may cause changes to measured scattered amplitude distributions. For example, large differences in speckle statistics have been observed in some cases when using the adaptive minimum variance distortionless response (MVDR) beamformer in medical ultrasound, unless preventative measures were implemented. Low complexity adaptive (LCA) beamforming techniques have shown similar, but smaller, differences. Recently, experiments have been conducted in the Oslofjord, Norway, to determine whether low-complexity adaptive (LCA) beamforming techniques can improve the performance of bathymetric echosounders. When using LCA with these types of sonar systems, it is important to understand any changes to the backscatter statistics, as these changes may affect, e.g., seafloor classification methods. In this talk we will present comparisons between backscatter statistics estimated using a weighted delay-and-sum beamformer and those estimated using a low complexity adaptive beamformer. We will discuss any differences seen, possible mechanisms causing these differences and potential remedies. [Research supported by The Norwegian Research Council and Kongsberg Maritime. The first author is employed in Kongsberg Maritime.]

The acoustic vector sensor (AVS) array is a powerful tool for direction of arrival (DOA) estimation of underwater targets. However, traditional DOA estimation algorithms generally suffer from low signal-to-noise ratio (SNR) as well as snapshot deficiency. A novel signal self-nulling based DOA estimation method is proposed. Firstly, achieve the crude estimation of the desired DOA using the traditional algorithms. Secondly, set the observation angular interval around the crudely estimated DOA. Thirdly, make the observation direction vary in the observation angular interval, and for each imaginary DOA, calculate the amplitude response of the minimum variance distortionless response (MVDR) beamformer. Finally, the pseudo spatial spectrum is achieved. Computer simulations verify that the proposed method is efficient in DOA estimation, especially in low SNR and insufficient snapshot data scenarios.

Microphone array (MA) own substantial gain in evaluation due to the high spatial diversity to suppress unwanted noise, third-party talker, background noise, interfering signals coming from undesired directions. Minimum Variance Distortionless Response (MVDR) beamformer is an effective solution for extracting the clean speech data while mitigating all total noise power in different recording environment. However, in realistic situation, because of the moving head, the error of estimating transfer function, the complex and annoying recording scenario, the different microphone sensitivities, the inaccurate MA configuration, the imprecise estimation of other acoustic factors, MVDR beamformer’s evaluation often decreased in the the term of speech quality and speech intelligibility. The existence of remained noise, speech distortion decrease the overall MVDR beamformer’s performance. In this paper the author proposed using an appropriate spectral mask for reducing speech distortion. The obtained promising result has confirmed the effectiveness of the author’s method for improving the MVDR beamformer’s performance in adverse recording situations.

The optimal microphone array includes two processing blocks ‐ minimum variance distortionless response (MVDR) beamformer and the single‐channel Wiener filter, which acts as post‐filter. The main drawback of MVDR beamformer is the cancellation of the desired speech signal and its degradation in multi‐path wave propagation environment. To make the adaptive algorithm robust against room reverberation and to prevent desired signal cancellation, an estimation of the unknown desired speaker's transfer function was proposed. The estimation is based on the imperfect signal and the interference covariance matrices estimated from available microphone signals during speaker activity and pause of speech respectively. As MVDR beamformer suppresses coherent interference, post‐filter has to reduce diffuse acoustic noise. The post‐filter proposed in this paper is developed under assumption that complex coherence function is unknown but time invariant. The additional improvement of the post‐processing algorithm on low frequencies is obtained by combining a priori noise power attenuation factor for diffuse noise field with estimated one. The proposed algorithm is tested on simulated room with reverberation, and compared with some known post‐processing algorithms with rather good results.

A broadband high resolution algorithm is proposed to estimate direction of arrival (DOA) of the source based on two stages beamforming and the sub array partition approach. The two stages beamforming scheme includes the sub array broadband beamforming stage and the minimum variance distortionless response (MVDR) beamforming stage in sub array level. Consequently, the proposed algorithm can be applied to broadband source detection and holds the high resolution ability meanwhile. What is more important is that it can be calculated very fast, thus it is suitable for realtime applications. Simulation results show the proposed algorithm works effectively.

60GHz wireless communication technology is considered to be one of the most promising technology in future wireless communication domains. The information of the DOA (direction of arrival) is the foundation for adaptive array antenna to adjust the optimal beam direction as well as track mobile terminal, and the method to obtain the precise DOA is the key technology of the adaptive beam forming. The STMV (Steered Minimum Variance) algorithm is a kind of high resolution wideband adaptive beam forming method, which takes advantages of the STCM (Steered Covariance Matrix) to estimate the adaptive beam forming weight vector so as to obtain DOA information. In this paper, we attempt to bring STMV algorithm into DOA estimation for 60GHz signals. It is proved practicable to apply STMV algorithm in 60GHz DOA estimation via MTALAB simulation. Comparing with the MVDR (Minimum Variance Distortionless Response) algorithm, the STMV algorithm shows excellent performance that it needs less fast beats in the required resolution, also has smaller variance.

A new approach to implement minimum variance distortionless response (MVDR) beamforming is introduced for coherent plane-wave compounding (CPWC). MVDR requires the covariance matrix of the incoming signal to be estimated and a spatial smoothing approximation is usually adopted to prevent this calculation from being underconstrained. In the new approach, we analyze MVDR as a spatial filter that decorrelates signals received at individual channels before summation. Based on the analysis, we develop two MVDR beamformers without using any spatial smoothing. First, MVDR weights are applied to the received signals after accumulating the data over transmits at different angles, while the second involves weighting the data collected in individual transmits and compounding over the transducer elements. In both cases, the covariance matrix is estimated using a set of slightly different combinations of the echo data. We show the sufficient statistic for this estimation that can be described by approximating the correlation among the backscattered ultrasound signals to their spatial coherence. Using the van Cittert-Zernike theorem, their statistical similarity is assessed by relating the spatial coherence to the profile of the source intensity. Both spatial-coherence-based MVDR beamformers are evaluated on data sets acquired from simulation, phantom, and in vivo studies. Imaging results show that they offer improvements over simple coherent compounding in terms of spatial and contrast resolutions. They also outperform other existing MVDR-based methods in the literature that are applied to CPWC.

The minimum variance distortionless response (MVDR) beamformer is very sensitive to errors, such as the the finite snapshots and the steering vector errors. A small mismatch can lead to serious degradation to its performance, especially at high signal-to-noise ratio(SNR). In this paper, a new robust MVDR beamformer is proposed. Our analysis starts from the physical meaning of the MVDR weight vector formula. Based on this analysis, DOA matrix is constructed in the single uniform linear array. Through the decomposition of this DOA matrix, we could obtain the actual signal modes contained in the covariance matrix of the snapshots. Then the weight vector could be modified to solve the robustness problem of MVDR beamforming. The computer simulation shows the effectiveness of this method.

Acoustic signals received by platform mounted sonar arrays can be spatially processed to enhance the detection of targets in the presence of both ambient and platform generated (self) noise. Ambient noise in the ocean, such as that due to distant shipping or biological choruses, are known to be spatially correlated. The platform generated noise will be of near-field origin and may not be received by all elements in the array. In this paper we investigate the performance of the minimum variance distortionless response (MVDR) beamformer and the recently introduced Fourier integral method (FIM) and compare their performances with the conventional beamformer. Real passive sonar data, obtained from a platform mounted sparse linear array of hydrophones, is used to study the performance of the beamformers in a typical sonar environment. It is shown that in the absence of self noise, when the array is accurately calibrated the MVDR beamformer will perform very well, but when sensor gain or phase errors are present the performance of the MVDR beamformer is degraded. Further, the MVDR beamformer is unable to reject the self noise which is not "seen" by the entire array. FIM however seems to perform well and a modified version of FIM, which we call weighted FIM (WFIM), is shown to perform better and is at worst comparable to a well calibrated MVDR beamformer.

In dynamic environments, fast-moving interferers traverse resolution cells quickly, challenging the minimum variance distortionless response (MVDR) beamformer to place accurate notches in the direction of interferers. The hybrid MVDR (HMVDR) beamformer generates serendipitous wide notches by factoring the beampattern into adaptive and fixed components. The adaptive MVDR factor implicitly places notches in the interferer direction for suppression and reduces some background noise, while the fixed conventional beamformer (CBF) factor improves white noise gain. The HMVDR product beampattern flexibly allocates degrees of freedom (DoFs) between the adaptive and fixed components. However, optimizing DoF allocation for the adaptive part remains a challenge, especially in environments with an unknown time-varying number of interferers. To address this challenge, we propose the universal HMVDR (UHMVDR) beamformer, which integrates a mixture of experts' universal algorithms into the HMVDR beamformer, blending different factorizations to dynamically allocate DoFs between the adaptive and fixed components. Simulations and microphone array experiments confirm that the UHMVDR beamformer achieves better array output power and white noise gain than competing beamformers. [Work supported by ONR 321US.]