MVDR Algorithm Research Articles

Deep ad-hoc beamforming based on speaker extraction for target-dependent speech separation

Recently, the research on ad-hoc microphone arrays with deep learning has drawn much attention, especially in speech enhancement and separation. An ad-hoc microphone array may cover such a large area where multiple speakers stand far apart and talk independently. Therefore, it is important to extract and trace a specific speaker in the ad-hoc array, which is called target-dependent speech separation, aiming to extract a target speaker from a mixed speech. However, this technique has not been explored yet. In this paper, we propose deep ad-hoc beamforming based on speaker extraction, which is to our knowledge the first work for target-dependent speech separation based on ad-hoc microphone arrays and deep learning. The algorithm contains three components. First, we propose a supervised channel selection framework based on speaker extraction, where the estimated utterance-level SNRs of the target speech are used as the basis for the channel selection. Second, we apply the selected channels to a deep learning based MVDR algorithm, where a single-channel speaker extraction algorithm is applied to each selected channel for estimating the mask of the target speech. We conducted an extensive experiment on WSJ0-adhoc corpus and Libri-adhoc40 corpus. Experimental results demonstrate the effectiveness of the proposed method in both simulation and real scenarios.

Hybrid MVDR-LMS beamforming for Massive MIMO

<p>With the high speed of communication in LTE-5G, fast beamforming techniques need to be adopted. The training time required to form and steer the main lobes toward 5G multiple users must be short. Least-Mean-Square (LMS) training time is not suitable to work with in LTE-5G, but it has a good performance in forming multiple beams to large number of users and producing nulls in the interference direction. In this paper, an optimized hybrid MVDR-LMS beamforming algorithm is proposed to reduce the time required to estimate the antenna’s weights. This optimization is made by the benefit of previously set weights calculated using MVDR algorithms. The performance of the proposed hybrid MVDR-LMS algorithm tested using MATLAB 2016a.</p>

Recursive Updating Algorithm for Robust Adaptive Beamforming in a Uniform Circular Array

When the phase-mode transformation technique is used in a uniform circular array, its output performance is known to degrade owing to the approximations applied to the formulation. In this paper, we develop a robust recursive updating algorithm based on the worst-case performance optimization and phase-mode transformation in the uniform circular array, which provides efficient robustness not only against the signal steering vector mismatches, but also against the transformation errors. The proposed algorithm belongs to the class of the diagonal loading technique and the transformation matrix belongs to a certain ellipsoid set. The weight vector is updated by the Lagrange multiplier method, in which the parameters are derived simply. The proposed algorithm has a closed-form solution, in which we analyse the reasonable ranges of two key parameters. The convergence performance and the output SINR performance are also analysed. In additional, the implementation complexity costs of the proposed algorithm and MVDR algorithm are presented in this paper. Our robust algorithm has a low implementation complexity cost and achieves the mean output array SINR consistently close to the optimal one. Simulation results are presented to compare the performances of our algorithm with the conventional algorithms.

Subspace projection semi-real-valued MVDR algorithm based on vector sensors array processing

The existing SRV-MVDR (semi-real-valued MVDR) algorithm is only applicable to the pressure sensors array and cannot distinguish between mirror radiation sources and real source (DOA fuzzy), and this paper presents a method of vector sensors array SRV-MVDR based on subspace projection. Compared with the existing SRV-MVDR, only half spectrum search is needed to solve the DOA fuzzy problem. No subsequent discrimination is needed. The data collected by vector sensors array are processed jointly by using the idea of dimensionality reduction so that it satisfies the processing condition of SRV-MVDR method. Theoretical analysis and computer simulation show that this method has robustness. At the same time, it is more suitable for low SNR and small snapshots and has broad prospects in practical engineering.

On the Analysis Performance of Updating Weight for Estimation Target of Drone System

In this paper, we propose the method which desired signal is estimated by updating the weight of the MVDR algorithm. The MUSIC algorithm is generally a lot of used in the direction of arrival estimation method. The MUSIC algorithm has a good resolution because of using subspace techniques consisting of a signal subspace and a noise subspace. The processor capability of drone system is required low power consumption and low computation complexity because it uses a microprocessor. If the drone system has a lot of computation complexity, the desired signal cannot be estimated. This paper study a method estimating the desired signal with a simple calculation. The proposed method is updated weight by the covariance matrix of MVDR algorithm. Through simulation, we analyse performance by comparing MVDR, MUSIC and the proposed method. In the simulation results, the proposed method is the same as the MUSIC algorithm in direction of arrival estimation. Since the proposed method has no subspace, it reduces computational complexity than MUSIC algorithm. The desired signal estimation of the proposed method is superior to the MVDR algorithm.

Research on the near field sound source localization method for small snapshots

The performance of standard MVDR algorithm would be decreased sharply when the sampled data is small. In this paper, vector MVDR algorithm and the diagonal loading are proposed to improve the robustness. Firstly, the sample date covariance matrix is whitened to obtain more uniform noise, and then use the standard deviation of covariance matrix as the loading amount to complete diagonal loading. Finally, the effectiveness and robustness of the proposed algorithm is verified by simulation. Simulation results reveal that it has higher positioning accuracy with small sample, with the increase of SNR and snapshots, the estimation’s RMSE of target location decreases continuously.

Research on near field acoustic source localization algorithm based on arbitrary planar vector array

In order to solve the problem of near acoustic target location, vector hydrophone array with arbitrary formation is proposed to locate near field acoustic source. In this paper, firstly, the near field measurement model of vector hydrophone array is derived detailed. Then, introduces the conventional beamforming and MVDR algorithm based on vector hydrophone array. Finally, the performance of the algorithm is verified by simulation. The results show that the vector MVDR has higher target resolution, and the error decreases with the increase of SNR. It is more suitable for underwater near field regional warning.

基于光纤光栅阵列和MVDR算法的声发射定位

基于光纤光栅阵列和MVDR算法的声发射定位

Weather Radar Range Resolution Improvement based on Steepest Descent MVDR Algorithm

Weather Radar Range Resolution Improvement based on Steepest Descent MVDR Algorithm

The Research of Cylindrical Noise Source Distribution Using Near-Field High-Resolution Location Methods Based on MVDR Algorithm

According to the lack of cylinder-model noise source localization technology based on near-field focused beam-forming, the paper aimed to find a near-field high-resolution method of the cylindrical noise source which is applied to measure the model as cylinder-models. This method established a near-field measurement model of noise sources using cylindrical measurement surface, and applied MVDR algorithm to noise sources localization. Reconstruct the cylinder measurement surface through the joint amplitude-phase compensation to achieve the noise source high-resolution localization and relative intensity estimates in the near-field. Verify the availability of this method through the simulations, which provided a theoretical basis for the next practical engineering application of the cylinder-model noise source localization based on near-field focused beam-forming.

Research on the Improved Least Variance Sound Vector Array DOA Estimation Algorithm

This paper proposes a MVDR algorithm based on quaternion model, expresses output of single 2D vector hydrophone as quaternion, establishes the DOA estimation equation of the MVDR algorithm based on quaternion domain, simulates and studies the visual tracking technology based on the particle filtering image frame and improves the four-element number domain MVDR algorithm by multiple signal classification. The four-element number domain MVDR algorithm and improved algorithm reduces the memory demand and computing complexity and improves the DOA estimation precision and resolution capability of the coherent sound source. The analysis and simulation results demonstrate validity of the algorithm, so this algorithm is valuable in engineering application.

A novel ULA-based geometry for improving AOA estimation

Due to relatively simple implementation, Uniform Linear Array (ULA) is a popular geometry for array signal processing. Despite this advantage, it does not have a uniform performance in all directions and Angle of Arrival (AOA) estimation performance degrades considerably in the angles close to endfire. In this article, a new configuration is proposed which can solve this problem. Proposed Array (PA) configuration adds two elements to the ULA in top and bottom of the array axis. By extending signal model of the ULA to the new proposed ULA-based array, AOA estimation performance has been compared in terms of angular accuracy and resolution threshold through two well-known AOA estimation algorithms, MUSIC and MVDR. In both algorithms, Root Mean Square Error (RMSE) of the detected angles descends as the input Signal to Noise Ratio (SNR) increases. Simulation results show that the proposed array geometry introduces uniform accurate performance and higher resolution in middle angles as well as border ones. The PA also presents less RMSE than the ULA in endfire directions. Therefore, the proposed array offers better performance for the border angles with almost the same array size and simplicity in both MUSIC and MVDR algorithms with respect to the conventional ULA. In addition, AOA estimation performance of the PA geometry is compared with two well-known 2D-array geometries: L-shape and V-shape, and acceptable results are obtained with equivalent or lower complexity.

QRD-based MVDR algorithm for adaptive multipulse antenna array signal processing

A numerically robust, computationally efficient processing scheme for adaptive multipulse radar is described. Its purpose is to detect a target embedded in clutter and directive electromagnetic (EM) interference by processing the echoes received by an array of antennas on-board an aircraft or satellite. The algorithm which performs an efficient QR decomposition of the underlying space-time data matrix, may be implemented in parallel on either a triangular array of processors or using a novel lattice-type structure of smaller triangular array processors. A Monte Carlo simulation study has been performed to evaluate the performance of the processing scheme in terms of steady-state improvement factor, transient response, 2D filtering capability, and numerical robustness.