Minimum Variance Distortionless Response Research Articles

High-performance deep-sea long-range underwater acoustic communication: Deconvolved conventional beamforming based approach

To address the challenges posed by large propagation loss and severe multipath delay spread, while aiming to improve communication rates in long-range underwater acoustic (UWA) communication, this paper introduces a novel approach: multi-beam (MB) Orthogonal Frequency Division Multiplexing (OFDM) based on deconvolved conventional beamforming (dCv). The proposed method establishes a signal processing framework for UWA channel receivers, wherein received signals corresponding to each angle beam are isolated and concentrated using the dCv technique. Subsequently, the separated signals from each angle undergo maximal-ratio combining (MRC) channel equalization followed by demodulation. Compared to conventional single-beam (SB) processing methods, the proposed approach capitalizes on multipath diversity gain achieved by combining MB outputs originating from various arrival angles. Moreover, employing dCv processing demonstrates superior performance compared to popularly employed beamforming techniques, especially when dealing with paths arriving from closely aligned angles. Additionally, the array beamforming utilized significantly enhances the signal-to-noise ratio (SNR) of each beam output, mitigating the elevated error rates in channel estimation associated with combining low SNR signals received from individual elements. Simulation results utilizing BELLHOP and experimental data from the South China Sea showcase notably improved bit error rate (BER) performance for the proposed method compared to SB equalization, MB-MRC equalization based on conventional beamforming (CBF), minimum variance distortionless response (MVDR), and worst-case performance optimization (WCPO), as well as standard MRC equalization techniques. The proposed receiver achieves error-free decoding results at a communication distance of 80 km with a data rate of 247 bps.

A unified beamforming and source separation model for static and dynamic human-robot interaction.

This paper presents a unified model for combining beamforming and blind source separation (BSS). The validity of the model's assumptions is confirmed by recovering target speech information in noise accurately using Oracle information. Using real static human-robot interaction (HRI) data, the proposed combination of BSS with the minimum-variance distortionless response beamformer provides a greater signal-to-noise ratio (SNR) than previous parallel and cascade systems that combine BSS and beamforming. In the difficult-to-model HRI dynamic environment, the system provides a SNR gain that was 2.8 dB greater than the results obtained with the cascade combination, where the parallel combination is infeasible.

A distortionless convolution beamformer design method based on the weighted minimum mean square error for joint dereverberation and denoising

This paper designs a weighted minimum mean square error (WMMSE) based distortionless convolution beamformer (DCBF) for joint dereverberation and denoising. By effectively using WMMSE with the constraint of distortionless, a DCBF is deduced, where the outputs of the weighted prediction error (WPE) filter and the WPE-based minimum variance distortionless response (MVDR) beamformer are combined to initialize target signal for balancing signal distortion, residual reverberation and residual noise. In addition, two optimization factors are introduced to reduce the reverberation and noise when the initialized target signal is used for the solution of beamformer. As a result, the designed beamformer is presented as a linear combination of the WMMSE-based convolution beamformer (CBF) and weighted power minimization distortionless response (WPD) filter. The experimental results demonstrate the superior performance of the designed beamformer for joint dereverberation and denoising compared to the reference methods.

Supervised AutoEncoder-Based Beamforming Approach for Satellite mmWave Communication

Beamforming is a technique commonly used in wireless communication systems to enhance the signal quality of a receiver. In this study, we compare the performance of an encoder-based beamformer with convolutional neural network (CNN) and minimum variance distortionless response (MVDR) approaches in terms of signal-to-interference-plus-noise ratio (SINR). Our results show that the encoder-based approach achieved an average SINR of 25.82 dB, while the CNN approach achieved an average SINR of 22.40 dB and the MVDR approach achieved an average SINR of 17.64 dB. The performance of the encoder-based approach was found to be superior to that of the CNN approach but much superior to that of the MVDR approach. The encoder-based approach outperformed the CNN approach by 3.42 dB and MVDR approach by 8.18 dB on average. In addition, the unique contribution of our encoder-based approach is presenting a new perspective on beamforming in mmWave communication. We further discuss its potential impact on addressing challenges related to LEO satellite systems.

Adaptive beamforming for maximum SINR in the presence of correlated interferences

The conventional steering vector (SV) based beamformers such as the minimum variance distortionless response (MVDR) can suffer from severe performance degradation due to the signal cancellation in the presence of correlated interferences. A partially correlated interference can be decomposed into two components, the coherent component and the other uncorrelated with the desired signal. The SVs of the desired signal and the coherent interference components compose the composite steering vector (CSV). In this paper, a beamforming method is presented that can achieve the maximum signal-to-interference-plus-noise ratio (SINR) via the use of the CSV and the interference covariance matrix (ICM) formed by the uncorrelated interference components only. The estimates of the CSV and the ICM are extracted from the sample matrix through oblique projections. In the estimation process, neither the interference powers nor the desired signal power is estimated, though the direction estimation is carried out using the multiple signal classification (MUSIC). We make theoretical performance analysis, which shows that the CSV based method is more effective as the correlation between the desired signal and the interferences increases and as the desired signal power becomes small. Simulation results demonstrate that it not only converges very quickly to the maximum SINR but also is robust to pointing errors.

Distortion‐less carrier phase tracking space‐time adaptive processor based on power inversion criterion for GNSS anti‐jamming receiver

AbstractSpace‐time adaptive processor (STAP) has been widely used for global navigation satellite system (GNSS) anti‐jamming receiver due to its good anti‐jamming performance. When direction of satellite is unknown, STAP can be implemented based on power inversion (PI) criterion. However, existing space‐time PI algorithm will introduce tens to hundreds of degrees biases into carrier phase, and sometimes will even cause cycle slips, which will reduce the success rate of ambiguity resolution, ultimately deteriorating positioning accuracy. A distortion‐less carrier phase tracking space‐time PI algorithm is proposed. The main novelty is that the proposed algorithm keeps the coefficients of the temporal taps as real values by imposing constraints on the weights of the antenna array. Several experiments are implemented to verify the effectiveness of the proposed algorithm. For comparison, the results of PI algorithm and minimum variance distortion‐less response (MVDR) algorithm are shown. Results show that when the number, style, and direction of interferences and the direction of GNSS signal vary, different degrees of biases are introduced into carrier phases for the PI and the MVDR algorithm. However, no bias is introduced into the proposed algorithm. As a result, the effectiveness of the proposed algorithm is verified.

Sound field visualization system by MVDR beamforming with augmented reality and point clouds

In previous research, we proposed an augmented reality (AR) and mixed reality (MR) system visualizing the sound field data measured by the beamforming method. Our system visualized the sound pressure as a colormap on the AR/MR device in real-time. Free viewpoint and visualization of a colormap along the spatial geometry observed by the AR/MR sensor enabled us to understand the location of the sound source. On the other hand, the spacial geometry information of the measurement space was used only for display purposes and not in the processing of sound source location estimation. And then the previous system displaying a flat colormap projected on the spatial geometry has not been able to make measurements that precisely in space. In this paper, we propose a sound field measurement and AR/MR visualization system using point cloud data acquired by light detection and ranging (LiDAR) sensor. The MVDR (minimum variance distortionless response) beamforming calculates the beamforming output by using point cloud data. The sound sources are assumed to be located on point clouds, and the system allows fast localization of a large number of sources. In addition, it is possible to interpret the sound field in various ways based on the scattered sound pressure information. We show some basic experiments conducted in an anechoic chamber.

Multiple sources localization with 2D-DFT under distributed massive antenna arrays

Traditional localization approaches, e.g., angle of arrival clustering (AOA-clustering), subspace data fusion, and minimum variance distortionless response, entail eigenvalue decomposition or global grid search within the target area, which could pose significant challenges when applied to massive antenna arrays as a result of their high computational complexity. In this paper, we present a low-complexity localization approach for multiple sources with two-dimensional discrete Fourier transform (2D-DFT). Firstly, we compute the cross-covariance, which could effectively suppress noise and obtain aligned AOAs via 2D-DFT. Then, we utilize phase offset method and total least square solution to obtain accurate position estimates. The proposed approach could achieve a good compromise between performance and computational complexity. The Cramér-Rao Bound (CRB) and simulation results are provided to corroborate the effectiveness and superiority of the proposed approach.

Improved minimum variance distortionless response spectrum method for efficient and robust non-uniform undersampled frequency identification in blade tip timing

Improved minimum variance distortionless response spectrum method for efficient and robust non-uniform undersampled frequency identification in blade tip timing

Robust Adaptive Beamformer Based on Constant Modulus Penalty Criteria

In this study, a robust adaptive beamformer based on constant modulus (CM) criteria is developed to improve the robustness of the array beamforming, which is a reconstructing minimal optimization for solving the mismatch problem of weight vector caused by steering vector mismatch. In the global positioning system (GPS) L1 band, firstly, a GPS array signal is modelled by designing a dual-polarized antenna array. Secondly, the distortion problem of beamforming is formulated in the traditional minimum variance distortionless response (MVDR) beamformer. For repairing the weight vector mismatch problem, a novel beamformer based on the CM envelope response is proposed to reconstruct MVDR beamforming in the array processing. Besides, min-max penalty criteria are used to enable the beamformer to allocate more degrees of freedom (DOFs) when penalizing the MVDR beamformer responses. Finally, an auxiliary two-element real variable is designed to plan the proposed beamformer. But it is still a nonconvex quadratic programming problem, so an alternating direction method of multipliers (ADMM) is employed to transform the objective function into several subproblems. Illustrative numerical simulation results are provided for validating the effectiveness of the proposed beamformer by comparing it with other existing approaches.

Beam-domain deconvolution beamforming algorithm based on compressive sensing

Deconvolution beamforming is often used to improve the azimuth resolution in target detection. However, most traditional deconvolution beamforming algorithms require the array directivity function to be shift-invariant, which are suitable for specific arrays such as linear arrays and circular arrays. A beam-domain deconvolution beamforming method suitable for arbitrary array is proposed based on compressive sensing. First, conventional beamforming is used to obtain several complex beam outputs, then the Sparse Bayesian Learning (SBL) reconstruction algorithm is applied to the beam-domain model to achieve deconvolution of complex beam outputs. The deconvolution process leads to more accurate estimation for the Direction Of Arrival (DOA) for target signals. The proposed method can also effectively reduce the computational complexity of the algorithm by controlling the number of output beams from the conventional beamforming and is applicable for both non coherent and coherent signals. The simulation and experiment results show that the proposed algorithm has azimuth resolution performance comparable to the traditional element-domain SBL beamforming algorithm, and is superior to the conventional beamforming and Minimum Variance Distortionless Response (MVDR) algorithms. When applied to short and dense arrays, the computation complexity of the proposed algorithm is significantly lower than that of the traditional element-domain SBL beamforming algorithm.

Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers

This paper presents an approach to damage identification in beams by modal curvatures based on the use of beamforming algorithms. These processors have been successfully used in acoustics for the last thirty years to solve the inverse problems encountered in source recognition and image reconstruction, based on ultrasonic waves. In addition, beamformers apply to a broader range of problems in which the forward solutions are computable and measurable. This especially concerns the field of structural vibrations, where the use of such estimators has not received attention to date. In this paper, modal curvatures will play the role of replica vectors of the imaging field. The choice to use modal curvatures is motivated by means of numerical studies and experimental tests on a steel beam. Furthermore, we compare the performance of the Bartlett and minimum variance distortionless response (MVDR) beamformers with an estimator based on the simple minimization of the difference between model and measured data. The results suggest that the application of the MVDR beamformer is highly effective, especially in cases of slight damage between two sensors. MVDR enables both damage localization and quantification.

New convex approaches to general MVDR robust adaptive beamforming problems

AbstractConsider general minimum variance distortionless response (MVDR) robust adaptive beamforming problems based on the optimal estimation for both the desired signal steering vector and the interference‐plus‐noise covariance (INC) matrix. The optimal robust adaptive beamformer design problem is an array output power maximization problem, subject to three constraints on the steering vector, namely, a (convex or nonconvex) quadratic constraint ensuring that the direction‐of‐arrival (DOA) of the desired signal is separated from the DOA region of all linear combinations of the interference steering vectors, a double‐sided norm constraint, and a similarity constraint; as well as a ball constraint on the INC matrix, which is centered at a given data sample covariance matrix. To tackle the nonconvex problem, a new tightened semidefinite relaxation (SDR) approach is proposed to output a globally optimal solution; otherwise, a sequential convex approximation (SCA) method is established to return a locally optimal solution. The simulation results show that the MVDR robust adaptive beamformers based on the optimal estimation for the steering vector and the INC matrix have better performance (in terms of, e.g., the array output signal‐to‐interference‐plus‐noise ratio) than the existing MVDR robust adaptive beamformers by the steering vector estimation only.

Deep Learning Approach to Source Localization of Electromagnetic Waves in the Presence of Various Sources and Noise

In this paper, the 3D localization and signal enhancement problem of a source in a noisy environment is addressed using an antenna array to ensure symmetry in communication engineering. The use of machine-learning-dependent convolutional recurrent neural networks (CRNN) and a minimum variance distortionless response (MVDR) beamformer for the localization of the source is developed. Furthermore, to ensure the adaptability of the signal enhancement module during deployment in a new environment or in new conditions, the training of a meta-learning model is conducted. At first, during the localization, the direction of arrival (DoA) estimation in both azimuth and elevation angles is generated. This is generated in a noisy three-dimensional plane and multi-source signal. Employing the DoA estimates, the MVDR is used for the enhancement of the signal source. Verifying the proposed method in the presence of mutual coupling, the two scenarios in communication engineering were simulated using a ray-tracing tool in the form of a real-world problem towards enhancing a signal source in a noisy environment and in the presence of various sources. The results obtained demonstrate how the proposed method outperforms the machine learning and parametric methods. In addition, the trained meta-learning model is employed to demonstrate how the proposed method is adaptable to any environment and still maintains an appreciable quality performance index after retraining with few data. Finally, the results obtained are motivating enough for the practical application of the proposed method.

SATCOM Earth Station Arrays Anti-Jamming Based on MVDR Algorithm

In modern life, a large amount of information is transmitted through satellites. The anti-jamming ability of satellite communication earth stations is the basis for ensuring the smooth flow of information. In the case that multiple small earth stations cannot communicate due to jamming, the minimum variance distortionless response (MVDR) algorithm based on reflector antenna array is proposed. Firstly, the gain expression of satellite communication earth station antenna is derived, and the mathematical model of the received signal of the reflector antenna array is established. Then, the MVDR beamforming algorithm based on the reflector antenna is proposed and applied to the satellite communication earth station reflector antenna array, and the anti-jamming capability of the reflector antenna array using this algorithm is analyzed through simulation. The simulation results show that the earth station reflector antenna array using MVDR algorithm can effectively achieve array gain of the direction of the satellite signal, suppress the jamming signals, increase the output signal to interference plus noise ratio (SINR) of the reflector antenna array, and increase the output signal to jamming plus noise ratio with the increase in the number of snapshots.

Low-Complexity Adaptive Beamforming Algorithm With High Dimensional and Small Samples

A large-scale array (LSA) inevitably encounter scenarios with a small number of samples, and its beamformer suffers from high computational complexity. High computational complexity prevents the system from being used in practical online engineering applications. The complex vector of the beamformer weights can be expressed as the product of training snapshots and the signal steering vector, and a coefficient vector, since the optimal weight vector is a linear combination of basis vectors of the signal-plus-interference subspace. In this study, a new adaptive beamformer is developed on the basis of the minimum variance distortionless response (MVDR) criterion and kernel techniques. The new beamformer only needs to compute the inversion of a low-dimensional Gram matrix instead of the high-dimensional sample covariance matrix, which significantly reduces the calculation cost. Moreover, an efficient loading parameter calculation method (only related to the received matrix and not required user-defined parameters) is derived, which can adaptively suppress the mismatches of the ill-conditioned Gram matrix. Furthermore, a fast version of the new beamformer is formulated for the LSA under the scanning mode. Simulation results demonstrate that the new beamformer achieves better performance and a lower computation load than existing algorithms for a small number of samples. Especially, insufficient samples and high computational complexity problems are more frequently aroused in the space-time broadband array signal processing. Interestingly, the new techniques can be successfully extended to wideband array signal processing and yield satisfactory beam pattern shapes.

Design of a robust MVDR beamforming method with Low-Latency by reconstructing covariance matrix for speech enhancement

Aiming at solving the problems of the conventional minimum variance distortionless response (MVDR) beamformer in practical applications, such as the sensibility of the steering vector mismatch and beampattern distortion, a robust broadband MVDR beamforming method with low-latency by reconstructing covariance matrix is proposed and applied to speech enhancement with a linear microphone array in this paper. In this work, some important steps are optimized, and the main contribution is to consider the problem of correlation terms generated by the low latency. Firstly, the direction of arrival (DOA) is corrected and the steering vector is estimated based on the sparsity of the DOAs corresponding to the sound sources, which improves the ability of anti-mismatches in the steering vector. Secondly, the correlation terms between the sound sources and noise are estimated and eliminated by the Capon power within the eigen-subspace, and the indirect dominant method is used to eliminate the correlation terms between the sound sources, so that the covariance matrix is reconstructed to obtain a more robust MVDR beamformer. Thirdly, the problem of white noise amplification at low frequency bins is analyzed, and a white noise gain (WNG) modification method is proposed to obtain a compromise between the interference suppression and WNG. In the experiments, the TIMIT corpus is used to generate the multi-channel speech data set, and the performance of the proposed method is evaluated with different DOAs and input signal to interference plus noise ratios (SINRs). The experimental results show that the proposed method can effectively suppress the interferences and reduce the noise with strong robustness.

Direction of Arrival Estimation for Single Microelectromechanical Systems Vector Hydrophone Using Modified Wavelet Packet De-Noising

The complex and variable ocean environment introduces complex noise to the signal received by microelectromechanical systems (MEMS) vector hydrophones, which seriously affects target location. To improve the detection efficiency and broaden the application range of vector hydrophone, this paper proposed a novel modified Wavelet packet de-noising method, known as the Wavelet packet energy (WPE) de-noising method. This method utilizes Shannon entropy to determine the optimal decomposition levels of Wavelet packets, and calculates the energy of reconstructed signals in each frequency band to construct feature vectors. The Wavelet packet with the larger energy is selected to reconstruct the signal until the root mean square error of the original signal and the reconstructed signal is minimized. thereby obtaining an optimal solution. The performance of this proposed method is evaluated through both indoor and outdoor experiments. The experimental results demonstrate that the developed method is effective in Direction of Arrival (DOA) estimation of single frequency signals and is feasible in low signal-to-noise ratio situations. The method improves the azimuth discrimination ability of the Minimum Variance Distortionless Response algorithm, and DOA estimation errors are all within 1°. Furthermore, the indoor and outdoor experiments also demonstrate that the WPE de-noising method solves the group delay and distortion of signals caused by conventional filtering.

Acoustic source localisation using vibroacoustic beamforming

For acoustic source localisation in a fluid, sound pressure is traditionally measured using a microphone array, and acoustic beamforming is then applied to the pressure signals to localise the source. However, in some engineering applications, it is not feasible to place the microphone/hydrophone array in the fluid to localise an acoustic source. Hence, this work proposes a vibroacoustic beamforming for sound source localisation which applies the beamforming procedure to the measured vibration signals at the surface of a structure in contact with the fluid in which the acoustic source is located. To evaluate the localisation performance of the proposed method, a numerical study is conducted to localise a monopole source in a room using vibration data obtained from an elastic panel on the room wall. To simulate more realistic situation, different levels of signal to noise ratio are considered by moving the source away from the wall-mounted sensors and consequently altering the direct acoustic field (signal). Moreover, commonly used frequency domain beamforming techniques namely conventional beamforming, functional beamforming, minimum variance distortionless response and minimum power distortionless response are applied to the measured signals to localise the source. The performance of each technique is then evaluated by comparing the beamforming output obtained from vibroacoustic beamforming with that of acoustic beamforming and by quantifying the dynamic range and spatial resolution for each method. It can be concluded that vibroacoustic beamforming can be used for acoustic localisation as an alternative to the acoustic beamforming.

Designing FDA Radars Robust to Contaminated Shared Spectra

This paper considers the problem of jointly designing the transmit waveforms and weights for a frequency diverse array (FDA) in a spectrally congested environment in which unintentional spectral interferences exist. Exploiting the properties of the interference signal induced by the processing of the multi-channel mixing and low-pass filtering FDA receiver, the interference covariance matrix structure is derived. With this, the receive weights are formed using the minimum variance distortionless response (MVDR) method for interference cancellation. Owing to the fact that the resulting output signal-to-interference-plus-noise ratio (SINR) is a function of the transmit waveforms and weights, as well as due to the ever-greater competition for the finite available spectrum, a joint design scheme for the FDA transmit weights and the spectrally compatible waveforms is proposed to efficiently use the available spectrum while maintaining a sufficient receive SINR. The performance of the proposed technique is verified using numerical simulations in terms of the achievable SINR, spectral compatiblity, as well as several aspects of the synthesized waveforms.