Minimum Variance Distortionless Response Research Articles

A Pre-Separation and All-Neural Beamformer Framework for Multi-Channel Speech Separation

Thanks to the use of deep neural networks (DNNs), microphone array speech separation methods have achieved impressive performance. However, most existing neural beamforming methods explicitly follow traditional beamformer formulas, which possibly causes sub-optimal performance. In this study, a pre-separation and all-neural beamformer framework is proposed for multi-channel speech separation without following the solutions of the conventional beamformers, such as the minimum variance distortionless response (MVDR) beamformer. More specifically, the proposed framework includes two modules, namely the pre-separation module and the all-neural beamforming module. The pre-separation module is used to obtain pre-separated speech and interference, which are further utilized by the all-neural beamforming module to obtain frame-level beamforming weights without computing the spatial covariance matrices. The evaluation results of the multi-channel speech separation tasks, including speech enhancement subtasks and speaker separation subtasks, demonstrate that the proposed method is more effective than several advanced baselines. Furthermore, this method can be used for symmetrical stereo speech.

An improved TOA estimation algorithm based on denoised MVDR for B5G positioning

This paper proposes an improved minimum variance distortionless response (MVDR) based TOA estimation algorithm for 5G NR signals under multipath environments. The proposed algorithm achieves high resolution by exploiting a large number of subcarriers of 5G signals and reduces the dimension of the covariance matrix involved in MVDR substantially by utilizing a novel smoothing scheme. Since MVDR requires a relatively high signal-to-noise ratio (SNR), a denoising method is used to improve the TOA estimation performance. Simulation results show that the proposed algorithm achieves much higher resolution than the Bartlett beamformer (BF) and the TOA estimation accuracy remains high over a wide range of SNRs.

A DOA Estimation Algorithm Based on Eigenvalues Ranking Problem

Although traditional subspace direction of arrival (DOA) algorithms enable super-resolution, the algorithm performance declines sharply when the number of sources is incorrectly estimated. This article proposes a new DOA estimation method as an optimization problem similar to the conventional minimum variance distortionless response (MVDR) that fully considers the eigenvalues ranking problem. After a mathematical derivation, a concise DOA estimation expression is given based on the mapping between eigenvalues. As the algorithm considers the relative ranking of eigenvalues, the number of sources is not required for DOA estimations. Therefore, the performance of the algorithm does not degrade due to incorrect estimations in the number of sources. To enhance the algorithm performance, this article analyzes the reasons for the formation of pseudo peaks based on the eigenvalues and gives the corresponding boundary conditions. Based on the analysis of white noise, in order to overcome the influence of color noise, this article gives a way to deal with color noise. Finally, the theory of the proposed algorithm is verified through simulation experiments, and its performance is demonstrated through comparative experiments with MUSIC under white and color noise.

A Low-Complexity DOA Estimation Algorithm for Distributed Source Localization

As a key aspect of radio monitoring and positioning, direction of arrival (DOA) techniques have been widely used in wireless communications, radar detection, and radio astronomy. DOA estimation can be used to locate a source in space and provide technical support for signal enhancement. However, in complex electromagnetic environments, distributed source models have high complexity; thus, the existing DOA estimation algorithms for distributed sources cannot adapt to application scenarios that require high real-time performance. This letter derives a low-complexity distributed source model and proposes a novel DOA estimation algorithm based on the minimum variance distortionless response criterion. The experimental results demonstrate that the proposed algorithm is superior to some other algorithms in terms of parameter estimation accuracy and source resolution for distributed sources.

Robust Multi-Beam Secure mmWave Wireless Communication for Hybrid Wiretapping Systems

In this paper, we consider the physical layer (PHY) security problem for hybrid wiretapping wireless systems in millimeter wave transmission, where active eavesdroppers (AEs) and passive eavesdroppers (PEs) coexist to intercept the confidential messages and emit jamming signals. To achieve secure and reliable transmission, we propose an artificial noise (AN)-aided robust multi-beam array transceiver scheme. Leveraging beamforming, we aim to minimize transmit power by jointly designing the information and AN beamforming, while satisfying valid reception for legitimate users (LUs), per-antenna power constraints for transmitter, as well as all interception power constraints for eavesdroppers (Eves). In particular, the interception power formulation is taken into account for protecting the information against hybrid Eves with imperfect AE channel state information (CSI) and no PE CSI. In light of the intractability of the problem, we reformulate the considered problem by replacing non-convex constraints with tractable forms. Afterwards, a two-stage algorithm is developed to obtain the optimal solution. Additionally, we design the received beamforming weights by means of minimum variance distortionless response, such that the jamming caused by AEs can be effectively suppressed. Simulation results demonstrate the superiority of our proposed scheme in terms of energy efficiency and security.

Group delay-based minimum variance distortion-less response cepstral features for speaker identification in whispered speech

Group delay-based minimum variance distortion-less response cepstral features for speaker identification in whispered speech

Broadband Spatial Spectrum Estimation Based on Space-Time Minimum Variance Distortionless Response and Frequency Derivative Constraints

The two-dimensional optimization capability of the space-time minimum variance distortionless response (STMVDR) can improve the spatial spectrum estimation performance of the broadband beamformer with limited samples, but its high sensitivity to frequency errors may lead to the suppression of some narrowband signals not at the grid point of the frequency response calculation. In order to reduce the sensitivity of STMVDR to frequency errors, a broadband spatial spectrum estimation method based on STMVDR and frequency derivative constraints is proposed in this paper. First, based on the beam response of the STMVDR beamformer, an arbitrary order frequency derivative constraint is derived. Second, combining the gain constraint and frequency derivative constraint, the linear constrained minimum variance problem is established to solve the optimal weight of the beamformer. Finally, the calculation method of the broadband spatial spectrum is derived. Numerical simulations show that the proposed method not only solves the problem of frequency mismatch caused by the insufficient number of points for calculating frequency response, but also does not cause the spatial response related peaks to deviate from the true direction due to the broadening of the main lobe of frequency response. In addition, the proposed method can reduce the computational complexity of the algorithm by more than half by increasing the interval of the frequency calculation grid points, and does not significantly loss in the angular resolution capability and estimation accuracy of the algorithm.

Group delay-based minimum variance distortion-less response cepstral features for speaker identification in whispered speech

Group delay-based minimum variance distortion-less response cepstral features for speaker identification in whispered speech

A Full Loading-Based MVDR Beamforming Method by Backward Correction of the Steering Vector and Reconstruction of the Covariance Matrix

In order to improve the performance of the diagonal loading-based minimum variance distortionless response (MVDR) beamformer, a full loading-based MVDR beamforming method is proposed in this paper. Different from the conventional diagonal loading methods, the proposed method combines the backward correction of the steering vector of the target source and the reconstruction of the covariance matrix. Firstly, based on the linear combination, an appropriate full loading matrix was constructed to correct the steering vector of the target source backward. Secondly, based on the spatial sparsity of the sound sources, an appropriate loading matrix was constructed to further suppress interferences. Thirdly, the spatial response power was utilized to derive a more accurate direction of arrival (DOA) of the target source, which is helpful for obtaining a more accurate steering vector of the target source and a more effective covariance matrix iteratively. The simulation results show that the proposed method can effectively suppress interferences and noise.

Simultaneous Execution of Dereverberation, Denoising, and Speaker Separation Using a Neural Beamformer for Adapting Robots to Real Environments

It remains challenging for robots to accurately perform sound source localization and speech recognition in a real environment with reverberation, noise, and the voices of multiple speakers. Accordingly, we propose “U-TasNet-Beam,” a speech extraction method for extracting only the target speaker’s voice from all ambient sounds in a real environment. U-TasNet-Beam is a neural beamformer comprising three elements: a neural network for removing reverberation and noise, a second neural network for separating the voices of multiple speakers, and a minimum variance distortionless response (MVDR) beamformer. Experiments with simulated data and recorded data show that the proposed U-TasNet-Beam can improve the accuracy of sound source localization and speech recognition in robots compared to the conventional methods in a noisy, reverberant, and multi-speaker environment. In addition, we propose the spatial correlation matrix loss (SCM loss) as a loss function for the neural network learning the spatial information of the sound. By using the SCM loss, we can improve the speech extraction performance of the neural beamformer.

Time-Range Adaptive Focusing Method Based on APC and Iterative Adaptive Radon-Fourier Transform

In conventional radar signal processing, the cascade of pulse compression (i.e., matched filter) and Radon-Fourier transform (RFT) can extract the estimated scattering coefficient of the target in the range-velocity dimension through long-time coherent integration (i.e., long-time focusing). However, matched filter has problems such as range sidelobes. RFT belongs to a standard time-dimension matched filter, which will cause velocity sidelobes of strong targets. The range-velocity sidelobes caused by matched filter and RFT will mask other weak targets and affect the subsequent signal processing processes such as target detection and tracking. To suppress range-velocity sidelobes and achieve better range-velocity focusing, this paper proposes a time-range adaptive focusing method named APC-IARFT for short, which is based on adaptive pulse compression (APC) and newly proposed iterative adaptive Radon-Fourier transform (IARFT). In the APC-IARFT method, the radar time-range adaptive focusing consists of two steps: range-dimension adaptive focusing and long-time adaptive focusing in the velocity dimension. The APC method can realize range-dimension adaptive focusing and suppress range sidelobes of strong targets. Then, based on the minimum variance distortionless response (MVDR) formulation, the proposed IARFT method iteratively designs time-dimension adaptive filter of each range-velocity grid according to the received signal processed by APC to suppress velocity sidelobes of strong targets and achieve long-time adaptive focusing. Compared with the conventional cascade of matched filter and RFT, the cascade of matched filter and adaptive Radon-Fourier transform (ARFT), the results show that the proposed time-range adaptive focusing method (i.e., APC-IARFT) is competent for a variety of scenarios.

Mainlobe Deceptive Jammer Suppression in SF-RDA Radar: Joint Transmit-Receive Processing

The problem of mainlobe deceptive jammer suppression with a novel stepped frequency (SF)-receive delay array (RDA) radar is addressed in this paper. In our system design, the linear frequency modulation (LFM) waveform with a SF across the antenna elements is transmitted in a uniform linear array, and the received echo is delayed with a time shift between adjacent antenna elements, introducing extra range-dependent degrees-of-freedom (DOFs) in both transmit and receive domains. Consider a typical form of mainlobe jammers, where multiple false targets are generated in the same transmit pulse or same range bin behind the true target after short time modulation. In this respect, the jammers are discriminated from the true target in the joint transmit-receive spatial frequency domain, and suppressed via a data-dependent beamformer based on the minimum variance distortionless response (MVDR) criterion. To further solve the problem of sample selection due to the pseudo-random distribution of the false targets, two kinds of robust beamformers based on the enhanced Capon power spectrum (ECPS) and the eigen-projection elimination (EPE), are developed to reconstruct the jammer-plus-noise covariance matrix and estimate the steering vector of the true target. The performance of the proposed methods in suppression of mainlobe deceptive jammers is evaluated via numerical simulations and compared with the existing systems and robust beamforming methods.

Multiple‐input multiple‐output sonar adaptive beamforming using transmission diversity smoothing and backward processing

AbstractBenefit from the transmission diversity smoothing (TDS) effect upon coherent targets decorrelation, the kind of adaptive beamformers can be directly applied for multiple‐input multiple‐output (MIMO) sonar applications. Increasing the number of transmitted signals helps improve the TDS‐based decorrelation performance. However, a large number of transmitters is not feasible for those sonar systems mounted on small platforms with limited space. To address the lack of TDS‐based degrees of freedom for smoothing, an improved TDS decorrelation method is proposed for MIMO sonar in this study. With the conjugated MIMO data covariance matrix being left‐multiplied and right‐multiplied, respectively, by the exchange matrix, the authors obtain a backward‐smoothed data covariance matrix. Afterwards, the authors average it with the original MIMO receiving data covariance matrix to reconstruct a new data covariance matrix. Compared with TDS, the improved TDS strengthens the decorrelation performance by increasing the number of covariance matrix for smoothing and thus enables the new rank restored signal covariance matrix to have better non‐singularity. Without increasing any aperture loss and system complexity, a lower correlation between echoes is obtained, and the performance of the minimum variance distortionless response beamformer is hence improved in the presence of coherent targets. The effectiveness of the proposed decorrelation method is verified by numerical simulations and real data from a water tank experiment.

High-dimensional MVDR beamforming based on a second unitary transformation

Minimum variance distortion-less response (MVDR) beamforming is a popular method to spatial filtering among adaptive array signal processing. However, the inversion of sample covariance matrix, especially the high-dimensional complex-valued matrix’s inversion, is an inevitable obstacle to the real-time performance of the beamformer. In order to address this problem, a second unitary transformation (UT) technique is presented to transform the inversion of original high-dimensional (M-dimensional) complex-valued covariance matrix into two low-dimensional (M/2-dimensional) real-valued symmetrical sub-ones’ inversion. The proposed second unitary transformation minimum variance distortion-less response (SUT-MVDR) beamforming converts complex-valued computation to real-valued computation at the first UT stage, based on which the computational complexity of matrix’s inversion is reduced to about one fourth of the original at the second UT stage, depending on two half-size sub-matrices’ inversion. Numerical simulations show that the proposed beamformer has a higher computational efficiency and a better output signal-to-interference-plus-noise ratio (SINR) performance than other similar beamformers.

Effective Dereverberation with a Lower Complexity at Presence of the Noise

Adaptive beamforming and deconvolution techniques have shown effectiveness for reducing noise and reverberation. The minimum variance distortionless response (MVDR) beamformer is the most widely used for adaptive beamforming, whereas multichannel linear prediction (MCLP) is an excellent approach for the deconvolution. How to solve the problem where the noise and reverberation occur together is a challenging task. In this paper, the MVDR beamformer and MCLP are effectively combined for noise reduction and dereverberation. Especially, the MCLP coefficients are estimated by the Kalman filter and the MVDR filter based on the complex Gaussian mixture model (CGMM) is used to enhance the speech corrupted by the reverberation with the noise and to estimate the power spectral density (PSD) of the target speech required by the Kalman filter, respectively. The final enhanced speech is obtained by the Kalman filter. Furthermore, a complexity reduction method with respect to the Kalman filter is also proposed based on the Kronecker product. Compared to two advanced algorithms, the integrated sidelobe cancellation and linear prediction (ISCLP) method and the weighted prediction error (WPE) method, which are very effective for removing reverberation, the proposed algorithm shows better performance and lower complexity.

Waveform optimization with SINR criteria for FDA radar in the presence of signal-dependent mainlobe interference

In this paper, we focus on the design of the transmit waveforms of a frequency diverse array (FDA) in order to improve the output signal-to-interference-plus-noise ratio (SINR) in the presence of signal-dependent mainlobe interference. Since the classical multi-carrier matched filtering-based FDA receiver cannot effectively utilize the waveform diversity of FDA, a novel FDA receiver framework based on multi-channel mixing and low pass filtering is developed to keep the separation of the transmit waveform at the receiver side, while preserving the FDA range-controllable degrees of freedom. Furthermore, a range-angle minimum variance distortionless response beamforming technique is introduced to synthesize receiver filter weights with the ability to suppress a possible signal-dependent mainlobe interference. The resulting FDA transmit waveform design problem is initially formulated as an optimization problem consisting of a non-convex objective function and multiple non-convex constraints. To efficiently solve this, we introduce two algorithms, one based on a signal relaxation technique, and the other based on the majorization minimization technique. The preferable performance of the proposed multi-channel low pass filtering receiver and the optimized transmit waveforms is illustrated using numerical simulations, indicating that the resulting FDA system is not only able to effectively suppress mainlobe interference, but also to yield estimates with a higher SINR than the FDA system without waveform optimization.

An Efficient Spectrum Sharing Strategy for Radar and Communication With Space-Time Design

This brief considers an efficient spectrum sharing strategy for radar and communication with space-time design, in which the performances of radar and communication systems are simultaneously optimized, under the radar constant modulus constraint and communication energy constraint. The degrees of freedom under the designer’s control are the spatial-temporal transmit waveform and receive filter for radar, as well as the communication system spatial-temporal codebook matrix. For the designed non-convex optimization, an alternating procedure based on majorization-minimization (MM) framework, minimum variance distortionless response (MVDR) and alternating direction method of multipliers (ADMM) is derived for the triplet variables design. Finally, thorough numerical results show the merits of the proposed schemes.

An Oblique Projection-Based Beamforming Method for Coherent Signals Receiving

Within a complex sea or ground surface background, multipath signals are strongly correlated or even completely coherent, which leads to signal cancellation when conventional optimal beamforming is performed. Aiming at the above problem, a coherent signal-receiving algorithm is proposed based on oblique projection technology in this paper. The direction of arrival (DOA) of incident signals is estimated firstly by the space smoothing-based MUSIC method. The composite steering vector of multipath coherent signals is then obtained utilizing the oblique projection matrix constructed with the estimated angles. The weight vector is thereby derived with the minimum variance distortionless response criteria. The proposed oblique projection-based beamformer can receive the multipath coherent signals effectively. Moreover, the proposed beamformer is more robust and converges to optimal beamformer rapidly without aperture loss. The theoretical analysis and simulation verify the validity and superiority of the proposed coherent signal beamformer.

Improvising limitations of DNN based ultrasound image reconstruction.

Ultrasound modalities are cost-effective and radiation-free technology for real-time medical imaging. These modalities require image reconstruction to obtain the actual ultrasound images from ultrasound raw data. The ultrasound raw data is obtained in the form of echo after scanning an imaging plane through ultrasound waves. The most commonly used image reconstruction beamforming technique is Delay and Sum (DAS). Other sophisticated beamforming techniques are Delay Multiply and Sum (DMAS) and Minimum Variance Distortionless Response (MVDR). DAS has limited image quality, and the employment of sophisticated techniques increases the computational complexity and computational time with improvement in image quality. To overcome these problems, various DNN (Deep Neural Networks) based techniques have been proposed which can reconstruct ultrasound images directly from ultrasound raw data. But DNN implementation has two limitations: accuracy of reconstruction and generalizability of the model. To overcome these limitations, we are proposing methodologies with a DNN model which was able to reduce these limitations. Firstly, we generated the datasets which include multiple shapes such as line, circle, ellipse, and parabola. After that, we have implemented a CNN-DNN (Convolution Neural Network and Deep Neural Network) hybrid model which has significantly improved computational time as well as image quality. We have trained our model with different sets of data to validate the reconstruction of the image matrix. We achieved a significant improvement in computational time of around 100 times (from around 0.6s to 0.0059s) as compared to DAS beamforming technique. At the same time, we also achieved a significant improvement in image quality with 37.19dB average and 41.37dB maximum improved Peak Signal to Noise Ratio (PSNR), and 87.41% average and 95% maximum Structural Similarity Index Matrix (SSIM) value. We also achieved generalizability and precise image reconstruction by using the proposed model.

Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation

Global navigation satellite system (GNSS) array antenna receivers are effective for suppressing wideband jamming. However, its anti-jamming performance decreases sharply when the number of wideband interference surpasses the number of array elements. Since a large number of jammers are often used in navigation countermeasures, it is crucial to keep array antenna receivers available in such conditions. Aiming at this issue, two main tasks were performed in this research and are presented in this paper: Firstly, the direction sensitivity of the sup-freedom anti-jamming performance is revealed and an anti-jamming method for array antenna receivers based on antenna rotation is proposed. Secondly, in order to determine the optimal rotation angle rapidly, a variable-step iteration algorithm based on gradient descent is proposed. Theoretical analysis and simulation show the effectiveness of the proposed anti-jamming method and the efficiency of the implementation algorithm. In a typical airborne scenario with a maximum azimuth difference of 90°, the anti-jamming ability of the proposed method improved by 21~26 dB and 5~10 dB for arrays adopting the PI (power inversion) and MVDR (minimum variance distortionless response) algorithms, respectively. The iterative efficiency improved by 78.35–99.63% in comparison with a traversal of 0.1° search resolution. The proposed method and algorithm are not limited to airborne scenarios and they might be influential to anti-jamming algorithms in the data domain.