Generalized Sidelobe Canceller Research Articles

Comparative Analysis of Generalized Sidelobe Cancellation and Multi-Channel Linear Prediction for Speech Dereverberation and Noise Reduction

For blind speech dereverberation, two frameworks are commonly used: on the one hand, the multi-channel linear prediction (MCLP) framework, and on the other hand, data-dependent beamforming, e.g., the generalized sidelobe canceler (GSC) framework. The MCLP framework is designed to perform deconvolution and hence has gained increased prominence in blind speech dereverberation. The GSC framework is commonly used for noise reduction, but may be applied for dereverberation as well. In previous work, we have shown that for the noiseless case, MCLP and the GSC yield in theory mathematically equivalent results in terms of dereverberation. In this paper, we assume additional coherent as well as incoherent-noise components and formally analyze and compare both frameworks in terms of dereverberation and noise reduction performance. Both the theoretical analysis and time domain simulation results demonstrate that unlike the GSC, MCLP expectably shows limited performance in terms of noise reduction, while both perform equally well in terms of dereverberation, provided that the GSC blocking matrix achieves complete blocking of the early reverberant-speech component and sufficiently many microphones are available. In case of incomplete blocking, however, the GSC performs inferior to MCLP in terms of dereverberation, as shown in short-time Fourier transform domain simulations.

Space-Time-Polarization Adaptive Antenna Arrays for GNSS Receivers

This paper discusses about space-time-polarization adaptive antenna arrays (STPA) for global navigation satellite system (GNSS) receivers. The conventional right-handed circularly polarized (RHCP) antenna is in-situ replaced by the proposed dual-polarized antenna, which combines space, time, and polarization for high anti-interference performance. By signal modelling, we suggest an efficient generalized sidelobe canceller (GSC) structure of STPA to equivalent the conventional linearly constrained minimum variance (LCMV) beamformer. We demonstrate by simulations that 4-element dual-polarized STPA can suppress 7 broadband interferences with different direction and polarization. However, if RHCP characteristic of GNSS signal is constrained, 7 broadband RHCP interferences with the same polarization characteristic cannot be suppressed effectively. The performance of this scenario is related to the normalized bandwidth of interferences. At last, we demonstrate that 4-element STPA and 7-element conventional spatial adaptive array have comparable anti-interference performances.

Hearing Aid Speech Enhancement Using Phase Difference-Controlled Dual-Microphone Generalized Sidelobe Canceller

This paper proposes a new technique for improving a generalized sidelobe canceller (GSC) for dual-microphone speech enhancement to be applied in an auditory device such as a hearing aid. Here, the GSC is implemented on a 32-channel uniform polyphase discrete Fourier transform filter bank, where the overall algorithm processing delay is 8 ms to meet hearing aid requirements. The proposed method can improve the fixed beamformer (FBF) and control the adaptive algorithm in the noise canceller (NC) using the phase difference obtained from dual-microphone signals. For this, spatial cues such as the phase differences are used to estimate the target-to-non-target directional signal ratio (TNR). A target-directional speech enhancing spectral gain-attenuator is calculated based on the estimated TNR, which is then incorporated to improve the FBF in the GSC. Furthermore, the weight update of the adaptive NC in the GSC is formulated using the phase difference-based TNR. The experimental results show that the auditory speech enhancement system that employs the proposed dual-microphone GSC algorithm provides better perceptual quality and intelligibility scores than conventional methods such as a beamformer, phase-error-based filter (PEF), GSC, or PEF-controlled GSC under multiple noise conditions of signal-to-noise ratio range 0-20 dB.

Throughput Enhancement for Dual-Function Radar-Embedded Communications Using Two Generalized Sidelobe Cancellers

In this paper, we develop a novel approach for a joint platform of radar and communication systems. The previous approaches for dual-function radar-communication systems focused only on the simultaneous transmission of radar and communications in radar active mode. To increase the throughput of communications without affecting the radar operation, we enable communication during the whole pulse repetition interval by using two generalized sidelobe cancellers. We propose two different operational modes of the dual-function radar-communications system. In active mode, the radar function is achieved through the mainlobe, and communications are achieved through the sidelobes. In active mode, only one generalized sidelobe canceller is functional. In rest mode, both the generalized sidelobe cancellers are functional. The first generalized sidelobe canceller has the mainlobe and sidelobe levels as in active mode, and the second generalized sidelobe canceller has the same mainlobe level but double the power in the sidelobes. The output is the difference between the two, in which the mainlobe is canceled, while the sidelobes have the same power, as in the case of active mode. The effectiveness of the proposed scheme is investigated in terms of the bit error rate. Moreover, the proposed system allows communication during the whole pulse repetition interval, thus, enhancing the throughput tremendously.

Generalized Sidelobe Canceller for Ultrasound Imaging based on Eigenvalue Decomposition

The improved generalized sidelobe canceller (GSC) based on eigenvalue decomposition beamforming technique for ultrasound imaging is proposed. Firstly, the signal subspace is obtained by performing eigenvalue decomposition on the covariance matrix of received data. Secondly, the weighting vector of GSC is divided into adaptive and non-adaptive two parts. Then the non-adaptive part is projected into the signal subspace to obtain a new steer vector. Subsequently, based on the orthogonal complementary space of the new steer vector, the blocking matrix is constructed. Finally, the weighting vector is updated by projecting the final weighting vector into the signal subspace. In order to verify the proposed algorithm, the simulations of the point targets and the cyst phantom were conducted in Field II. The experimental results indicate that the proposed method has better resolution and contrast ratio than the conventional algorithms. In addition, the algorithm is robust to noises. Furthermore, combining with coherence factor, the contrast ratio of the proposed algorithm can be further improved in comparison with a conventional GSC with coherence factor.

Beamforming Designs Robust to Propagation Model Estimation Errors for Binaural Hearing Aids

This work introduces new binaural beamforming algorithms for hearing aids, with a good robustness to errors in the estimated target source propagation model. Two different binaural beamforming designs are proposed. One design is based on an adaptive null positioning scheme, and the second is based on a combination of the adaptive null positioning scheme and wider beampatterns. Simulations are performed using signals and propagation models obtained from multichannel binaural hearing aids recordings, including some in a mildly reverberant environment. Evaluations are done in terms of noise reduction and target distortion. Evaluation results illustrate the robustness of the two proposed designs to errors between the true and estimated directions of arrival for the target source, and to mismatch between the anechoic propagation models used for the beamformers designs and the reverberant propagation models used to generate the signals at the sensors or beamformer inputs. Both designs surpass the performance of standard binaural Minimum Variance Distortionless Response (MVDR) and binaural Generalized-Side Lobe Canceler (GSC) beamformers.

Reduced‐dimension STAP using a modified generalised sidelobe canceller for collocated MIMO radars

This study addresses the problem of beam-space post-Doppler reduced-dimension (RD) space-time adaptive processing (STAP) using a modified generalised sidelobe canceller (GSC) in an airborne collocated multiple-input multiple-output (MIMO) radar. Steering vectors corresponding to the nulls of beamforming are used to construct the blocking matrix (BM) of the GSC processor with optimal signal to clutter plus noise ratio. However, it is difficult to determine the beampattern nulls for a collocated MIMO radar which is equivalent to a virtual array with coherent amplitude weights. To build a valid BM of the GSC-based STAP processor, the authors propose a BM modification method based on the modified Gram-Schmidt orthogonalisation algorithm. The authors prove that the modified RD-GSC processor can achieve the performance equal to that of the fully optimal processor conditionally. The authors also show that the proposed method has reduced computational complexity and fast convergence rate. Numerical simulations are presented to demonstrate the effectiveness of the proposed methods.

Low-Complexity Reduced-Dimension Space–Time Adaptive Processing for Navigation Receivers

In this paper, we propose a low-complexity adaptive reduced-rank interference suppression scheme based on alternating low-rank decomposition techniques for navigation receivers. The proposed scheme, which employs a generalized sidelobe canceler structure, makes use of a projection matrix for rank reduction followed by a reduced-dimension receive filter. An alternating optimization strategy based on recursive least squares is devised to compute the basis vectors of the projection matrix and the receive filter adaptively. Simulation results show that the proposed algorithm achieves a better performance than existing methods with a reduced computational complexity.

Reduced-dimension space-time adaptive processing with sparse constraints on beam-Doppler selection

Abstract State-of-the-art space-time adaptive processing (STAP) algorithms devised in the beam-Doppler domain are confined by fixing the beam-Doppler cells used for adaptation, which may suffer from performance degradation. To overcome this drawback, a novel STAP algorithm in the beam-Doppler domain is proposed. The proposed algorithm adopts a generalized sidelobe canceller structure, and the filter design is formulated as a sparse representation problem by imposing a sparse constraint on the weight vector. As the sparse constraint enforces most of the elements in the weight vector to be zero (or sufficiently small in amplitude), the proposed algorithm can adaptively select the best beam-Doppler cells for adaptation, and thus it falls under the category of reduced-dimension approach. Simulation results illustrate that the proposed algorithm outperforms the existing STAP approaches with fixed beam-Doppler localized processing.

Robust LCMV Beamformer for Direction of Arrival Mismatch Without Beam Broadening

In this work, we propose a new and efficient algorithm to mitigate the signal look direction error problem in adaptive beamforming without broadening the main beam. The algorithm exploits a reference lobe in the region of signal look direction error. This reference lobe is the main beam of the generalized sidelobe canceller when there is no direction of arrival mismatch for the desired signal. The desired pattern is forced to follow the reference beam in the region of signal look direction error by utilizing multiple additional constraints. Simulations are performed in MATLAB to check and test the validity of the proposed algorithm on the basis of different scenarios.

Eigenspace generalized sidelobe canceller combined with SNR dependent coherence factor for plane wave imaging

BackgroundThe eigenspace generalized sidelobe canceller (EGSC) beamformer combined with a signal-to-noise ratio (SNR) dependent coherence factor (CF) is suggested for coherent plane wave compounding (PW) imaging. Conventional CF based methods such as generalized CF and subarray CF can improve the image quality, however, they are not suitable for low SNR. On the other hand, the EGSC CF based approach can introduce improvements in image quality, however, in PW imaging is susceptible to suffer from degradation due to low SNR which leads to a poor image quality. To overcome this limitation, the SNR dependent CF method is suggested for application in such situations due to its ability to control the SNR levels.MethodsThe Field II and the Verasonics ultrasound imaging system with a L11-4v array transducer with a contrast resolution phantom were used to capture the plane wave sequences of simulation and experimental data, respectively. The performance evaluation using full width at half maximum (FWHM), contrast (CR and CNR) and the speckle statistics by using the signal to noise ratio (SNR) complemented by the Rayleigh distribution analysis was performed. In order to evaluate the performance of the text {EGSC}_{3} (the SNR CF) beamformer, the comparison is done with particular importance to other CF-based approaches such as text {EGSC}_{1} (the generalized CF) and, text {EGSC}_{2} (the subarray CF) respectively.ResultsTaking DAS as reference, text {EGSC}_3 showed 30.3 and 39.5% of improvement for text {CR(dB)} and text {CNR}, respectively, when using experimental data. The proposed method also slightly outperforms the text {EGSC}_1 and text {EGSC}_2 methods for text {CR(dB)}, text {CNR}, and speckle statistics assessment.ConclusionThe text {EGSC}_3 is, therefore, suitable for CPWC by improving the spatial resolution and contrast while preserving the speckle pattern.

An alternative to diagonal loading for implementation of a white noise array gain constrained robust beamformer

Diagonal loading is one of the most popular methods of robust adaptive beamforming, and the solution to many different problems aimed at producing beamformers which are robust to finite samples effects or/and steering vector errors. Among the latter, constraining the white noise array gain (WNAG) is a meaningful approach. However, relating the loading level to the desired WNAG is not straightforward. In this communication, using a generalized sidelobe canceler structure of the beamformer, we prove that the WNAG constraint can be encoded directly in the beamformer, and the latter can be obtained in a rather simple way from a specific eigenvector and without going through the diagonal loading step.

A robust reduced-rank monopulse algorithm based on variable-loaded MWF with spatial blocking broadening and automatic rank selection

For antenna array with large number of elements, the multistage Wiener filter (MWF) is an efficient method to perform reduced-rank beamforming. Similar to the generalized sidelobe canceller (GSC), MWF is also sensitive to direction of arrival (DOA) mismatch of the signal of interest (SOI). This condition limits the application of MWF to monopulse beamforming, in which the actual DOA of SOI may mismatch the assumed direction (i.e. the pattern look direction) due to target movement and platform disturbance. In this paper, we develop a variable-loaded multistage Wiener filter with spatial blocking broadening and automatic rank selection (VLSRS-MWF) to perform robust reduced-rank monopulse beamforming. Firstly, a null-broadened spatial blocking matrix which can be used for both the sum and difference beams is developed instead of the derivative constraints and the eigenvector constraints based blocking matrices. Secondly, a l2-norm regularization method is incorporated with MWF to further improve the output signal to noise ratio and to restrict the pattern sidelobe level. Then a Ritz value criterion with deterministic threshold for selecting the rank of the reduced-rank subspace is developed utilizing the eigenvalue distribution of sample covariance matrix and the Ritz value criterion. Simulation results show that the proposed VLSRS-MWF based monopulse algorithm can maintain good robustness and high angle estimation accuracy in presence of DOA mismatch.

Singular Value Decomposition-Based Generalized Side Lobe Canceller Beamforming Method for Ultrasound Imaging

In the previous studies, eigenspace-based minimum variance (ESBMV) algorithms were proposed, however, the quality of the algorithm will degrade in low signal to noise occasions. In this study, a singular value decomposition generalized side lobe canceller (SVD-GSC) beamforming method based on the GSC is proposed. The sample covariance matrix is eigendecomposed, and a kind of further SVD is introduced to establish the noise space and the signal space, respectively. After that, the weighting vectors acquired by GSC are projected into the left singular space of the desired signal space. The performance of the proposed method is investigated by both of the simulation and experimental data. And the sound velocity error is also investigated in this paper. The imaging quality of point targets are measured by the [Formula: see text][Formula: see text]dB main lobe width and the peak side lobe (PSL). The contrast ratio (CR) is introduced to describe the quality of cyst phantom. Both the point targets and cyst phantom simulation show that the proposed SVD-GSC performs better in terms of spatial resolution, PSL and CR. Furthermore, the proposed method has a stronger robustness than the traditional GSC.

Binaural Speaker Localization Integrated Into an Adaptive Beamformer for Hearing Aids

In this paper, we present and compare novel algorithms to localize simultaneous speakers using four microphones distributed on a pair of binaural hearing aids. The framework consists of two groups of localization algorithms, namely, beamforming-based and statistical model based localization algorithms. We first generalize our previously proposed methods based on beamforming techniques to the binaural configuration with 2 $\times$ 2 microphones. Next, we contribute two statistical model based methods for binaural localization using the maximum likelihood approach that also takes head-related transfer functions and unknown noise conditions into account. The methods enable the localization of multiple source positions for all azimuth angles and do not require prior training of binaural cues. The proposed localization algorithms are integrated into a generalized side-lobe canceller (GSC) to extract the desired speaker in the presence of competing speakers and background noise and when the head of the listener turns. The GSC components are adapted with the frequency-wise target presence probability and the frame-wise broadband direction-of-arrival (DOA) estimates that track the turns of the listener's head. We evaluate the performance of the localization algorithms individually and also in the context of the adaptive binaural beamformer in various noisy and reverberant conditions. Finally, we introduce a new adaptive beamformer, which combines the GSC with multichannel speech presence probability estimation and achieves superior source separation performance in noisy environment.

Out-of-Band Radiation Reduction in Cognitive Radio OFDM Systems Hybridizing Firefly Algorithm with Generalized Sidelobe Canceller

Orthogonal frequency division multiplexing is the most popular method for dynamic spectrum access because of its ability in changing the spectrum shapes. However, it produces a substantial out-of-band (OOB) radiation due to the high sidelobes of the modulated subcarriers which results in considerable interference to the neighboring users. This challenge becomes even more critical in cognitive radio networks, which gives the secondary system the right to use the available spectrum holes. In this paper, a joint technique for the reduction of OOB radiation is developed by adding the advantages of the cancellation carriers, based on firefly algorithm and of the generalized sidelobe canceller. Simulation results show that by using the proposed technique substantial reduction in OOB radiation is achieved.

Knowledge-Based Generalized Side-Lobe Canceller for Ionospheric Clutter Suppression in HFSWR

High frequency surface wave radar (HFSWR) has been successfully developed for early warning and remote sensing. However, the ionospheric clutter is a difficult challenge that can make HFSWR system inefficient. The Generalized Side-lobe Canceller (GSC) has been proved to be an effective algorithm for clutter suppression in theory, but it suffers from the performance degradation for some non-ideal conditions in practice. The most intolerable shortcoming is the signal to noise ratio (SNR) loss caused by the residual signal in the secondary data. In this paper, a knowledge-based GSC (KB-GSC) method has been proposed via an adaptive single notch filter design to reject the residual signal for reducing the SNR loss. The feasibility and availability has been demonstrated by measured data.

A Division-Free and Variable-Regularized LMS-Based Generalized Sidelobe Canceller for Adaptive Beamforming and Its Efficient Hardware Realization

This paper proposes a new division-free generalized sidelobe canceller-based adaptive beamformer and its efficient hardware realization. A discrete cosine transform-based blocking matrix is proposed for uniform linear array to decorrelate the input so as to achieve a faster convergence speed. A new variable step-size least mean squares algorithm for complex input is proposed to further improve the convergence and the steady-state performance of the adaptive beamformer. Moreover, a variable regularization scheme is incorporated to mitigate possible signal cancellation due to possible mismatches in steering vector. Furthermore, a statistical analysis on the mean and mean squares convergence of the algorithm is performed and validated using Monte Carlo simulations. An efficient architecture of the proposed adaptive beamformer is also proposed for its real-time implementation. It employs a novel division-free approach by quantizing the normalization factor into a limited number of levels so that the division can be implemented using canonical signed digits, resulting in multiplier-less realization. The performance of the resultant division-free implementation can achieve similar convergence and steady-state performance as a conventional divider approach while achieving at least 21% less hardware resources and 26.85% higher operating speed in Xilinx Virtex7 (XC7VX330T) field programming gate array for an eight-sensor uniform linear array. Finally, the beam can be stabilized remarkably in only 1 μs at a system clock frequency of 124 MHz.

A Robust Approach to Generalized Sidelobe Canceler for Medical Ultrasound Imaging

A Robust Approach to Generalized Sidelobe Canceler for Medical Ultrasound Imaging

Adaptive interference nulling with pattern maintaining under mainlobe subspace and quadratic constraints

A fast adaptive interference nulling algorithm with arbitrary pattern maintaining without direction of arrival is proposed under the framework of generalised sidelobe canceller (GSC). The problem is formulated to optimise the weight by minimising the total output power of the beamformer, while keeping the antenna gain accurately approximated to that of the quiescent pattern by the mainlobe subspace constraint (MSC) and the weight quadratic constraint. The MSC is constructed by the dominant eigenvectors of the mainlobe covariance matrix. The closed-form solution to this problem is obtained by two steps. First, the quadratic constraint is relaxed to obtain the weight in the form of a GSC beamformer with diagonal loading (DL) sample covariance matrix. Then, the stringent quadratic constraint is guaranteed by optimising the DL level, which is precisely estimated by a simple iterative equation. The maintenance of pattern mainlobe and the reduction of the computation complexity can be achieved simultaneously. Numerical experiments are performed to demonstrate the effectiveness and efficiency of the proposed algorithm, and discuss the influence of mutual coupling between antenna elements.