Generalized Sidelobe Canceller Research Articles

Subcanceller: adaptive generalised sidelobe canceller with optimum subband decomposition

This paper presents a subband beamformer with the generalised sidelobe canceller (GSC) as the underlying structure. This new beamformer, referred to as the subcanceller, determines the statistically optimum filter bank succeeding the blocking matrix of the GSC based on the minimum mean-square error criterion. As a consequence, the signals passing through the lower branch of the GSC are decomposed into more appropriate subband components by such filter banks according to the input data statistics to enhance the interference suppression capability. The determination of the filter bank coefficients, however, calls for computationally demanding nonlinear optimisation. To alleviate the computational overhead, an iterative procedure is also proposed, which solves the Rayleigh quotient in each iteration. Furthermore, a detailed performance analysis of the proposed beamformer is conducted, which includes the development expression of the output signal to interference-plus-noise ratio and its implications, and the analysis of the convergence characteristic. Furnished simulations show that the new scheme yields superior interference suppression performance with a faster convergence rate compared with previous studies.

Adaptive Wideband Beamforming with Combined Spatial/Temporal Subband Decomposition

An adaptive wideband beamforming structure with a lower computational com- plexity and higher convergence speed is proposed, which is achieved by combined spatial/temporal subband decomposition. First, the received array signals are processed by a transformation ma- trix, and then split into subbands by a series of analysis fllter banks. At each subband, an independent beamformer can be operated, where some of the lower subbands can be discarded without afiecting its performance due to the highpass flltering efiect of the transformation matrix. 1. INTRODUCTION Beamforming has found many applications in various areas ranging from sonar and radar to wireless communications (1). It is a signal processing technique to form beams in order to receive signals illuminating a sensor array from speciflc directions, whilst attenuating signals from other directions. In a statistically optimum beamformer (2), since the statistics of the array data are often not known or may change over time, adaptive algorithms may be used to determine the beamformer's coe-cients. A beamformer structure for wideband array signals is shown in Fig. 1, where each of the M received signals is processed by an FIR fllter with a length of J and the outputs of these FIR fllters are then sumed up to form the flnal output. To perform adaptive wideband beamforming with high interference rejection and angular resolu- tion, arrays with a large number of sensors and fllter coe-cients have to be employed. Reducing the resultant high computational complexity and increasing its convergence speed has motivated many difierent solutions, including partially adaptive beamforming (3), transform-domain or frequency- domain beamforming (4,5), and subband beamforming (6,7). Recently, a generalised sidelobe can- celler (GSC) with combined subband decomposition in both the temporal and spatial domains was proposed in (8), where the columns of the blocking matrix are judiciously designed to generate a highpass flltering efiect to the array signals. The bandlimited spectra of the resultant outputs are then exploited by subband decomposition and appropriately discarding the low-pass subbands, where there is no signal existing. In this paper, we generalize the idea in (8) and propose a subband-selective transformation matrix, which is applied to the received array signals as a preprocessing step. Each of the outputs

Adaptive Antenna Beamforming Using Quiescent Pattern Control

Three adaptive beamforming algorithms are proposed that have a prescribed quiescent beam pattern and utilize the remaining degrees of freedom to reduce interference. The algorithms do not require explicit knowledge of the direction of arrival for the signal of interest. However, a correlated reference is required. These algorithms have well-behaved transient response and fast convergence making them suitable for time-varying channels. The algorithms have improved SINR over the generalized side-lobe canceller (GSC) when multiple interferers impinge upon the main beam of the quiescent pattern.

Performance comparison between statistical-based and direct data domain STAPs

In the situation that a radar platform is moving very fast, the number of training data used in space-time adaptive processing (STAP) is a major concern. Less number of training data is preferred in this situation. In this paper, four versions of statistical-based and direct data domain STAPs are discussed and compared their performance when the number of training data is varied. The four statistical-based methods are the full-rank statistical method, the relative importance of the eigenbeam (RIE) method, the principle component generalized sidelobe canceller (GSC) method, and the cross-spectral GSC method. We will compare the performance of these four methods with that of the direct data domain least squares (D3LS) approach, which utilizes only one snapshot of data in its processing. The channel mismatch will be also introduced to all methods to evaluate their performance. It is found that to make the statistical-based methods work; we need to know the rank of the interference covariance matrix. And the D3LS performs better when the number of training data available for the statistical-based methods is less than the rank of the interference covariance matrix.

Overview of generalized monopulse estimation

Monopulse is an established technique for radar angle estimation. One can show that monopulse estimation is based on a general approximation derived from maximum likelihood (ML) estimation. This tutorial provides a derivation of this relation and presents extensions of this monopulse principle to multi-dimensional array and parameter estimation problems, in particular to space-time adaptive processing (STAP) with reduced dimension, subarrays and generalized sidelobe canceller (GSLC) configurations. The performance of these monopulse applications can be predicted by exploiting the distribution of the monopulse ratio. It is demonstrated that this distribution is more realistic than the Cramer-Rao bound (CRB). Several examples of performance of monopulse estimators are given for thinned and fully filled planar arrays, adaptive beamforming with and without low sidelobes, GSLC, and STAP. Finally, conditions for estimates with low variance are discussed.

Performance analysis of reduced-rank beamformers for estimating dipole source signals using EEG/MEG

We study the performance of various beamformers for estimating a current dipole source at a known location using electroencephalography (EEG) and magnetoencephalography (MEG). We present our beamformers in the form of the generalized sidelobe canceler (GSC). Under this structure, the beamformer can be solved by finding a filter that achieves the minimum mean-squared error (MMSE) between the mainbeam response and filtered observed signal. We express the MMSE as a function of the filter's rank and use it as a criterion to evaluate the performance of the beamformers. We do not make any assumptions on the rank of the interference-plus-noise covariance matrix. Instead, we treat it as low-rank and derive a general expression for the MMSE. We present numerical examples to compare the MSE performance of beamformers commonly studied in the literature: principal components (PCs), cross-spectral metrics (CSMs), and eigencanceler (EIG) beamformers. Our results show that good estimates of the dipole source signals can be achieved using reduced-rank beamformers even for low signal-to-noise ratio (SNR) values.

Dual Channel Based Speech Enhancement Using Novelty Filter for Robust Speech Recognition in Automobile Environment

In this paper, a simple and effective dual channel speech enhancement algorithm is proposed. The proposed algorithm incorporates a novelty filter scheme, performing associative memory and aiming at improving the performance of the dual channel generalized sidelobe canceller (GSC.) The novelty filter is demonstrated as effective for enhancing the reference noise used in the dual channel GSC, such that the assumptions required for adaptive noise cancellation are satisfied. In this paper, the concept of a novelty filter scheme is described, in addition to the conditions required to guarantee, the performance of the dual channel GSC and the method of applying the novelty filter scheme to speech enhancement objectives. In representative experiments, superiority of the proposed algorithm is demonstrated using three objective measures; segmental signal to noise ratio (SNR), log spectral distance (LSD) and word accuracy, which are evaluated via speech data recorded in real automobile environments.

Blind adaptive DS/CDMA receivers for multipath and multiuser environments

This paper proposes several blind adaptive receivers to eliminate multiple-access interference (MAI), intersymbol interference (ISI), and interchip interference (ICI) in direct-sequence code-division multiple access (DS/CDMA) downlink multiuser systems. We use the following concepts to formulate the cost function: 1) the variance of the despreading output approaches to the variance of the desired signal and 2) the discreteness property of the input signal. The proposed approaches are called variance-oriented approaches (VOAs). The VOA is then applied to three proposed receiver structures, especially the generalized sidelobe canceller (GSC) scheme that is generally the concept of spatial domain in beamforming system, to eliminate the MAI by one particular constraint in temporal domain. Besides, by this constraint, GSC filter possesses the property of global convergence in multipath environment once the channel estimation is appropriate. Simulation examples are shown to demonstrate the effectiveness and comparison of the proposed blind adaptive receivers.

A Pilot Assisted Beamforming Scheme with Interference Resistant Angle of Arrival Estimation for OFDM Systems

In this paper, we propose an angle of arrival (AOA)-based beamforming structure with strong interference resistant capability for OFDM systems. First, we present novel interference resistant angle of arrival (AOA) estimation schemes for each multipath without training sequences or symbols as the pre-processing of the proposed structure. The generalized sidelobe canceler (GSC) structure is thus applied with the AOA information to avoid calculating the beamforming weight vector individually on a per subcarrier basis, wherein the GSC structure with the reduced-rank multistage Wiener filter (MSWF) is adopted. We also propose a signal transformation scheme to improve performance before the signals are fed into the GSC receiver. The proposed receiver offers better performance than the GSC form of the constrained Wiener filter-based receiver due to the faster convergence property of reduced rank processing and the signal transformation scheme.

A Near-Optimal Low-Complexity Transceiver for CP-Free Multi-Antenna OFDM Systems

Conventional orthogonal frequency division multiplexing (OFDM) system utilizes cyclic prefix (CP) to remove the channel-induced inter-symbol interference (ISI) at the cost of lower spectral efficiency. In this paper, a generalized sidelobe canceller (GSC) based equalizer for ISI suppression is proposed for uplink multi-antenna OFDM systems without CP. Based on the block representation of the CP-free OFDM system, there is a natural formulation of the ISI suppression problem under the GSC framework. By further exploiting the signal and ISI signature matrix structures, a computationally efficient partially adaptive (PA) implementation of the GSC-based equalizer is proposed for complexity reduction. The proposed scheme can be extended for the design of a pre-equalizer, which pre-suppresses the ISI and realizes CP-free downlink transmission to ease the computational burden of the mobile unit (MU). Simulation results show that the proposed GSC-based solutions yield equalization performances almost identical to that obtained by the conventional CP-based OFDM systems and are highly resistant to the increase in channel delay spread.

Beamformer performance with acoustic vector sensors in air

For some time, compact acoustic vector sensors (AVSs) capable of sensing particle velocity in three orthogonal directions have been used in underwater acoustic sensing applications. Potential advantages of using AVSs in air include substantial noise reduction with a very small aperture and few channels. For this study, a four-microphone array approximating a small (1 cm3) AVS in air was constructed using three gradient microphones and one omnidirectional microphone. This study evaluates the signal extraction performance of one nonadaptive and four adaptive beamforming algorithms. Test signals, consisting of two to five speech sources, were processed with each algorithm, and the signal extraction performance was quantified by calculating the signal-to-noise ratio (SNR) of the output. For a three-microphone array, robust and nonrobust versions of a frequency-domain minimum-variance (FMV) distortionless-response beamformer produced SNR improvements of 11 to 14 dB, and a generalized sidelobe canceller (GSC) produced improvements of 5.5 to 8.5 dB. In comparison, a two-microphone omnidirectional array with a spacing of 15 cm yielded slightly lower SNR improvements for similar multi-interferer speech signals.

Noise reduction method based on generalized subtractive beamformer

Combating noise signals is still a very important and challenging research topic. Previously, we presented a subtractive-beamformer-based noise reduction algorithm using paired microphones, which was shown to be effective in reducing directional noise. However, its basic assumption, namely a perfectly coherent noise field, is generally not satisfied in real-world environments. In this paper, we develop a general expression for the original algorithm we suggested earlier, on the basis of a generalized subtractive beamformer and by relaxing the strict assumption to that of an arbitrary noise field. Following ideas similar to those of the original algorithm, the generalized algorithm with a generalized sidelobe canceller (GSC)-like structure is derived. A theoretical analysis is then presented to show the linkage between this generalized algorithm and the original algorithm, and to show its noise reduction performance in theoretically defined noise fields. Finally, the superiority of the proposed noise reduction algorithm to other comparable algorithms was verified by experiments using multichannel recordings.

An Improved Array Steering Vector Estimation Method and Its Application in Speech Enhancement

We propose a robust microphone array for speech enhancement and noise suppression. To overcome target signal cancellation problem of conventional beamformes caused by array imperfections or reverberation effects, the proposed method adopts arbitrary transfer function relating each microphone and target speech signal as array channel model. This is achieved in two ways. First, we propose a method to estimate the array steering vector (ASV) by means of exploiting the nonstationarity of speech signal to combat stationary noise and interference. Next, with the estimated ASV, a robust matched-filter-(MF-) array-based generalized sidelobe canceller (MF-GSC) is constructed to enhance the speech signal and suppress noise/interference. In addition, it also has the capability to reduce the reverberation effects of the acoustic enclosure. Numerical results show that the proposed method demonstrates high performance even in adverse environments.

A robust adaptive generalized sidelobe canceller with decision feedback

Conventional generalized sidelobe canceller (GSC) is sensitive to a mismatch between the estimated and actual direction of arrival (DOA) of the desired signal. Such a mismatch induces signal cancellation in the GSC, and it severely degrades the beamforming performance. In this paper, we propose a new decision feedback (DF) technique to increase the robustness against the DOA mismatch. Our new scheme introduces a blind equalizer and a feedback filter in the GSC structure. We first derive Wiener solutions for the DF-GSC with perfectly matched and mismatched DOA and show that the problem of signal cancellation can be avoided. Then, we consider the adaptive GSC implementation in which the least-mean-square (LMS) algorithm is used for weight adaptation. In addition to the improved robustness, the proposed scheme also remedies the slow convergence problem inherent in the conventional adaptive GSC structure. The convergence behavior of the LMS-based DF-GSC is fully analyzed and the analytic signal-to-interference-plus-noise ratio (SINR) is also derived. Finally, simulation results demonstrate that while the proposed structure can considerably enhance the overall performance, it has greatly improved robustness as compared to other existing robust adaptive beamformers.

Two-dimensional adaptive array beamforming with multiple beam constraints using a generalized sidelobe canceller

This paper deals with the problem of two-dimensional (2-D) adaptive array beamforming with multiple beam constraints (MBC) using a generalized sidelobe canceller (GSC). We present a method for the construction of signal blocking matrix required by the 2-D GSC. The resulting 2-D adaptive beamformer can provide almost the same performance as conventional 2-D adaptive beamformers based on a linearly constrained minimum variance (LCMV) criterion. The effectiveness of the proposed GSC is that the construction of the required signal blocking matrix requires only the computation of a few entries from analytical formulas. In comparison with conventional methods, the proposed technique gets rid of the computational complexity due to the eigendecomposition required for finding the 2-D signal blocking matrix. For dealing with the performance degradation due to coherent interference, we present a 2-D weighted spatial smoothing scheme to effectively alleviate the coherent jamming effect. Several simulation examples are provided for illustration and comparison.

Robustness analysis of multichannel Wiener filtering and generalized sidelobe cancellation for multimicrophone noise reduction in hearing aid applications

For small-sized arrays such as hearing aids, noise reduction is obtained at the expense of an increased sensitivity to errors in the assumed signal model, i.e., microphone mismatch, variations in speaker and microphone positions, reverberation. However, the noise reduction algorithm should still be robust, i.e., insensitive to small signal model errors. In this paper, we evaluate the robustness of the Generalized Sidelobe Canceller (GSC) and a recently developed Multichannel Wiener Filtering (MWF) technique for hearing aid applications both analytically and experimentally. The analysis reveals that robustness of the GSC is especially crucial in complicated noise scenarios and that microphone mismatch is particularly harmful to the GSC, even when the adaptive noise canceller is adapted during noise only. Hence, a constraint on the noise sensitivity of the GSC is essential, at the expense of less noise reduction. The MWF on the other hand, is not affected by microphone mismatch and has a potential benefit over the robust GSC with noise sensitivity constraint. However, the MWF is sensitive to the estimation accuracy of the second order statistics of speech and noise so that its benefit may be lost in nonstationary noise scenarios.

A generalized sidelobe canceller employing two-dimensional frequency Invariant filters

Based on the generalized sidelobe canceller (GSC), we propose a novel adaptive broadband beamformer, where the quiescent vector and blocking matrix are replaced by a series of two-dimensional frequency invariant filters (FIFs). As opposed to standard beamspace techniques, the number of FIFs is flexible and they are not required to possess a very low sidelobe level. Compared with a standard GSC, a faster convergence speed and a lower computational complexity of its adaptive part are achieved.

Adaptive Nonlinear Regression Using Multiple Distributed Microphones for In-Car Speech Recognition

In this paper, we address issues in improving hands-free speech recognition performance in different car environments using multiple spatially distributed microphones. In the previous work, we proposed the multiple linear regression of the log spectra (MRLS) for estimating the log spectra of speech at a close-talking microphone. In this paper, the concept is extended to nonlinear regressions. Regressions in the cepstrum domain are also investigated. An effective algorithm is developed to adapt the regression weights automatically to different noise environments. Compared to the nearest distant microphone and adaptive beamformer (Generalized Sidelobe Canceller), the proposed adaptive nonlinear regression approach shows an advantage in the average relative word error rate (WER) reductions of 58.5% and 10.3%, respectively, for isolated word recognition under 15 real car environments.

Adaptive Microphone Array System with Two-Stage Adaptation Mode Controller

In this paper, an adaptive microphone array system with a two-stage adaptation mode controller (AMC) is proposed for high-quality speech acquisition in real environments. The proposed system includes an adaptive array algorithm, a time-delay estimator and a newly proposed AMC. To ensure proper adaptation of the adaptive array algorithm, the proposed AMC uses not only temporal information, but also spatial information. The proposed AMC is constructed with two processing stages: an initialization stage and a running stage. In the initialization stage, a sound source localization technique is adopted, and a signal correlation characteristic is used in the running stage. For the adaptive array algorithm, a generalized sidelobe canceller with an adaptive blocking matrix is used. The proposed algorithm is implemented as a real-time man-machine interface module of a home-agent robot. Simulation results show 13 dB SINR improvement with the speaker sitting 2 m distance from the home-agent robot. The speech recognition rate is also enhanced by 32% when compared to the single channel acquisition system.

Adaptive log-spectral regression for in-car speech recognition using multiple distributed microphones

This letter addresses issues in improving hands-free speech recognition performance in different car environments. We propose a new speech-enhancement approach based on optimizing regression of the log-spectra, which is used to estimate the log-spectra of speech at a close-talking microphone by using multiple spatially distributed microphones. The regression weights can be adapted automatically for different noise environments. Compared to the nearest distant microphone and adaptive beamformer generalized sidelobe canceller (GSC), the proposed approach shows an advantage in the average relative word error rate (WER) reduction of 58.5 and 10.3%, respectively, for isolated word recognition under 15 real-car environments.