Suppressing radio frequency interferences with adaptive beamformer based on weight iterative algorithm

In this paper, we study the performance of the minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV) noise reduction filters when a source of interference and ambient noise coexist with the target signal. We demonstrate that both filters are related as we decompose the MVDR filter into the LCMV and a matched filter (MVDR solution in the absence of interference). Both components are properly weighted to achieve maximum interference-plus-noise reduction at each frequency bin. Furthermore, we elaborate new closed-form expressions for the signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) at the output of the LCMV, MVDR, and matched filters. These expressions theoretically prove that a tradeoff between noise reduction and interference rejection has to be made. In fact, the total removal of the interference may severely amplify the output ambient noise. Conversely, totally focussing on noise reduction leads to increased level of residual interference. The proposed study is finally supported by numerical examples.

Speech enhancement is an important pre-processing step in a wide diversity of practical fields related to speech signals, and many signal-enhancement methods, such as fixed beamforming and adaptive beamforming, have already been proposed for speech enhancement. However, lack of comprehensive and quantitative performance evaluation of current enhancement methods when multi-speeches exist simultaneously, it is difficult to choose an appropriate enhancement method in a practical application. This work aims to study the implementation of several enhancement methods for multi-speech enhancement in indoor environment of T60 = 0 s and T60 = 0.3 s. We first propose and compare two types of enhancement approaches. The first type is the basic enhancement methods, including the delay-and-sum beamforming (DSB), minimum variance distortionless response (MVDR), linearly constrained minimum variance (LCMV), and independent component analysis (ICA); The second type is the improved enhancement methods, including the robust MVDR and LCMV, realized by eigen decomposition and diagonal loading. In addition, the online enhancement performance based on the iteration of single-frame speech signals is then researched. The experimental results show that the enhancement effect of LCMV and ICA is relatively more stable in the case of the basic enhancement methods; in the case of the improved enhancement algorithms, the methods which employed diagonal loading iterations are better. In the case of the online enhancement, the DSB with frequency masking yields the best performance on the signal-to-interferences ratio (SIR) and possesses the capacity to suppress interferences.

We propose a new joint multiple matrix diagonalization algorithm for robust blind beamforming. This new algorithm is based on the iterative eigen- decomposition of cumulant matrices. Therefore it can avoid the stability and misadjustment problems arising among the conventional steepest-descent approaches for constant- modulus or cumulant optimization. Our Monte Carlo simulations show that our proposed algorithm significantly outperforms the JADE algorithm based on the Givens rotation for PSK source signals in terms of signal-to- interference-and-noise ratio for a wide variety of signal-to- noise ratios. Keywords-blind beamforming; eigen decomposition;JADE algorithm; cumulant; constant modulus I. INTRODUCTION The traditional beamforming techniques such as the minimum variance distortionless response (MVDR) approach (1) and the linear constrained minimum variance (LCMV) method (1) are based on the a priori knowledge of the directional vector associated with the desired source signal and may be quite sensitive to the perturbation of this vector. The perturbation of the vector direction is caused by the unknown deformation of the antenna array, the drifting effect in the electronics or the multipath propagation. Consequently, those factors strictly limit the performance of MVDR and LCMV beamformers in practice.

Most existing methods on beamformer design, such as the minimum variance distortionless response (MVDR) method and the linearly constrained minimum variance (LCMV) method, assume that the group delay is known and often heuristically preselected to be half of the filter length. However, the optimal group delay is problem-dependent in practice, and the beamformer design performance based on the optimal beamformer response can be significantly better than the one based on the preselected beamformer response in the reverberant environment. In this paper, we consider the optimal beamformer design problem, i.e., the joint beamformer response and beamformer design problem. We introduce a new variable to represent the beamformer response and formulate the joint beamformer response design and beamformer design problem as a structured constrained convex optimization (SCCO) problem. Moreover, we exploit the separable structures of the SCCO problem and propose to use the alternating direction method of multipliers (ADMM) to solve it, and give a simple proof for the analysis of convergence. Numerical simulation results show the effectiveness of the proposed method in the reverberant environment by comparing it with the LCMV method.

In real-world environments, the signals captured by a set of microphones in a speech communication system are mixtures of the desired signal, interference, and ambient noise. A promising solution for proper speech acquisition (with reduced noise and interference) in this context consists in using the linearly constrained minimum variance (LCMV) beamformer to reject the interference, reduce the overall mixture energy, and preserve the target signal. The minimum variance distortionless response beamformer (MVDR) is also commonly known to reduce the interference-plus-noise energy without distorting the desired signal. In either case, it is of paramount importance to accurately quantify the achieved noise and interference reduction. Indeed, it is quite reasonable to ask, for instance, about the price that has to be paid in order to achieve total removal of the interference without distorting the target signal when using the LCMV. Besides, it is fundamental to understand the effect of the MVDR on both noise and interference. In this correspondence, we investigate the performance of the MVDR and LCMV beamformers when the interference and ambient noise coexist with the target source. We demonstrate a new relationship between both filters in which the MVDR is decomposed into the LCMV and a matched filter (MVDR solution in the absence of interference). Both components are properly weighted to achieve maximum interference-plus-noise reduction. We investigate the performance of the MVDR, LCMV, and matched filters and elaborate new closed-form expressions for their output signal-to-interference ratio (SIR) and output signal-to-noise ratio (SNR). We theoretically demonstrate the tradeoff that has to be made between noise reduction and interference rejection. In fact, the total removal of the interference may severely amplify the residual ambient noise. Conversely, totally focussing on noise reduction leads to increased level of residual interference. The proposed study is finally supported by several numerical examples.

In this paper, the direction of arrival (DOA) and adaptive antenna array beamforming for the smart antenna applications are realized by using Minimum Variance Distortionless Response (MVDR) algorithm of DOA estimation. The MVDR supported by joint approximate diagonalization of eigenmatrices (JADE) algorithm is used to estimate the direction of arrivals of coherent signals in non-coherent groups. The generalized steering vectors using JADE algorithm are first estimated, and then the spectrum methods are realized to estimate the DOA of each signal. Root mean square error (RMSE) values are computed in different scenarios for the proposed method as well as JADE-MUSIC and the corresponding results are compared. Although both methods resolved the signals correctly, simulation results show that JADE based MVDR has better performances in the cases of low number of antenna elements and snapshots. The DOAs obtained are then processed using LMS adaptive beamforming algorithm to steer the main lobe of the radiation pattern to desired angles (signals) and the nulls to undesired angles (signals). The simulation results also reveal that adaptive beamforming is successfully done by annihilating the effects of undesired signals significantly.

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.

For multi-microphone speech enhancement, different microphones might have different contributions, assome are even marginal. This is more likely to happen in wireless acoustic sensor networks (WASNs), where somesensors might be distant. In this work, we therefore consider sensor selection for linearly-constrained beamformers. Theproposed sensor selection approach is formulated by minimizing the total output noise power and constraining thenumber of selected sensors. As the considered sensor selection problem requires the relative acoustic transfer function(RTF), the covariance whitening based RTF estimation or a direct-path RTF approximation is exploited. For a singletarget source, we can thus substitute the estimated RTF or the assumed RTF to the original problem formulation in orderto design a minimum variance distortionless response (MVDR) beamformer. Alternatively, we can integrate the two RTFsto design a linearly constrained minimum variance (LCMV) beamformer in order to alleviate the effects of RTFestimation/approximation errors. By leveraging the superiority of LCMV beamformers, the proposed approach can beapplied to the multi-source case. An evaluation using a simulated large-scale WASN demonstrates that the integration ofRTFs for the sensor selection based LCMV beamformer can be beneficial as opposed to relying on either of theindividual RTF steered sensor selection based MVDR beamformers. We conclude that the sensors that are close to thetarget source(s) and also some around the coherent interferers are more informative.

60GHz wireless communication technology is considered to be one of the most promising technology in future wireless communication domains. The information of the DOA (direction of arrival) is the foundation for adaptive array antenna to adjust the optimal beam direction as well as track mobile terminal, and the method to obtain the precise DOA is the key technology of the adaptive beam forming. The STMV (Steered Minimum Variance) algorithm is a kind of high resolution wideband adaptive beam forming method, which takes advantages of the STCM (Steered Covariance Matrix) to estimate the adaptive beam forming weight vector so as to obtain DOA information. In this paper, we attempt to bring STMV algorithm into DOA estimation for 60GHz signals. It is proved practicable to apply STMV algorithm in 60GHz DOA estimation via MTALAB simulation. Comparing with the MVDR (Minimum Variance Distortionless Response) algorithm, the STMV algorithm shows excellent performance that it needs less fast beats in the required resolution, also has smaller variance.

In contrast with the common delay-and-sum (DAS) beamformer, adaptive beamformers can be used to increase the resolution of acoustic measurement systems without sacrificing sidelobe levels. The non-linear nature of adaptive beamforming methods may cause changes to measured scattered amplitude distributions. For example, large differences in speckle statistics have been observed in some cases when using the adaptive minimum variance distortionless response (MVDR) beamformer in medical ultrasound, unless preventative measures were implemented. Low complexity adaptive (LCA) beamforming techniques have shown similar, but smaller, differences. Recently, experiments have been conducted in the Oslofjord, Norway, to determine whether low-complexity adaptive (LCA) beamforming techniques can improve the performance of bathymetric echosounders. When using LCA with these types of sonar systems, it is important to understand any changes to the backscatter statistics, as these changes may affect, e.g., seafloor classification methods. In this talk we will present comparisons between backscatter statistics estimated using a weighted delay-and-sum beamformer and those estimated using a low complexity adaptive beamformer. We will discuss any differences seen, possible mechanisms causing these differences and potential remedies. [Research supported by The Norwegian Research Council and Kongsberg Maritime. The first author is employed in Kongsberg Maritime.]

In order to improve the robustness of microphone array beamformer in existing mismatch errors, robust beamforming algorithm based on linear constrained minimum variance and diagonal loading method is proposed. The proposed algorithm incorporates the spatial response variation function into the linear constrainted minimum variance criterion to design the frequency invariant beamformer and improve the robustness against microphone array mismatch errors in the actual environment by diagonal loading method. Simulation results and analyses illustrate that the array response within passband is robust, the stopband amplitude is controlled in the appropriate range, and the proposed algorithm based closed solution is better than the proposed algorithm based on convex optimization algorithm. Introduction As one of the key technologies for microphone arrays, broadband beamforming has been used in a wide range of audio and speech processing applications[1]. In many popular methods, the traditional linear constrained minimum variance(LCMV) beamforming method is mainly used to design narrow-band antenna beamformer[2]. If it is used to design a broadband beamformer of microphone array, it isn’t fit. It is necessary for microphone array to design broadband beamformer without specific arrays[3][4]. In order to achieve broadband frequency invariant beamformer[3], it is necessary to consider frequency invariant beamformer with mismatch errors caused by the gain, phase and position of the microphone[5][6]. For this purpose, in this paper, frequency invariant beamforming algorithm based on the linear constrainted minimum variance (LCMV), the spatial response variation function(SRV) of the microphone array, and diagonal loading method are used to design frequency invariant beamformer and improve the robustness on the given frequency range and the region. Array Model Consider a M-element linear microphone array in the far-field (not general, this method can be applied to arbitrary array structure), FIR subfilter with tap length L is added to microphone. The array output signal can be expressed as ( ) ( ) H k k = y w x , where H denotes conjugate transpose.The weight vector of microphone array response is written as 11 1 1 ( ) [ ( ), , ( ), , ( ), , ( )] T M L ML k w k w k w k w k = w L L L , T denotes transpose. 11 1 1 ( ) [ ( ), , ( ), , ( ), , ( )] T M L ML k x k x k x k x k = x L L L and it denotes the received signal of microphone array, the definition of the angle between the direction of the sound source and the line array is θ , the beam-pattern at frequency f and angle θ of arrival can be expressed as ( , ) | ( , )| T H f f θ θ = w d ,where frequency f is belongs to [ , ] l h f f ,the angle θ of the received signal is in[ , ] l h θ θ , and ( , ) f θ d denotes the array response vector. 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering (ICCMCEE 2015) © 2015. The authors Published by Atlantis Press 102 Frequency Invariant Beamforming Algorithm and Diagonal Loading Method The spatial response variation function(SRV) is given by

Different beamforming techniques are looking forward to playing an important role in the next generation Multiple Input and Multiple Output (MIMO) radar based system. For the enhancement in resolution and for identification of more number of targets, MIMO radar is very useful. We get more flexibility in beampattern design using the MIMO concept in the radar system. Beamforming is used in sensor arrays for signal transmission or reception in particular directions. For improving resolution of target and reducing interference of signals from undesirables,a powerful technique is used in signal processing of MIMO radar known as beamforming. Various sorts of calculations are researched utilizing conventional beamforming and Adaptive beamforming to alter the necessary weighting on radio wire components. Effects of various errors are illustrated. Also, Linearly Constrained Minimum Variance (LCMV) and Minimum Variance Distortionless Variance (MVDR) calculations introduced which are the techniques gone under versatile beamforming and their belongings are talked about. The most useful algorithms which are the versatile techniques for beamforming as mentioned above are discussed in this paper.From the comparison of LCMV and MVDR algorithms with different number of array elements concluded which is better in different situations of the MIMO radar system.

The linearly constrained minimum variance (LCMV) method in adaptive signal processing is designed in such a way that it attempts to minimize the output power subject to constraints on the look directions. This adaptive method has been applied to time-reversal mirror (TRM) for simultaneous multiple focusing and its efficiency has been verified by [Kim et al., J. Acoust. Soc. Am. 109 (5), 1817–1825 (2001)]. However, the norm of the weighting vector ‖w‖ that satisfies the constraints tends to become very large when two probe sources (PS) are close to each other. It causes prominent spatial sidelobes as the weighting vector w is back-propagated in TR focusing. In this study, a relationship between ‖w‖ and PS locations is illustrated, and LCMV method is partially reformulated to calculate the weighting vector w which satisfies new constraint responses, so that sidelobes are significantly suppressed. The proposed method for improved adaptive time-reversal mirror for stable simultaneous multiple focusing is verified using numerical simulations and experimental data.

This paper addresses a simple, and yet effective approach to the design of block adaptive beamformers via parallel projection method (PPM), which is an extension of the classic projection onto convex set (POCS) method to inconsistent sets scenarios. The proposed approach begins with the construction of the convex constraint sets which the weight vector of the adaptive beamformer lies in. The convex sets are judiciously chosen to force the weights to possess some desirable properties or to meet some prescribed rules. Based on the minimum variance criterion and a fixed gain at the look direction, two constraint sets including the minimum variance constraint set and the gain constraint set are considered. For every input block of data, the weights of the proposed beamformer can then be determined by iteratively projecting the weight vector onto these convex sets until it converges. Furnished simulations show that the proposed beamformer provides superior performance compared with previous works in various scenarios but yet in general with lower computational overhead.

Reduced rank (RR) linearly constrained minimum variance (LCMV) adaptive beamforming with quadratic pattern constraints (QPC) is applied to space-time adaptive processing (STAP) for airborne radar. The problem is formulated for general rank reducing transformations and main beam and sidelobe pattern control is achieved by imposing a set of inequality constraints on the mean-square error between the adaptive pattern and a desired beampattern over a set of angle-Doppler regions. Both a fixed PRI-staggered post-Doppler transformation and a data-dependent principal component transformation are shown to perform at least as well as full-dimension LCMV-QPC STAP in terms of processing gain and sidelobe reduction with significantly reduced computational complexity.