Showing papers on "Adaptive beamformer published in 2012"

Robust Adaptive Beamforming Based on Interference Covariance Matrix Reconstruction and Steering Vector Estimation

Abstract: Adaptive beamformers are sensitive to model mismatch, especially when the desired signal is present in training snapshots or when the training is done using data samples. In contrast to previous works, this correspondence attempts to reconstruct the interference-plus-noise covariance matrix instead of searching for the optimal diagonal loading factor for the sample covariance matrix. The estimator is based on the Capon spectral estimator integrated over a region separated from the desired signal direction. This is shown to be more robust than using the sample covariance matrix. Subsequently, the mismatch in the steering vector of the desired signal is estimated by maximizing the beamformer output power under a constraint that prevents the corrected steering vector from getting close to the interference steering vectors. The proposed adaptive beamforming algorithm does not impose a norm constraint. Therefore, it can be used even in applications where gain perturbations affect the steering vector. Simulation results demonstrate that the performance of the proposed adaptive beamformer is almost always close to the optimal value across a wide range of signal to noise and signal to interference ratios.

Robust Adaptive Beamforming Based on Steering Vector Estimation With as Little as Possible Prior Information

Abstract: A general notion of robustness for robust adaptive beamforming (RAB) problem and a unified principle for minimum variance distortionless response (MVDR) RAB techniques design are formulated. This principle is to use standard MVDR beamformer in tandem with an estimate of the desired signal steering vector found based on some imprecise prior information. Differences between various MVDR RAB techniques occur only because of the differences in the assumed prior information and the corresponding signal steering vector estimation techniques. A new MVDR RAB technique, which uses as little as possible and easy to obtain imprecise prior information, is developed. The objective for estimating the steering vector is the maximization of the beamformer output power, while the constraints are the normalization condition and the requirement that the estimate does not converge to any of the interference steering vectors and their linear combinations. The prior information used is only the imprecise knowledge of the antenna array geometry and angular sector in which the actual steering vector lies. Mathematically, the proposed MVDR RAB is expressed as the well known non-convex quadratically constrained quadratic programming problem with two constraints, which can be efficiently and exactly solved. Some new results for the corresponding optimization problem such as a new algebraic way of finding the rank-one solution from the general-rank solution of the relaxed problem and the condition under which the solution of the relaxed problem is guaranteed to be rank-one are derived. Our simulation results demonstrate the superiority of the proposed method over other previously developed RAB techniques.

A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix

Abstract: In recent years, adaptive beamforming methods have been successfully applied to medical ultrasound imaging, resulting in simultaneous improvement in imaging resolution and contrast. These improvements have been achieved at the expense of higher computational complexity, with respect to the conventional non-adaptive delay-and-sum (DAS) beamformer, in which computational complexity is proportional to the number of elements, O(M). The computational overhead results from the covariance matrix inversion needed for computation of the adaptive weights, the complexity of which is cubic with the subarray size, O(L3). This is a computationally intensive procedure, which makes the implementation of adaptive beamformers less attractive in spite of their advantages. Considering that, in medical ultrasound applications, most of the energy is scattered from angles close to the steering angle, assuming spatial stationarity is a good approximation, allowing us to assume the Toeplitz structure for the estimated covariance matrix. Based on this idea, in this paper, we have applied the Toeplitz structure to the spatially smoothed covariance matrix by averaging the entries along all subdiagonals. Because the inverse of the resulting Toeplitz covariance matrix can be computed in O(L2) operations, this technique results in a greatly reduced computational complexity. By using simulated and experimental RF data-point targets as well as cyst phantoms-we show that the proposed low-complexity adaptive beamformer significantly outperforms the DAS and its performance is comparable to that of the minimum variance beamformer, with reduced computational complexity.

On Transmit Beamforming for MIMO Radar

Abstract: Methods for transmit beamforming in multiple-input multiple-output (MIMO) radar based on the design of multiple correlated waveforms have been proposed. This paper points out that this approach couples the spatial (beamformer) and temporal (waveform) parts of the problem, significantly complicating the design. It is shown here that the most general form of transmit beamforming can be achieved in a decoupled form, using orthogonal (uncorrelated) waveforms and a multi-rank transmit beamformer. This formulation allows the use of standard beamformer design procedures. Examples are provided to illustrate the design of multi-rank beamformers for search and tracking applications. The examples include single and multiple beamformers, adaptive beamformers, and wide beams for illumination. These examples are illustrated by simulation results.

Processing audio signals

Abstract: A method of processing an audio signal transmitted from a remote transmitter and received at a local receiver of an acoustic system, includes at the receiver receiving with the audio signal an indication of remote transmitter gain, determining an overall system gain of the acoustic system from the remote transmitter gain and a local receiver gain and selectively applying a system gain reduction step to the audio signal if it is determined that the overall system gain exceeds a threshold.

Location-assisted beamforming

Abstract: The present invention relates to improving the throughput and power efficiency of communication systems that use beamforming to accurately orient transmission of signals between emitters and receivers. A first communication device implements adaptive beamforming with respect to a second communication device. That is, the first communication device controls the degree and/or direction of the anisotropy of a receiver and/or transmitter that it comprises according to the direction in which the second communication device is determined to lie, said determination being made using a communication between the first and second devices at a particular time. A suitable time for that adaptive beamforming communication to be made is chosen according to position data obtained from one or both of the communication devices.

A Two-Channel Time Modulated Linear Array With Adaptive Beamforming

Abstract: A Time modulated linear array (TMLA) can be configured to perform many of the functions of phased array antennas but at much lower cost as they do not require phase shifters. However, conventional time modulated linear arrays which are configured for beam steering based on a single output channel topology. Such a topology is inefficient in terms of time utilization of the array elements. In this contribution we have proposed a multiple output channels time modulated linear array which exploits the time redundancy of conventional systems. The concept is introduced by considering conventional harmonic beam steering and then extending the problem to two-channel adaptive beamforming.

Real-time microphone array with robust beamformer and postfilter for speech enhancement and method of operation thereof

Abstract: A microphone array processing system and method carried out in the system. In one embodiment, the system includes: (1) a beamformer configured to perform adaptive beamforming on gain-compensated signals received from a plurality of microphones, the adaptive beamforming including dynamic range compression and diagonal loading of a sample correlation matrix based on order statistics and (2) a postfilter configured to receive an output of the beamformer and reduce noise components remaining from the beamforming.

Eigenspace Based Minimum Variance Beamforming Applied to Ultrasound Imaging of Acoustically Hard Tissues

Abstract: Minimum variance (MV) based beamforming techniques have been successfully applied to medical ultrasound imaging. These adaptive methods offer higher lateral resolution, lower sidelobes, and better definition of edges compared to delay and sum beamforming (DAS). In standard medical ultrasound, the bone surface is often visualized poorly, and the boundaries region appears unclear. This may happen due to fundamental limitations of the DAS beamformer, and different artifacts due to, e.g., specular reflection, and shadowing. The latter can degrade the robustness of the MV beamformers as the statistics across the imaging aperture is violated because of the obstruction of the imaging beams. In this study, we employ forward/backward averaging to improve the robustness of the MV beamforming techniques. Further, we use an eigen-spaced minimum variance technique (ESMV) to enhance the edge detection of hard tissues. In simulation, in vitro, and in vivo studies, we show that performance of the ESMV beamformer depends on estimation of the signal subspace rank. The lower ranks of the signal subspace can enhance edges and reduce noise in ultrasound images but the speckle pattern can be distorted.

Main-Lobe Jamming Suppression Method of using Spatial Polarization Characteristics of Antennas

Abstract: As interferences are introduced from a main-lobe direction, which results in target signals being masked by interference, traditional adaptive beamforming and eigen-subspace projection methods cannot suppress main-lobe interferences effectively. A novel interference suppression method is proposed that processes the sample data by using the scanning property of a single polarized antenna. The orthogonal polarization decomposition and polarization estimation of the receiving signal based on the spatial polarization characteristics of the antenna are realized so that the interference is diminished. The effect of an elevation measurement error on interference suppression performance is eliminated by design spatial multinotch virtual polarization filtering. Theoretical and simulation results show that it made single polarized radar own a polarization information processing ability that improved radar working performance.

Adaptive Beamforming for Uniform Linear Arrays With Unknown Mutual Coupling

Abstract: This letter proposes a new adaptive beamforming algorithm for uniform linear arrays (ULAs) with unknown mutual coupling. It is based on the fact that the mutual coupling matrix (MCM) of a ULA can be approximated as a banded symmetric Toeplitz matrix as the mutual coupling between two sensor elements is inversely related to their separation, and hence it is negligible when they are separated by a few wavelengths. Taking advantage of this specific structure of the MCM, a new approach to calibrate the signal steering vector is proposed. By incorporating this improved steering vector estimate with a diagonally loaded robust beamformer, a new adaptive beamformer for ULA with unknown mutual coupling is obtained. Simulation results show that the proposed steering vector estimate considerably improves the robustness of the beamformer in the presence of unknown mutual coupling. Moreover, with appropriate diagonal loading, it is found that the proposed beamformer can achieve nearly optimal performance at all signal-to-noise ratio (SNR) levels.

Adaptive beamforming for array signal processing in aeroacoustic measurements

Abstract: Phased microphone arrays have become an important tool in the localization of noise sources for aeroacoustic applications. In most practical aerospace cases the conventional beamforming algorithm of the delay-and-sum type has been adopted. Conventional beamforming cannot take advantage of knowledge of the noise field, and thus has poorer resolution in the presence of noise and interference. Adaptive beamforming has been used for more than three decades to address these issues and has already achieved various degrees of success in areas of communication and sonar. In this work an adaptive beamforming algorithm designed specifically for aeroacoustic applications is discussed and applied to practical experimental data. It shows that the adaptive beamforming method could save significant amounts of post-processing time for a deconvolution method. For example, the adaptive beamforming method is able to reduce the DAMAS computation time by at least 60% for the practical case considered in this work. Therefore, adaptive beamforming can be considered as a promising signal processing method for aeroacoustic measurements.

Improved Antenna Array Adaptive Beamforming with Low Side Lobe Level Using a Novel Adaptive Invasive Weed Optimization Method

Abstract: An improved adaptive beamforming technique of antenna arrays is introduced. The technique is implemented by using a novel Invasive Weed Optimization (IWO) variant called Adaptive Dispersion Invasive Weed Optimization (ADIWO) where the seeds produced by a weed are dispersed in the search space with standard deviation specifled by the fltness value of the weed. The adaptive seed dispersion makes the ADIWO converge faster than the conventional IWO. This behavior is verifled by applying both the ADIWO and the conventional IWO on well-known test functions. The ADIWO method is utilized here as an adaptive beamformer that makes a uniform linear antenna array steer the main lobe towards the direction of arrival (DoA) of a desired signal, form nulls towards the respective DoA of several interference signals and achieve low side lobe level (SLL). The proposed ADIWO based beamformer is compared to a Particle Swarm Optimization (PSO) based beamformer and a well known beamforming method called Minimum Variance Distortionless Response (MVDR). Several cases have been studied with difierent number of interference signals and difierent power level of additive zero-mean Gaussian noise. The results show that the ADIWO provides su-cient steering ability regarding the main lobe and the nulls, works faster than the PSO and achieves better SLL than the PSO and MVDR.

Signal-enhancing beamforming in an augmented reality environment

Abstract: An augmented reality environment allows interaction between virtual and real objects. Beamforming techniques are applied to signals acquired by an array of microphones to allow for simultaneous spatial tracking and signal acquisition from multiple users. Localization information such as from other sensors in the environment may be used to select a particular set of beamformer coefficients and resulting beampattern focused on a signal source. Alternately, a series of beampatterns may be used iteratively to localize the signal source in a computationally efficient fashion. The beamformer coefficients may be pre-computed.

An approach to multibeam covariance matrices for adaptive beamforming in ultrasonography

Abstract: Medical ultrasound imaging systems are often based on transmitting, and recording the backscatter from, a series of focused broadband beams with overlapping coverage areas. When applying adaptive beamforming, a separate array covariance matrix for each image sample is usually formed. The data used to estimate any one of these covariance matrices is often limited to the recorded backscatter from a single transmitted beam, or that of some adjacent beams through additional focusing at reception. We propose to form, for each radial distance, a single covariance matrix covering all of the beams. The covariance matrix is estimated by combining the array samples after a sequenced time delay and phase shift. The time delay is identical to that performed in conventional delay-and-sum beamforming. The performance of the proposed approach in conjunction with the Capon beamformer is studied on both simulated data of scenes consisting of point targets and recorded ultrasound phantom data from a specially adapted commercial scanner. The results show that the proposed approach is more capable of resolving point targets and gives better defined cyst-like structures in speckle images compared with the conventional delay-and-sum approach. Furthermore, it shows both an increased robustness to noise and an increased ability to resolve point-like targets compared with the more traditional per-beam Capon beamformer.

Model-Based Subspace Projection Beamforming for Deep Interference Nulling

Abstract: This paper considers the problem of adaptive array processing for interference canceling to drive very deep nulls in difficult signal environments. In many practical scenarios, the achievable null depth is limited by covariance matrix estimation error leading to poor identification of the interference subspace. We address the particularly troublesome cases of low interference-to-noise ratio (INR), relatively rapid interference motion, and correlated noise across the receiving array. A polynomial-based model is incorporated in the proposed algorithm to track changes in the array covariance matrix over time, mitigate interference subspace estimation errors, and improve canceler performance. The application of phased array feeds for radio astronomical telescopes is used to illustrate the problem and proposed solution. Here even weak residual interference after cancellation may obscure a signal of interest, so very deep beampattern nulls are required. Performance for conventional subspace projection (SP) is compared with polynomial-augmented SP using simulated and real experimental data, showing null-depth improvement of 6 to 30 dB.

Semi-Adaptive Beamforming for OFDM Based Hybrid Terrestrial-Satellite Mobile System

Abstract: Adaptive interference mitigation requires significant resources due to recursive processing. Specific to satellite systems, interference mitigation by employing adaptive beamforming at the gateway or at the satellite both have associated problems. While ground based beamforming reduces the satellite payload complexity, it results in added feeder link bandwidth requirements, higher gateway complexity and suffers from feeder link channel degradations. On the other hand, employing adaptive beamforming onboard the satellite gives more flexibility in case of variation in traffic dynamics and also for changing of beam patterns. However, these advantages come at the cost of additional complexity at the satellite. In pursuit of retaining the benefits of onboard beamforming and to reduce the complexity associated with adaptive processing, we here propose a novel semi-adaptive beamformer for a Hybrid Terrestrial-Satellite Mobile System. The proposed algorithm is a dual form of beamforming that enables adaptive and non-adaptive processing to coexist via a robust gradient based switching mechanism. We present a detailed complexity analysis of the proposed algorithm and derive bounds associated with its power requirements. In the scenarios studied, results show that the proposed algorithm consumes up to 98% less filter computing power as compared to full-adaptive case without compromising on system performance.

Adaptive detection of distributed targets with orthogonal rejection

Abstract: This study deals with the problem of detecting distributed targets in the presence of homogeneous and partially homogeneous Gaussian disturbance with unknown covariance matrix. The proposed detectors improve the adaptive beamformer orthogonal rejection test (ABORT) idea to address detection of distributed targets, which makes it possible to decide whether some observations contain a useful target or a signal belonging to the orthogonal complement of the useful subspace. At the design stage, the authors resort to either the plain generalised likelihood ratio test (GLRT) or ad hoc design procedures. Remarkably, the considered criteria lead to receivers ensuring the constant false alarm rate (CFAR) property with respect to the unknown quantities. Moreover, authors’ derivations show that the ad hoc detector for a partially homogeneous environment coincides with the generalised adaptive subspace detector. The performance assessment conducted by Monte Carlo simulation has confirmed the effectiveness of the newly proposed detection algorithms.

Adaptive Beamforming with Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean PSO

Abstract: A new adaptive beamforming technique based on neural networks (NNs) is proposed. The NN training is accomplished by applying a novel optimization method called Mutated Boolean PSO (MBPSO). In the beginning of the procedure, the MBPSO is repeatedly applied to a set of random cases to estimate the excitation weights of an antenna array that steer the main lobe towards a desired signal, place nulls towards several interference signals and achieve the lowest possible value of side lobe level. The estimated weights are used to train e-ciently a NN. Finally, the NN is applied to a new set of random cases and the extracted radiation patterns are compared to respective patterns extracted by the MBPSO and a well-known robust adaptive beamforming technique called Minimum Variance Distortionless Response (MVDR). The aforementioned comparison has been performed considering uniform linear antenna arrays receiving several interference signals and a desired one in the presence of additive Gaussian noise. The comparative results show the advantages of the proposed technique.

Robust Adaptive Beamforming Using Multidimensional Covariance Fitting

Abstract: Over the last decade, several set-based worst-case beamformers have been proposed. It has been shown that some of these beamformers can be formulated equivalently as one-dimensional (ID) covariance fitting problems. Based on this formulation, we show that these beamformers lead to inherently nonoptimum results in the presence of interferers. To mitigate the detrimental effect of interferers, we extend the ID covariance fitting approach to multidimensional (MD) covariance fitting, modeling the source steering vectors by means of uncertainty sets. The proposed MD covariance fitting approach leads to a nonconvex optimization problem. We develop a convex approximation of this problem, which can be solved, for example, by means of the logarithmic barrier method. The complexity required to compute the barrier function and its first- and second-order derivatives is derived. Simulation results show that the proposed beamformer based on MD covariance fitting achieves an improved performance as compared to the state-of-the-art narrowband beamformers in scenarios with large sample support.

An Analog Integrated Circuit Beamformer for High-Frequency Medical Ultrasound Imaging

Abstract: We designed and fabricated a dynamic receive beamformer integrated circuit (IC) in 0.35-μm CMOS technology. This beamformer IC is suitable for integration with an annular array transducer for high-frequency (30-50 MHz) intravascular ultrasound (IVUS) imaging. The beamformer IC consists of receive preamplifiers, an analog dynamic delay-and-sum beamformer, and buffers for 8 receive channels. To form an analog dynamic delay line we designed an analog delay cell based on the current-mode first-order all-pass filter topology, as the basic building block. To increase the bandwidth of the delay cell, we explored an enhancement technique on the current mirrors. This technique improved the overall bandwidth of the delay line by a factor of 6. Each delay cell consumes 2.1-mW of power and is capable of generating a tunable time delay between 1.75 ns to 2.5 ns. We successfully integrated the fabricated beamformer IC with an 8-element annular array. Experimental test results demonstrated the desired buffering, preamplification and delaying capabilities of the beamformer.

Cell splitting based on active antennas: Performance assessment for LTE system

Abstract: The adaptive beamforming technology is an important tool in modern communication systems like LTE-Advanced in order to enhance system capacity. Beamforming techniques allow to direct the signal toward the useful users while decreasing at the same time the interference toward and from the users of the neighbouring cells. The aim of this document is to study the impact of both vertical and horizontal beamforming on LTE system performance using Active Antenna System (AAS).

A distributed adaptive GSC beamformer over coordinated antenna arrays network for interference mitigation

Abstract: A general framework of adaptive coordinated beamforming is proposed to enhance the performance of distributed antenna arrays network based on generalized sidelobe canceller (GSC) The proposed method exploits GSC structure to realize the convex combination of distributed neighboring nodes' weights sophistically such that the steady-state and robustness of antenna arrays network are greatly improved in strong interference environment The optimal design as well as adaptive implementation by least-mean squares (LMS) algorithm is developed with detailed theoretical analysis on the stability and the steady-state output signal to interference plus noise ratio Moreover, the effectiveness of the proposed method is demonstrated by simulation studies

The Effect of Mutual Coupling on the Nulling Performance of Adaptive Antennas

Abstract: The effect of mutual coupling on the performance of adaptive antennas has been a topic of considerable interest for the last three decades. The general conclusion of the work reported in the open literature is that mutual coupling degrades the performance of adaptive antennas. We have carried out an in-depth study of the effects of mutual coupling on the performance of adaptive antennas. Our studies show that this conclusion is not entirely correct. Yes, one does need the in-situ array manifold to obtain the fixed response in the desired signal direction. Otherwise, adaptive weights can also suppress the desired signal. Note that for adaptive antennas based on minimizing the mean squared error between the array output and a locally generated reference signal, this is not an issue. However, mutual coupling between antenna elements hardly affects the nulling performance of adaptive antennas. In fact, in a given size aperture, as the number of antenna elements is increased, one obtains better nulling performance, irrespective of the increased mutual coupling between antenna elements. Also, as expected, for strong wideband interfering signals, one should carry out space-time adaptive processing (STAP).

Blind Adaptive Beamformer Based on Orthogonal Projections for GNSS

Abstract: The quality of the ranging data provided by a global navigation satellite systems (GNSS) receiver largely depends on the synchronization error, that is, on the accuracy of the propagation time-delay estimation of the line-of-sight (LOS) signal. In case the LOS signal is corrupted by several superimposed delayed replicas (reflective, diffractive, or refractive multipath) and/or additional radio frequency interference (RFI), the estimation of the propagation time-delay and thus the position can be severely degraded using state-of-the-art GNSS receivers. Multi-antenna GNSS receivers enable application of array processing for effective multipath and interference mitigation. Especially, beamforming (spatial filtering) approaches have been studied intensely for GNSS in the past years due to a balanced trade-off between performance and complexity. Usually these beamforming approaches require knowledge of the spatial signature (spatial reference) of the desired signal and thus require detailed knowledge of the direction-of-arrival (DOA) of the LOS signal and/or non-LOS signals, the antenna response, the array geometry, and other hardware biases. Even if the antenna array response can be approximately determined, either by empirical measurements (array calibration) or by making certain assumptions (e.g. identical sensor elements in known locations), the true antenna array response can be significantly different due to for example changes in antenna location, temperature, calibration inaccuracy and the surrounding environment. Thus, robust beamforming algorithms were developed in order to cope with errors in the array response model to be applied to derive the spatial filter. In this work we propose a blind adaptive beamforming approach based on orthogonal projections for GNSS, for which knowledge about the array response and spatial reference for the LOS signal are not required. The proposed approach is capable of adaptively mitigating RFI and multipath components based on orthogonal projections. In order to derive the needed projectors adaptively two eigendecompositions of the estimate of the spatial covariance matrix before (pre-correlation) and after (post-correlation) despreading are performed. Based on these eigendecompositions appropriate estimations of subspaces are achieved in order to derive projectors onto the interference free and multipath free subspaces, respectively. At pre-correlation stage the covariance matrix estimation can be evaluated over a short time interval in order to realize good performance in case of a jammer with a high time-frequency dynamic (e.g. Chirp-like jammer). For the implementation within a real-time receiver, dedicated building-blocks are used. Computation of the covariance matrix and the projection into the interference free subspace is performed by hardware-macros at sampling-rate. In contrast, the eigendecomposition is executed on a processor achieving projector update-rates in the kHz-range. Implementation issues addressing quantization losses related to wordlength configurations for both hardware building-blocks are discussed. Once interfering signals are removed from the input signals, wordlengths can be reduced in order to minimize implementation costs for the subsequent despreading or correlation. Wordlength reduction is realized using a digital automatic gain control (AGC). At post-correlation stage all available degrees of freedom are used for multipath mitigation, noise reduction and further cancellation of residual interferences. After despreading, projection into the multipath free subspace becomes an individual process for each channel of the receiver. Considering the computational load on a navigation processor, this is a very challenging task since covariance matrices and eigendecompositions have to be computed individually for each channel. A cost-analysis in terms of processing cycles on an embedded processor for the covariance matrix computation and eigendecomposition is provided. In addition, the relation between the covariance observation time and multipath mitigation performance are analyzed for selected scenarios. Simulation results show that the proposed blind adaptive beamforming approach based on orthogonal projections achieves effective interference and multipath mitigation capabilities compared to state-of-the-art non-blind beamforming algorithms. The overall complexity required by the blind beamformer is discussed and a feasible hardware implementation is derived. The accuracy and numerical stability of estimation of the spatial covariance matrix before and after despreading are shown for both block interval and recursive estimation methods. Providing costs in terms of computational requirements and navigation performance related to a specific implementation a trade-off between estimation robustness, time-frequency characterization and mitigation capability is derived. Based on this analysis a complete multi-antenna GNSS receiver architecture is proposed taking into account hardware complexity and navigation performance. A software bit accurate representation of the receiver hardware platform is used for performance evaluation. As the proposed blind approach does not require precise a priori information about the DOAs of the LOS (spatial reference) or non-LOS signals and about the antenna array response, robustness with respect to errors in the antenna array response model and additional hardware biases can be achieved without further increase of complexity.

Noise Adaptive Beamforming for Microphone Arrays

Abstract: The subject disclosure is directed towards a noise adaptive beamformer that dynamically selects between microphone array channels, based upon noise energy floor levels that are measured when no actual signal (e.g., no speech) is present. When speech (or a similar desired signal) is detected, the beamformer selects which microphone signal to use in signal processing, e.g., corresponding to the lowest noise channel. Multiple channels may be selected, with their signals combined. The beamformer transitions back to the noise measurement phase when the actual signal is no longer detected, so that the beamformer dynamically adapts as noise levels change, including on a per-microphone basis, to account for microphone hardware differences, changing noise sources, and individual microphone deterioration.

A Speech Distortion and Interference Rejection Constraint Beamformer

Abstract: Signals captured by a set of microphones in a speech communication system are mixtures of desired and undesired signals and ambient noise. Existing beamformers can be divided into those that preserve or distort the desired signal. Beamformers that preserve the desired signal are, for example, the linearly constrained minimum variance (LCMV) beamformer that is supposed, ideally, to reject the undesired signal and reduce the ambient noise power, and the minimum variance distortionless response (MVDR) beamformer that reduces the interference-plus-noise power. The multichannel Wiener filter, on the other hand, reduces the interference-plus-noise power without preserving the desired signal. In this paper, a speech distortion and interference rejection constraint (SDIRC) beamformer is derived that minimizes the ambient noise power subject to specific constraints that allow a tradeoff between speech distortion and interference-plus-noise reduction on the one hand, and undesired signal and ambient noise reductions on the other hand. Closed-form expressions for the performance measures of the SDIRC beamformer are derived and the relations to the aforementioned beamformers are derived. The performance evaluation demonstrates the tradeoffs that can be made using the SDIRC beamformer.

Comparative Study of Neural Network Training Applied to Adaptive Beamforming of Antenna Arrays

Abstract: This paper presents a comparative study of neural network (NN) training. The trained NNs are used as adaptive beamformers of antenna arrays. The training is performed either by a recently developed method called Mutated Boolean PSO (MBPSO) or by a well known beamforming method called Minimum Variance Distortionless Response (MVDR). The training procedure starts by applying the MBPSO and the MVDR to a set of random cases where a linear antenna array receives a signal of interest (SOI) and several interference signals at random directions of arrival (DOA) difierent from each other in the presence of additive Gaussian noise. For each case, the MBPSO and the MVDR are independently applied to estimate respective excitation weights that make the array steer the main lobe towards the DOA of the SOI and form nulls towards the DOA of the interference signals. The lowest possible value of side lobe level (SLL) is additionally required. The weights extracted by the MBPSO and the weights extracted by the MVDR are used to train respectively two difierent NNs. Then, the two trained NNs are independently applied to a new set of cases, where random DOA are chosen for the SOI and the interference signals. Finally, the radiation patterns extracted by the two NNs are compared to each other regarding the steering ability of the main lobe and the nulls as well as the side lobe level. The comparison exhibits the superiority of the NN trained by the MBPSO.

An architecture for an embedded antenna-array digital GNSS receiver using subspace-based methods for spatial filtering

Abstract: This paper presents an architecture for an embedded multi-antenna digital GNSS receiver. A two-stage adaptive beamformer for interference suppression and Line-of-Sight (LoS) signal amplification is presented and analyzed w.r.t. to an efficient implementation on embedded receivers. Jammer signals are mitigated at pre-correlation stage whereas the LoS signals are amplified at post-correlation stage. The method is based on a subspace-based approach where filter coefficients are derived from the eigenvalues and -vectors of the covariance matrix. In the first stage, the covariance matrix is determined immediately from the digital antenna signals for interference mitigation and in the second stage, the matrix is computed based on the correlator outputs of each satellite in LoS. Dedicated buildingblocks for covariance matrix estimation and filtering are required for interference mitigation since this operation is computed on sampling rate. A fixed-point VHDL implementation and related costs in terms of logic-cell requirements on an FPGA are provided for both blocks. Eigendecomposition is computed on an embedded processor. The implementation of two decomposition algorithms (one for interference mitigation and the other one for LoS-signal amplification) are presented. Optimizations and costs in terms of processing-cycles on an embedded processor are provided.