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Adaptive beamformer

About: Adaptive beamformer is a research topic. Over the lifetime, 4934 publications have been published within this topic receiving 93100 citations.


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Journal ArticleDOI
TL;DR: This work introduces the first true-time-delay digital beamforming IC, which eliminates beam squinting error by adopting a baseband true- time-delay technique, and presents a constant output impedance current-steering digital-to-analog converter (DAC), which improves the spurious-free dynamic range (SFDR) of a bandpass delta–sigma modulator.
Abstract: Phased arrays are widely used due to their low power and small area usage. However, phased arrays depend on the narrowband assumption and, therefore, are not suitable for high-bandwidth applications. Emerging communication standards require increasingly higher bandwidths for improved data rates, which results in a need for timed arrays. However, high power consumption and large area requirements are drawbacks of radio frequency (RF) timed arrays. To resolve these issues, we introduce the first true-time-delay digital beamforming IC, which eliminates beam squinting error by adopting a baseband true-time-delay technique. Furthermore, we present a constant output impedance current-steering digital-to-analog converter (DAC), which improves the spurious-free dynamic range (SFDR) of a bandpass delta–sigma modulator by 7 dB. Due to the new DAC architecture, the 16-element beamformer improves SFDR by 13.7 dB from the array. Measured error vector magnitudes (EVMs) are better than 37 dB for 5-MBd quadratic-amplitude modulation (QAM)-64, QAM-256, and QAM-512. The prototype beamformer achieves nearly ideal beam patterns for both conventional and adaptive beamforming (i.e., adaptive nulling and tapering). The difference between normalized measured beam patterns and normalized simulated beam patterns is less than 1 dB within the 3-dB beamwidth. The beamformer, including 16 bandpass analog-to-digital converters (ADCs) occupies 0.29 mm2 and consumes 453 mW in total power.

48 citations

Journal ArticleDOI
TL;DR: The analysis shows that the adaptive beamformer techniques are insensitive to interference when its spatial singular vectors are so different from a lead field vector of a brain source that the generalized cosine between these two vectors is much smaller than unity.
Abstract: The influence of external interference on neuromagnetic source reconstruction by adaptive beamformer techniques was investigated. In our analysis, we assume that the interference has the following two properties: First, it is additive and uncorrelated with brain activity. Second, its temporal behavior can be characterized by a few distinct activities, and as a result, the spatio-temporal matrix of the interference has a few distinctly large singular values. Namely, the interference can be modeled as a low-rank signal. Under these assumptions, our analysis shows that the adaptive beamformer techniques are insensitive to interference when its spatial singular vectors are so different from a lead field vector of a brain source that the generalized cosine between these two vectors is much smaller than unity. Four types of numerical examples verifying this conclusion are presented.

48 citations

Journal ArticleDOI
TL;DR: A new beamformer, which combines the eigenspace based minimum variance (ESBMV) beamformer with a subarray coherence based postfilter (SCBP) for improving the quality of ultrasound plane-wave imaging, which showed an improved imaging quality and showed advantages over the ESBMV-Wiener beamformer in preserving a less grainy speckle.

48 citations

Proceedings ArticleDOI
16 Dec 2009
TL;DR: A tool for the modelling, analysis and simulation of direction-of-arrival (DOA) estimation and adaptive beamforming needed in the design of smart antenna arrays for wireless mobile communications and a simulation tool with a graphical user interface, which implements these algorithms is developed.
Abstract: This paper presents a tool for the modelling, analysis and simulation of direction-of-arrival (DOA) estimation and adaptive beamforming needed in the design of smart antenna arrays for wireless mobile communications. In this paper performance of adaptive beamforming algorithm has been studied for two different DOA estimation algorithms namely MUSIC & MVDR. MUSIC estimates the number of incident signals on the array and their directions of arrival. It also gives the direction of arrival of desired signal. The Minimum Variance Distortion less Response (MVDR) is a very well known algorithm to obtain the optimum weight vector, which maximizes the output signal to noise and interference ratio (SNIR) of multiple antennas. The LMS algorithm, which has been simulated, is a simple yet efficient technique for robust adaptive beamforming. The LMS algorithm recursively computes and updates the weight vector. It has been observed that LMS algorithm gives the response for the two different DOA estimation algorithms MUSIC & MVDR depending upon the DOA estimations.As the signals are randomly generated the response of LMS algorithm differ for some cases of DOA estimations using MUSIC & MVDR otherwise it gives same response for both the algorithms. The algorithms have been simulated in MATLAB 7.4 version. A simulation tool with a graphical user interface, which implements these algorithms, is developed. Results of numerical simulation are useful for the design of smart antennas systems with optimal performance.

48 citations

Journal ArticleDOI
TL;DR: Although the per-node signal transmission and computational power is greatly reduced compared to a centralized realization, it is shown that it is possible for each node to generate the centralized LCMV beamformer output as if it had access to all sensor signals in the entire network, without an explicit computation of the network-wide sensor signal covariance matrix.
Abstract: Linearly constrained minimum variance (LCMV) beamforming is a popular spatial filtering technique for signal estimation or signal enhancement in many different fields. We consider distributed LCMV (D-LCMV) beamforming in wireless sensor networks (WSNs) with either a fully connected or a tree topology. In the D-LCMV beamformer algorithm, each node fuses its multiple sensor signals into a single-channel signal of which observations are then transmitted to other nodes. We envisage an adaptive/time-recursive implementation where each node adapts its local LCMV beamformer coefficients to changes in the local sensor signal statistics, as well as to changes in the statistics of the wirelessly received signals. Although the per-node signal transmission and computational power is greatly reduced compared to a centralized realization, we show that it is possible for each node to generate the centralized LCMV beamformer output as if it had access to all sensor signals in the entire network, without an explicit computation of the network-wide sensor signal covariance matrix. We provide sufficient conditions for convergence and optimality of the D-LCMV beamformer. The theoretical results are validated by means of Monte Carlo simulations, which demonstrate the performance of the D-LCMV beamformer.

48 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202371
2022168
2021133
2020154
2019198
2018154