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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.


Papers
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Journal ArticleDOI
TL;DR: Two new approaches to adaptive beamforming in sparse subarray-based partly calibrated sensor arrays are developed, based on a worst-case beamformer design which exploits a specific structured ellipsoidal uncertainty model for the signal steering vector rather than the commonly used unstructured uncertainty models.
Abstract: Two new approaches to adaptive beamforming in sparse subarray-based partly calibrated sensor arrays are developed. Each subarray is assumed to be well calibrated, so that the steering vectors of all subarrays are exactly known. However, the intersubarray gain and/or phase mismatches are known imperfectly or remain completely unknown. Our first approach is based on a worst-case beamformer design which, in contrast to the existing worst-case designs, exploits a specific structured ellipsoidal uncertainty model for the signal steering vector rather than the commonly used unstructured uncertainty models. Our second approach is based on estimating the unknown intersubarray parameters by maximizing the output power of the minimum variance beamformer subject to a proper constraint that helps to avoid trivial solution of the resulting optimization problem. Different modifications of the second approach are developed for the cases of gain-and-phase and phase-only intersubarray distortions.

52 citations

Journal ArticleDOI
TL;DR: Simulation results demonstrate that the overestimation of interference powers hardly degrades the performance of adaptive beamforming, and the proposed algorithm achieves nearly optimal performance across a wide range of signal-to-noise ratios.
Abstract: Adaptive beamformer is very sensitive to model mismatch, especially when the signal-of-interest is present in the training data. In this paper, we focus on the topic of robust adaptive beamforming (RAB) based on interference-plus-noise covariance matrix (INCM) reconstruction. First, we analyze the effectiveness of several INCM reconstruction schemes, and particularly analyze the impacts of interference power estimation on RAB. Second, according to the analysis results, we develop a simplified algorithm to estimate the interference powers, and a RAB algorithm based on INCM reconstruction is then presented. Compared with some existing methods, the proposed algorithm simplifies the interference power estimation of INCM reconstruction. Aligned with our analysis, simulation results demonstrate that the overestimation of interference powers hardly degrades the performance of adaptive beamforming, and our proposed algorithm achieves nearly optimal performance across a wide range of signal-to-noise ratios.

52 citations

Journal ArticleDOI
L.C. Godara1
TL;DR: In this article, two adaptive algorithms which make use of all the available samples to estimate the required gradient are proposed and studied: recursive LMS (least mean squares) and improved LMS algorithm and exploits the Toeplitz structure of the array correlation matrix.
Abstract: Two adaptive algorithms which make use of all the available samples to estimate the required gradient are proposed and studied. The first algorithm is referred to as the recursive LMS (least mean squares) and is applicable for a general array. The second algorithm is referred to as the improved LMS algorithm and exploits the Toeplitz structure of the array correlation matrix and can be used only for an equispaced linear array. >

52 citations

Patent
Osamu Hoshuyama1
01 Sep 1995
TL;DR: In this article, a spatial beamforming filter is connected to a sensor array for respectively filtering and summing array signals to produce a first filter output containing a target signal that arrives in a specified direction.
Abstract: In an adaptive array beamformer, a spatial beamforming filter is connected to a sensor array for respectively filtering and summing array signals to produce a first filter output containing a target signal that arrives in a specified direction. First adaptive filters provide transversal-filtering the first filter output to produce a second filter output not containing the target signal, using a first error signal by restraining their tap weight coefficients. The array signals are further coupled to subtractors. Each subtractor detects a difference between the second filter output of the corresponding first adaptive filter and the corresponding sensor signal to derive the first error signal. Second adaptive filters provide transversal-filtering the first error signals of the subtractors to produce third filter outputs, using a second error signal, by restraining their tap weight coefficients. The third filter outputs are summed and subtracted from the first filter output to produce an output of the beamformer, which is supplied as the second error signal to the second adaptive filters

52 citations

Journal ArticleDOI
T. Gebauer1, Heinz G. Gockler1
TL;DR: This paper describes the analysis, design, simulation and implementation of the hierarchically structured demultiplexer, which was developed for use in a digital channel-individual beamforming network for an adaptive satellite array antenna for mobile satellite communications.
Abstract: A digital frequency-division multiplex demultiplexer for single channel per carrier (SCPC) signals was developed for use in a digital channel-individual beamforming network for an adaptive satellite array antenna for mobile satellite communications. The available ASIC (35000 gate functions in CMOS technology, clock rate up to 30 MHz with low power consumption) permits the separation of FDM signals comprising up to 16 channels. Starting from a suitable model for the demultiplexer beamforming system, this paper describes the analysis, design, simulation and implementation of the hierarchically structured demultiplexer. Aspects of digital beamforming are only discussed as far as necessary for understanding. >

51 citations


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