Adaptive beamformer

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

Blind adaptive beamforming for CDMA based PCS/cellular

Abstract: A blind adoptive beamforming scheme is developed for CDMA that cancels strong multi-user access interference and optimally combines multipath based on the following observation: after passing the output of each antenna through a matched filter corresponding to the PN sequence of the desired user, one can isolate periods of time when the cochannel interference dominates. This allows one to estimate the spatio-frequency correlation matrix of the co-channel interference alone. The weight vector yielding the optimum signal to interference plus noise ratio for bit decisions is the "largest" generalised eigenvector of the signal plus interference spatio-frequency correlation matrix in the metric of the spatio frequency correlation matrix of the co-channel interference alone. Simulations are presented demonstrating the efficacy of the procedure.

Eigenspace-Based Minimum Variance Combined With Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging

Abstract: In photoacoustic imaging, delay-and-sum (DAS) algorithm is the most commonly used beamformer. However, it leads to a low resolution and high level of sidelobes. Delay-multiply-and-sum (DMAS) was introduced to provide lower sidelobes compared to DAS. In this paper, to improve the resolution and sidelobes of DMAS, a novel beamformer is introduced using the eigenspace-based minimum variance (EIBMV) method combined with DMAS, namely EIBMV-DMAS. It is shown that expanding the DMAS algebra leads to several terms, which can be interpreted as DAS. Using the EIBMV adaptive beamforming instead of the existing DAS (inside the DMAS algebra expansion) is proposed to improve the image quality. EIBMV-DMAS is evaluated numerically and experimentally. It is shown that EIBMV-DMAS outperforms DAS, DMAS, and EIBMV in terms of resolution and sidelobes. In particular, at the depth of 11 $\text{mm}$ of the experimental images, EIBMV-DMAS results in about 113 $\text{dB}$ and 50 $\text{dB}$ sidelobe reduction, compared to DMAS and EIBMV, respectively. At the depth of 7 $\text{mm}$ , for the experimental images, the quantitative results indicate that EIBMV-DMAS leads to improvement in signal-to-noise ratio of about 75% and 34%, compared to DMAS and EIBMV, respectively.

Noise suppression system and method for phased-array based systems

Abstract: This invention relates to a system and method for suppressing external interference in radar data provided by a plurality of sensors from a main sensor array, the data being pre-processed. The noise suppression system includes a first processing module and a second processing module. The first processing module receives the radar data and produces matched radar data while the second processing module receives the radar data and produces mis-matched radar data. The system further includes a beamformer that is in communication with the first processing module and an adaptive beamformer that is in communication with the second processing module and the beamformer. The beamformer receives the matched radar data and produces beamformed matched radar data.

Wideband RELAX and Wideband CLEAN for Aeroacoustic Imaging

Abstract: Microphone arrays can be used for acoustic source localization and characterization in wind tunnel testing. In this paper, the wideband RELAX (WB-RELAX) and the wideband CLEAN (WB-CLEAN) algorithms are presented for aeroacoustic imaging using an acoustic array. WB-RELAX is a parametric approach that can be used efficiently for point source imaging without the sidelobe problems suffered by the delay-and-sum beamforming approaches. WB-CLEAN does not have sidelobe problems either, but it behaves more like a nonparametric approach and can be used for both point source and distributed source imaging. Moreover, neither of the algorithms suffers from the severe performance degradations encountered by the adaptive beamforming methods when the number of snapshots is small and/or the sources are highly correlated or coherent with each other. A two-step optimization procedure is used to implement the WB-RELAX and WB-CLEAN algorithms efficiently. The performance of WB-RELAX and WB-CLEAN is demonstrated by applying them to measured data obtained at the NASA Langley Quiet Flow Facility using a small aperture directional array (SADA). Somewhat surprisingly, using these approaches, not only were the parameters of the dominant source accurately determined, but a highly correlated multipath of the dominant source was also discovered.

Enhanced Minimum Variance Beamforming

Abstract: The MVDR criterion for achieving maximum wavefront Signal-to-Noise Ratio (SNR) gain with a spatial sensor array beamformer is well established. The MVDR beamformer has its technical origins in the early 1960’s (see [1] for bibliography). Eigenstructure based, signal subspace wavenumber spectrum analysis procedures appeared in the early 1970’s [2, 3] and have evolved to the present day high resolution procedures typified by the MUSIC algorithm [4]. The difference between a beamformer, which can produce a directional waveform estimate, and a wavenumber analyzer which provides a source direction estimate only is emphasized. Accordingly, the evaluation of MVDR and eigenstructure techniques has been along two parallel paths. This dual perspective has precluded the establishment of common theoretical and practical approaches which could result in the exploitation of the advantages of both techniques. The enhanced minimum variance (EMV) beamformer represents an attempt to find such a common viewpoint and to subsequently address current space-time processing technical issues from this unified perspective. Such issues as detection of threshold signals, the determination of the number of sources present, reducing computational burden, coherent multipath arrivals and wideband signals are of particular concern.