Adaptive beamformer

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

Deep Learning for Fast Adaptive Beamforming

Abstract: The real-time nature that makes diagnostic ultrasonography so appealing to clinicians imposes strong constraints on the computational complexity of image reconstruction algorithms. As such, these typically rely on traditional delay-and-sum beamforming, a low-complexity approach that unfortunately comes at the cost of reduced image quality as compared to more advanced and content-adaptive beamformers. Here, we propose a model-aware deep learning strategy to ultrasound image reconstruction, which leverages knowledge of minimum variance beamforming while exploiting the efficiency of deep neural networks. Our approach yields high quality images with strong contrast at real-time reconstruction rates. The neural network is trained using in vivo and simulated radio frequency channel data of a single plane wave transmit, and corresponding high-quality minimum-variance beamformed reconstructions. Performance is benchmarked using simulated acquisitions from the PICMUS [1] dataset, demonstrating the convincing generalizability and image quality of the proposed beamformer.

Mainlobe Interference Suppression Based on Eigen-Projection Processing and Covariance Matrix Reconstruction

Abstract: A mainlobe interference suppression method is proposed based on eigen-projection processing and covariance matrix reconstruction. In the proposed method, the eigen-projection matrix is calculated based on the eigenvector of mainlobe interference to suppress the mainlobe interference in received echo data. The interference covariance matrix is reconstructed after the eigenvalue of mainlobe interference is replaced by the average value of noise eigenvalues to eliminate the effect of mainlobe interference to adaptive weight vector. Finally, the output of adaptive beamforming is calculated by processed echo data and adaptive weight vector obtained based on reconstructed covariance matrix. Simulation results show that the proposed method can suppress the mainlobe interference effectively and achieve excellent performance .

Adaptive microphone array for unknown desired speaker’s transfer function

Abstract: The main drawback of minimum variance distortionless response (MVDR) beamformer is the cancellation of the desired speech signal and its degradation in multi-path wave propagation environment. To make the adaptive algorithm robust against room reverberation and to prevent desired signal cancellation an estimation of unknown desired speaker’s transfer function was proposed. The estimation is based on the signal and the interference covariance matrices. The estimated transfer function is then applied to the MVDR beamformer. The proposed algorithm was tested on a simulated room with reverberation. The results showed better quality of the restored speech compared to some typical adaptive algorithms.

2-D spatial smoothing for multipath coherent signal separation

Abstract: The use of two-dimensional spatial smoothing (SS) to increase channel capacity of wireless communications system is discussed, The necessary and sufficient conditions on array configuration for applying SS to multiple signal classification (MUSIC), and adaptive beamforming algorithms are defined and proved, This array must have an orientational invariance structure with an ambiguity free center array, and the number of subarrays must be larger than or equal to the size of the largest group of coherent signals. We also studied the cause of ambiguities in a multipath environment. We found the necessary and sufficient conditions for a three-sensor array manifold to be ambiguity free and identified several higher order ambiguity situations, If an array is also central symmetric, the forward/backward SS (FBSS) can be used to improve the resolution. Finally, we extended our results to the estimation of signal parameters via rotational invariance techniques (ESPRIT). All the predicted results are verified by simulations.

Capacity enhancement using adaptive arrays in an AMPS system

Abstract: Adaptive arrays for an advanced mobile phone service (AMPS) system can significantly increase cell capacity, improve signal quality, and reduce transmitter power requirements. In this paper, we investigate the capacity improvement that can potentially be achieved using adaptive arrays at the base station of an AMPS system. For the analysis, we use two types of spatial filters at the base station: an ideal and flat-top beamformer. An ideal beamformer has a flat main lobe and no side lobes, while a flat-top beamformer has flat main and side lobes. Analysis includes calculation of outage probability when a beamformer is used at the base station, and then we calculate the capacity increase that can be offered by practical antenna arrays. In this paper, we show that spatial-division multiple access (SDMA), i.e., all the users in a cell occupying the same frequency, is impossible to achieve in an AMPS system. A cell-reuse factor of four can be easily achieved with a five-element uniform linear array (ULA) with /spl lambda//2 spacings, but to achieve a reuse factor of three, a ULA with eight elements is required.