Topic
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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TL;DR: A new robust adaptive beamforming algorithm with the coprime array that avoids the coarray aperture loss and enhances the accuracy of INCM reconstruction and outperforms the existing approaches in high SNR regions.
54 citations
Posted Content•
TL;DR: In this article, a deep neural network was proposed to perform high-quality ultrasound beamforming using very little training data, and applied to two distinct ultrasound acquisition strategies (plane wave and synthetic aperture) and demonstrated that high image quality can be maintained when measuring at low data-rates, using undersampled array designs.
Abstract: Biomedical imaging is unequivocally dependent on the ability to reconstruct interpretable and high-quality images from acquired sensor data. This reconstruction process is pivotal across many applications, spanning from magnetic resonance imaging to ultrasound imaging. While advanced data-adaptive reconstruction methods can recover much higher image quality than traditional approaches, their implementation often poses a high computational burden. In ultrasound imaging, this burden is significant, especially when striving for low-cost systems, and has motivated the development of high-resolution and high-contrast adaptive beamforming methods. Here we show that deep neural networks that adopt the algorithmic structure and constraints of adaptive signal processing techniques can efficiently learn to perform fast high-quality ultrasound beamforming using very little training data. We apply our technique to two distinct ultrasound acquisition strategies (plane wave, and synthetic aperture), and demonstrate that high image quality can be maintained when measuring at low data-rates, using undersampled array designs. Beyond biomedical imaging, we expect that the proposed deep~learning based adaptive processing framework can benefit a variety of array and signal processing applications, in particular when data-efficiency and robustness are of importance.
54 citations
TL;DR: The effectiveness of the model is demonstrated by designing a beamformer of several hundred weights that duplicates and interpolates the measured external ear response of a cat over broad ranges of frequency and direction.
Abstract: In this article, a beamformer is proposed as a functional model for the spatial and temporal filtering characteristics of the external ear. The output of a beamformer is a weighted combination of the data received at an array of spatially distributed sensors. The beamformer weights and array geometry determine its spatial and temporal filtering characteristics. A procedure is described for choosing the weights to minimize the mean‐squared error between the beamformer response and the measured response of the external ear. The effectiveness of the model is demonstrated by designing a beamformer of several hundred weights that duplicates and interpolates the measured external ear response of a cat over broad ranges of frequency and direction. A limited investigation of modeling performance as a function of array geometry is reported.
53 citations
Patent•
31 May 2000TL;DR: In this article, the adaptive array weights used at the transmitter are estimated in response to comparing characteristics of the received element pilot signal to characteristics of a received communication signal, in order to compare the two signals.
Abstract: A transmitter uses adaptive array weights to modify a gain and a phase of a communication signal to produce a plurality of element communication signals coupled to antenna elements in an adaptive array antenna. The communication signal is transmitted along with an element pilot signal that is coupled to one of the elements in the adaptive array antenna. In a receiver, the communication signal is received, and the element pilot signal is received. Thereafter, the adaptive array weights used at the transmitter are estimated in response to comparing characteristics of the received element pilot signal to characteristics of the received communication signal.
53 citations
TL;DR: In this article, a modified constant modulus algorithm (M-CMA) is proposed to give adaptability to microwave beamforming phased array antennas, which is able to give a gradient vector by a combination of analytical calculation and perturbation of the microwave beamform control voltage.
Abstract: This paper proposes a novel algorithm, called modified constant modulus algorithm (M-CMA), which is able to give adaptability to microwave beamforming phased array antennas. Since microwave analog beamformers basically require much fewer RF devices than digital beamformers, microwave analog beamformers based on M-CMA, that is, adaptive microwave beamformers, can be cheaply fabricated. Therefore, they are very suitable for mobile communication systems where both miniaturization and low cost are required for the mobile terminals. M-CMA obtains a gradient vector by a combination of analytical calculation and perturbation of the microwave beamforming control voltage. Though M-CMA is implemented with a digital signal processor, M-CMA controls the microwave analog beamformer by utilizing the gradient vector. The microwave analog beamformer based on M-CMA is analyzed to have the following characteristics: (1) the beamformer can point its main beam to the desired direction in additive white Gaussian noise (AWGN) channels; (2) although the beamformer may possibly fail in ill solutions in cochannel interference (CCI) channels, M-CMA can converge to the optimum solution when the desired direction is roughly a priori known.
53 citations