Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mesh Analysis for AC Circuits01:12

Mesh Analysis for AC Circuits

In the domain of radio communication, the significance of impedance matching must be considered. It is crucial to ensure the efficient transmission of signals between radio transmitters and receivers. Achieving this balance involves using impedance-matching circuits, with one fundamental configuration comprising a resistor, capacitor, and inductor.
The process of harmonizing these impedances begins with a clear understanding of the input and output signals. Once these signals are known, the...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Polar Coordinates: Problem Solving01:27

Polar Coordinates: Problem Solving

Directional radiation patterns are central to antenna analysis, as they illustrate how signal strength varies with direction. These patterns are often modeled using polar plots, where the radial distance from the origin represents signal intensity at a given angle. A commonly used idealized form is the four-lobed rose curve, which captures the concept of directional beams in a simplified mathematical form.The four-lobed rose curve, described by r = cos⁡(2θ), features four symmetric lobes, each...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

First-in-human, phase 1 study of CM512, a TSLP/IL-13 bispecific antibody, in healthy volunteers: safety, tolerability, pharmacokinetics, pharmacodynamics, and immunogenicity.

Frontiers in immunology·2026
Same author

Geospatial genetic evolution and phenotypic plasticity in triple-negative breast cancer.

Genome medicine·2026
Same author

MRPL3 enhances mitochondrial function via the TOMM40/PGC-1α/TFAM axis to drive early recurrence in hepatocellular carcinoma.

Cell death & disease·2026
Same author

Annotation of bat IG H/L/K loci and analysis of the characteristics of bat BCR-CDR3 repertoires.

Frontiers in immunology·2026
Same author

Targeted extracellular vesicle-photoimmunotherapy remodels stromal-immune microenvironment to boost chemo-immunotherapy in preclinical models.

Cell reports. Medicine·2026
Same author

State-Driven Adaptive Deep-Unfolded PGA Algorithm for Hybrid Beamforming in MIMO-JCAS Systems.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jul 13, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.2K

CAWE-ACNN Algorithm for Coprime Sensor Array Adaptive Beamforming.

Fulai Liu1,2, Wu Zhou3, Dongbao Qin3

  • 1Laboratory of GNSS Anti-Jamming Technology, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces the coprime sensor array with weighted attention (CAWE) algorithm, an attention convolutional neural network (ACNN) for robust adaptive beamforming. The CAWE-ACNN algorithm enhances signal-to-interference-plus-noise ratio (SINR) performance and computational efficiency.

Keywords:
attention convolutional neural networkcoprime sensor arrayrobust adaptive beamformingweight vector estimation

More Related Videos

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
05:57

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

8.0K
Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays
05:04

Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

Published on: June 13, 2023

1.4K

Related Experiment Videos

Last Updated: Jul 13, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.2K
Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
05:57

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

8.0K
Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays
05:04

Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

Published on: June 13, 2023

1.4K

Area of Science:

  • Signal Processing
  • Machine Learning
  • Array Signal Processing

Background:

  • Adaptive beamforming is crucial for enhancing desired signals in sensor arrays.
  • Traditional methods struggle with complex interference environments and computational load.
  • Coprime sensor arrays offer advantages in degrees of freedom but require sophisticated processing.

Purpose of the Study:

  • To develop a robust adaptive beamforming algorithm for coprime sensor arrays.
  • To improve signal-to-interference-plus-noise ratio (SINR) performance using deep learning.
  • To achieve high computational efficiency in beamforming weight vector estimation.

Main Methods:

  • An attention convolutional neural network (ACNN) model is proposed, incorporating spatial and channel attention units.
  • A novel interference-plus-noise covariance matrix reconstruction algorithm is utilized for ACNN model training.
  • The ACNN is trained using sample signals from coprime sensor arrays to output beamforming weights.

Main Results:

  • The proposed CAWE-ACNN algorithm demonstrates significant improvements in SINR performance.
  • The algorithm achieves high computational efficiency compared to existing methods.
  • Simulation results validate the robustness and effectiveness of the ACNN-based beamforming.

Conclusions:

  • The CAWE-ACNN algorithm offers a robust and efficient solution for adaptive beamforming in coprime sensor arrays.
  • The integration of attention mechanisms in CNNs effectively enhances beamforming performance.
  • This deep learning approach provides a promising direction for future array signal processing applications.