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

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CAWE-ACNN Algorithm for Coprime Sensor Array Adaptive Beamforming.

Fulai Liu^1,2, Wu Zhou³, Dongbao Qin³

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

Sensors (Basel, Switzerland)

|September 14, 2024

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 network coprime sensor array robust adaptive beamforming weight vector estimation

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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.