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 Experiment Video

Updated: Jun 27, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Direction of arrival estimation with neural networks via test time self-supervised optimization and array manifold

Yining Liu1,2,3, Xiaojun Zhang4, Ziqiang Luo1,2,3

  • 1School of Ocean Engineering and Technology, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China.

The Journal of the Acoustical Society of America
|May 11, 2026
PubMed
Summary

Related Concept Videos

Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

You might also read

Related Articles

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

Sort by
Same author

long-range underwater acoustic hyperbolic frequency modulation signal denoising based on lightweight neural networka).

The Journal of the Acoustical Society of America·2026
Same author

Single-snapshot complex approximate message passing method for active sonar imaging in shallow water.

The Journal of the Acoustical Society of America·2026
Same author

Advancing photoacoustic microscopy via single-source triple-beam interference excitation.

Optics letters·2025
Same author

An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman Filter.

Sensors (Basel, Switzerland)·2025
Same author

An intrusive Gibbs sampling method for implementing the nonsynchronous measurements of microphone array.

The Journal of the Acoustical Society of America·2025
Same author

Mode-informed complex-valued neural processes for matched field processing.

The Journal of the Acoustical Society of America·2025

This study introduces Neural-MVDR, a self-supervised method for robust direction-of-arrival estimation. It improves adaptive beamforming by adapting in real-time without pretraining, enhancing accuracy in challenging conditions.

Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Machine Learning for Signal Processing

Background:

  • Adaptive beamformers like Minimum Variance Distortionless Response (MVDR) are crucial for signal processing but sensitive to errors.
  • Mismatches in the sample covariance matrix (SCM) and array steering vectors degrade performance.
  • Existing robust MVDR methods often rely on specific assumptions or pre-trained models.

Purpose of the Study:

  • To develop a robust direction-of-arrival (DOA) estimation framework using an adaptive beamformer.
  • To introduce a closed-loop Neural-MVDR system that enforces distortionless constraints with a parameterized array model.
  • To enable self-supervised, per-frame adaptation at test time, eliminating the need for offline pretraining.

Main Methods:

Related Experiment Videos

Last Updated: Jun 27, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Proposes a closed-loop Neural-MVDR framework for DOA estimation.
  • Enforces a distortionless constraint aligned with a physically parameterized array model.
  • Employs per-frame, self-supervised adaptation, alternating between steering-vector refinement, robust SCM reconstruction via a neural module, and analytical MVDR spectral estimation.

    • Main Results:

    • Demonstrates improved robustness in DOA estimation compared to conventional beamformers and MVDR variants.
    • Achieves enhanced performance on both synthetic data and real-world SWellEx-96 S5 event data.
    • Outperforms existing robust MVDR techniques including eigenspace suppression, covariance matrix tapering, and shrinkage methods.

    Conclusions:

    • The proposed Neural-MVDR framework offers a robust and adaptive solution for DOA estimation.
    • Self-supervised, real-time adaptation significantly enhances performance without requiring external labeled datasets.
    • This approach provides a promising direction for improving adaptive beamforming in practical scenarios with model uncertainties.