Grid-less wideband direction of arrival estimation based on variational Bayesian inference.

Area of Science:

• Signal Processing
• Array Signal Processing
• Computational Electromagnetics

Background:

• Wideband direction of arrival (DOA) estimation is crucial in various applications.
• Existing grid-less sparse techniques offer improved performance over traditional methods.
• Current grid-less methods often require manual parameter tuning and exhibit high computational complexity, limiting their practical use.

Purpose of the Study:

• To propose a novel grid-less wideband DOA estimation method.
• To address the limitations of manual parameter tuning and high computational complexity in existing methods.
• To achieve accurate DOA estimation with reduced computational load.

Main Methods:

• A grid-less wideband DOA estimation approach based on variational Bayesian inference.
• Approximation of the posterior probability density function of DOA using variational Bayesian inference.
• Implementation of a method that bypasses manual parameter adjustment.

Main Results:

• The proposed method achieves accurate DOA estimation without manual parameter tuning.
• It demonstrates significantly lower computational complexity compared to existing grid-less techniques.
• Numerical simulations and real data processing confirm superior DOA estimation accuracy and faster computation.

Conclusions:

• The variational Bayesian inference-based method provides an effective solution for grid-less wideband DOA estimation.
• It enhances practicality by eliminating the need for parameter tuning and reducing computational demands.
• The method offers a competitive alternative with improved accuracy and speed.