Gridless sparse covariance-based beamforming via alternating projections including co-prime arrays

Area of Science:

• Signal Processing
• Array Signal Processing
• Electromagnetics

Background:

• Direction-of-Arrival (DOA) estimation is crucial for applications like radar and sonar.
• Existing sparse methods often rely on discretized grids, limiting resolution.
• Gridless methods offer higher accuracy but can be computationally intensive.

Purpose of the Study:

• To develop a novel gridless sparse processing algorithm for DOA estimation.
• To improve the speed and accuracy of existing gridless DOA estimation techniques.
• To address DOA estimation challenges with non-uniformly configured linear arrays.

Main Methods:

• The proposed method employs alternating projections to reconstruct Toeplitz-structured low-rank matrices.
• It solves a gridless sparse covariance-based estimation problem.
• The algorithm is designed to handle non-uniformly configured linear arrays.

Main Results:

• The method achieves high-resolution and reliable DOA estimation.
• Performance improvements are demonstrated for single-snapshot data, coherent sources, and non-uniform arrays.
• Simulations show superior performance compared to existing gridless sparse DOA estimators.

Conclusions:

• The developed gridless sparse processing method offers significant advantages in speed and accuracy.
• It provides robust DOA estimation even under challenging conditions.
• The approach is validated through simulations and real-world ocean experiment data.