Subspace Complexity Reduction in Direction-of-Arrival Estimation via the RASA Algorithm.

Area of Science:

• Array signal processing
• Subspace methods
• Computational electromagnetics

Background:

• Increasing data complexity necessitates advanced subspace processing for accurate Direction of Arrival (DoA) estimation.
• Traditional DoA estimation methods struggle with source coherence, limited snapshots, and low Signal-to-Noise Ratio (SNR).

Purpose of the Study:

• To develop a selective subspace refinement technique for enhanced dimensionality reduction in DoA estimation.
• To improve accuracy and reduce computational complexity in high-resolution DoA estimation.

Main Methods:

• A novel dimensionality reduction technique using selective subspace refinement.
• Minimizing the projection subspace by selecting least correlated noise subspace columns based on the ℓ2-norm.
• Adaptive selection of eigenvectors to maintain angular resolution and estimation accuracy.

Main Results:

• Achieved up to 75% reduction in computational complexity.
• Enhanced robustness, numerical stability, and orthogonality of the pseudo-spectrum.
• Demonstrated superior accuracy and execution time compared to traditional DoA estimation methods.

Conclusions:

• The proposed correlation-aware subspace design offers a scalable and effective solution for high-resolution DoA estimation.
• The method excels in data-intensive signal environments with challenging conditions like low SNR and source coherence.
• Experimental results validate the method's performance improvements over conventional approaches.