Null Broadening Robust Adaptive Beamforming Algorithm Based on Power Estimation

Area of Science:

• Signal Processing
• Adaptive Beamforming
• Array Signal Processing

Background:

• Traditional beamforming algorithms suffer performance degradation with rapid source movement and platform instability.
• Adaptive beamforming techniques are crucial for enhancing signal detection and interference rejection in dynamic environments.

Purpose of the Study:

• To propose a novel null broadening robust adaptive beamforming algorithm.
• To address the performance limitations of existing methods in scenarios with moving sources and unstable platforms.

Main Methods:

• Interference signal power estimation using characteristic subspace theory.
• Reconstruction of the interference covariance matrix (ICM) based on signal power and steering vector (SV) orthogonality.
• Implementation of null broadening by introducing virtual interference sources.

Main Results:

• Achieved deeper nulls and lower side lobes compared to conventional methods.
• Demonstrated higher tolerance to desired signal steering vector mismatch.
• Maintained low computational complexity.

Conclusions:

• The proposed null broadening robust adaptive beamforming algorithm significantly improves performance under challenging conditions.
• The algorithm exhibits enhanced robustness and effectiveness, validated through simulation results.