Robust Null Broadening Beamforming Based on Covariance Matrix Reconstruction via Virtual Interference Sources

Area of Science:

• Signal Processing
• Adaptive Beamforming
• Array Signal Processing

Background:

• Rapid movement of jammers or antenna platforms degrades interference suppression.
• Interference signals moving out of the array beampattern nulls cause performance loss.

Purpose of the Study:

• To propose a novel null broadening beamforming method.
• To address performance degradation caused by moving interference signals and platform motion.

Main Methods:

• Reconstruction of the interference-plus-noise covariance (INC) matrix.
• Introduction of virtual interference sources to broaden nulls.
• Solving a convex optimization problem to obtain the desired signal steering vector.

Main Results:

• The proposed beamformer effectively broadens null width and deepens null depth.
• Demonstrated robust interference cancellation against fast-moving jammers and platform motion.
• Null depth is controllable via power parameters and knowledge of interference motion.

Conclusions:

• The novel beamforming method enhances robustness in dynamic environments.
• Effective for scenarios with fast-moving jammers or high-speed platform movement.
• Offers adjustable null depth for optimized interference suppression.