Eigenvalue Adjustment-Based STAP in Airborne MIMO Radar Under Limited Snapshots.

Area of Science:

• Radar Systems Engineering
• Signal Processing
• Random Matrix Theory

Background:

• The covariance matrix is crucial for airborne multiple-input multiple-output (MIMO) radar's space-time adaptive processing (STAP).
• Accurate estimation of the clutter-plus-noise covariance matrix (CPNCM) is essential for MIMO-STAP performance.
• Traditional methods require numerous snapshots, which are often impractical for airborne radar systems.

Purpose of the Study:

• To develop a novel covariance matrix estimation method for airborne MIMO-STAP radar under limited snapshot conditions.
• To enhance the estimation of the CPNCM by adjusting noise and clutter sample eigenvalues.
• To enable reliable implementation of MIMO-STAP with improved performance and robustness.

Main Methods:

• A novel covariance matrix estimation method inspired by random matrix theory.
• Eigenvalues adjustment (EA) technique applied to noise and clutter samples.
• Adjustment of noise eigenvalues to noise power and clutter eigenvalues by minimizing radar output power.

Main Results:

• An effective CPNCM is formulated using adjusted eigenvalues and sample eigenvectors.
• The proposed EA-MIMO-STAP method demonstrates superior performance.
• Experimental results confirm the robustness of the EA-MIMO-STAP approach.

Conclusions:

• The developed eigenvalue adjustment method significantly improves CPNCM estimation accuracy in limited snapshots.
• EA-MIMO-STAP offers a reliable and robust solution for airborne MIMO radar.
• The method addresses the practical challenge of insufficient snapshots in MIMO-STAP systems.