A Novel Fast Iterative STAP Method with a Coprime Sampling Structure.

Area of Science:

• Signal Processing
• Radar Systems
• Adaptive Filtering

Background:

• Coprime sampling in space-time adaptive processing (STAP) offers enhanced clutter suppression and lower hardware costs compared to uniform linear arrays.
• Algorithm performance in practical STAP applications is frequently constrained by the availability of sufficient training samples.

Purpose of the Study:

• To address the limitation of training sample scarcity in coprime STAP.
• To propose a novel fast iterative algorithm for improved performance in coprime STAP systems.

Main Methods:

• A fast iterative coprime STAP algorithm is proposed, utilizing truncated kernel norm minimization (TKNM).
• A virtual clutter covariance matrix (CCM) is established, incorporating TKNM regularization to ensure low rank.
• The non-convex optimization problem is converted into a convex one, solved using an alternating direction method.

Main Results:

• The proposed TKNM-based algorithm effectively handles limited training samples in coprime STAP.
• Simulation experiments confirm the algorithm's ability to achieve accurate clutter suppression.
• The method demonstrates superior performance compared to existing approaches under sample-limited conditions.

Conclusions:

• The fast iterative coprime STAP algorithm based on TKNM provides an effective solution for clutter suppression with limited training data.
• The established virtual CCM and convex optimization approach enhance the robustness and accuracy of STAP systems.
• This method offers a practical advancement for radar systems employing coprime sampling structures.