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

## TL;DR

This paper introduces a new STAP method using coprime sampling and optimization techniques to improve clutter suppression with fewer training samples.

## Contribution

A fast iterative coprime STAP algorithm using truncated kernel norm minimization is proposed to handle limited training samples.

## Key findings

- The proposed algorithm improves clutter suppression with fewer training samples.
- Truncated kernel norm regularization ensures low rank of the clutter covariance matrix.
- Simulation experiments confirm the algorithm's effectiveness and accuracy.

## Abstract

In space-time adaptive processing (STAP), the coprime sampling structure can obtain better clutter suppression capabilities at a lower hardware cost than the uniform linear sampling structure. However, in practical applications, the performance of the algorithm is often limited by the number of training samples. To solve this problem, this paper proposes a fast iterative coprime STAP algorithm based on truncated kernel norm minimization (TKNM). This method establishes a virtual clutter covariance matrix (CCM), introduces truncated kernel norm regularization technology to ensure the low rank of the CCM, and transforms the non-convex problem into a convex optimization problem. Finally, a fast iterative solution method based on the alternating direction method is presented. The effectiveness and accuracy of the proposed algorithm are verified through simulation experiments.