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

## TL;DR

This paper introduces a new method for improving radar signal processing in airborne MIMO radar when there are limited data samples.

## Contribution

A novel covariance matrix estimation method using eigenvalue adjustment for MIMO-STAP under limited snapshots.

## Key findings

- The proposed method adjusts eigenvalues to enhance clutter-plus-noise covariance matrix estimation.
- EA-MIMO-STAP shows superior performance and robustness in experiments.
- The method reliably works with limited snapshots, addressing a practical challenge in airborne radar.

## Abstract

The covariance matrix performs a vital role for space-time adaptive processing (STAP) in airborne multiple-input multiple-output (MIMO) radar. As is known, the clutter-plus-noise covariance matrix (CPNCM), reflecting the statistical characteristics of radar echo, is a key component for MIMO-STAP. Commonly, an ideal CPNCM is impossible to obtain, and it must be estimated with sufficient snapshots. According to the RMB rule, MIMO-STAP requires many snapshots since MIMO radar has a high degree-of-freedom (DoF) due to its orthogonal transmit waveform. However, this is hard to satisfy in practice. This paper develops a novel covariance matrix estimation method under limited snapshots in airborne MIMO-STAP radar. Motivated by the random matrix theory, the proposed method enhances the CPNCM estimation by noise and clutter sample eigenvalues adjustment (EA). Concretely, the sample eigenvalues of noise are adjusted as noise power, and the ones of clutter are adjusted through minimizing the radar output power. Then, with the sample eigenvectors and adjusted sample eigenvalues, an effective CPNCM is formulated, and EA-MIMO-STAP is implemented reliably. Multiple experiments demonstrate that EA-MIMO-STAP has superior performance and robustness.