Support Vector Data Description for Radar Target Detection

TL;DR

This paper explores the use of Support Vector Data Description (SVDD) and Deep SVDD as robust, covariance-estimation-free methods for radar target detection in cluttered environments, demonstrating their effectiveness through simulations.

Contribution

It introduces two novel SVDD-based algorithms for radar detection and adapts them as CFAR detectors to improve performance in heavy-tailed clutter conditions.

Findings

SVDD-based detectors outperform traditional covariance-based methods in clutter environments

Deep SVDD enhances detection robustness with deep learning techniques

Proposed algorithms show promising results on simulated radar data

Abstract

Classical radar detection techniques rely on adaptive detectors that estimate the noise covariance matrix from target-free secondary data. While effective in Gaussian environments, these methods degrade in the presence of clutter, which is better modeled by heavy-tailed distributions such as the Complex Elliptically Symmetric (CES) and Compound-Gaussian (CGD) families. Robust covariance estimators like M-estimators or Tyler's estimator address this issue, but still struggle when thermal noise combines with clutter. To overcome these challenges, we investigate the use of Support Vector Data Description (SVDD) and its deep extension, Deep SVDD, for target detection. These one-class learning methods avoid direct noise covariance estimation and are adapted here as CFAR detectors. We propose two novel SVDD-based detection algorithms and demonstrate their effectiveness on simulated radar data.