用变分自编码器提升雷达在复杂噪声下的目标检测能力
Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders
- 用变分自编码器学习杂波与噪声分布，识别异常目标
- 在相关高斯、复合高斯杂波加白噪声下性能优于传统匹配滤波器
- 适合需要高鲁棒性的雷达系统，尤其在噪声复杂场景
本文提出一种基于变分自编码器（VAE）的新型雷达目标检测方法。得益于其学习复杂分布并识别异常样本的能力，该方法能有效区分雷达目标与多种噪声类型，包括相关高斯杂波、复合高斯杂波，以及叠加的加性白高斯热噪声。仿真结果表明，在复杂噪声条件下，所提VAE性能显著优于经典自适应检测器如匹配滤波器（Matched Filter）和归一化匹配滤波器（Normalized Matched Filter），凸显其在雷达应用中的鲁棒性与适应性。
原文摘要 · Abstract (English)
This paper presents a novel approach to radar target detection using Variational AutoEncoders (VAEs). Known for their ability to learn complex distributions and identify out-ofdistribution samples, the proposed VAE architecture effectively distinguishes radar targets from various noise types, including correlated Gaussian and compound Gaussian clutter, often combined with additive white Gaussian thermal noise. Simulation results demonstrate that the proposed VAE outperforms classical adaptive detectors such as the Matched Filter and the Normalized Matched Filter, especially in challenging noise conditions, highlighting its robustness and adaptability in radar applications.
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