ABSTRACT

Non-cooperative aerial target classification is one of the most attractive but challenging tasks in radar remote sensing applications. Multiview high-range resolution profiles (HRRPs) of the aerial target contain abundant information and will benefit to classification. In this paper, a new aerial target classification method based on an end-to-end lightweight feature learning network (LFL-Net) with multiview HRRPs is proposed. The aerial target classification scenario using multiview HRRPs is first studied and modelled. Then a LFL-Net with multi-inputs and some distinct modules is designed to effectively learn the target classification information from the multiview HRRPs. Therefore, the proposed method can achieve accurate and reliable classification results under different signal-to-noise ratios (SNRs). Experimental results have shown the superiorities of the proposed non-cooperative aerial target classification method.

摘要

非合作空中目标分类是雷达遥感应用中最具吸引力但最具挑战性的任务之一。空中目标的多视角高分辨率剖面（HRRP）包含丰富的信息，有利于分类。在本文中，提出了一种基于具有多视图 HRRP 的端到端轻量级特征学习网络 (LFL-Net) 的新航空目标分类方法。首先研究和建模了使用多视图 HRRP 的空中目标分类场景。然后设计了一个具有多输入和一些不同模块的 LFL-Net，以有效地从多视图 HRRP 中学习目标分类信息。因此，所提出的方法可以在不同的信噪比（SNR）下获得准确可靠的分类结果。