用模型集成提升合成雷达数据的实用性，自动拒绝未知目标。
The Comparability of Model Fusion to Measured Data in Confuser Rejection
- 通过集成多个合成数据训练的模型，增强对真实场景的适应性。
- 在未见目标上实现高拒识率，有效避免误分类。
- 适合雷达目标识别中数据稀缺、未知目标多的场景。
大规模深度学习网络的建模与训练面临数据收集难题，现有数据集无法涵盖实际应用中的所有细微差异。合成孔径雷达（SAR）数据采集成本高昂，难以覆盖多样目标与工作条件。为此，研究者利用射击与弹跳射线法开发模拟器，基于三维模型生成合成SAR数据。然而，合成数据与实测数据存在偏差，仅用合成数据训练的模型在真实环境中表现受限。本文提出以计算资源替代高质量实测数据，通过集成多个在合成数据上训练的模型，提升泛化能力。由于合成数据不完整，无法预知真实环境中的所有目标，因此需引入混淆物拒识机制，使模型能识别并拒绝未训练过的未知目标，仅对已知类别进行分类。
原文摘要 · Abstract (English)
Data collection has always been a major issue in the modeling and training of large deep learning networks, as no dataset can account for every slight deviation we might see in live usage. Collecting samples can be especially costly for Synthetic Aperture Radar (SAR), limiting the amount of unique targets and operating conditions we are able to observe from. To counter this lack of data, simulators have been developed utilizing the shooting and bouncing ray method to allow for the generation of synthetic SAR data on 3D models. While effective, the synthetically generated data does not perfectly correlate to the measured data leading to issues when training models solely on synthetic data. We aim to use computational power as a substitution for this lack of quality measured data, by ensembling many models trained on synthetic data. Synthetic data is also not complete, as we do not know what targets might be present in a live environment. Therefore we need to have our ensembling techniques account for these unknown targets by applying confuser rejection in which our models will reject unknown targets it is presented with, and only classify those it has been trained on.
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