利用Salsa快速生成代表性合成数据集以训练含电磁耦合数据增强的ATR模型
Fast Generation of Representative Synthetic Dataset with Salsa to Train ATR Models with Electromagnetic Couplings Data-Augmentation
- Scalian DS
- DGA Maîtrise de l’Information（法国国防采购局信息控制中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出Salsa模拟器可快速生成高代表性合成SAR数据集，结合ADASCA方法训练的ATR模型在MSTAR数据集上达87%准确率，还揭示电磁耦合对ATR模型性能的关键影响。
AI中文摘要：
本研究聚焦于利用模拟合成孔径雷达（SAR）图像训练自动目标识别（ATR）模型，以规避真实测量数据的匮乏问题。为获得鲁棒且通用的ATR模型，模拟器需生成涵盖真实测量所有变异性的大规模数据集，因此需在执行速度、计算资源消耗与物理代表性间取得良好平衡。本研究证实Salsa模拟器可解决该问题：计算性能测试显示，Salsa使用单块Nvidia GeForce RTX 4090 GPU可在10分钟内生成21600张合成图像；结合ADASCA深度学习方法，这些数据足以训练ATR模型，在MSTAR公开数据集上达到86%的准确率。为展示Salsa为ATR模型训练带来的新可能，本研究利用该模拟器开展目标与其周边环境间电磁（EM）耦合的研究，证实若训练数据未考虑地面变异性引发的EM耦合变异性，将导致ATR模型性能显著下降，准确率降幅超4%；同时表明Salsa可在4小时内（使用同一块GPU）及时生成含大量耦合类型的648000张图像的大规模数据集，使ATR模型对EM耦合变异性具备鲁棒性，最终在MSTAR数据集上达到87%的准确率。
英文摘要：
This work focuses on training Automatic Target Recognition (ATR) models using simulated Synthetic Aperture Radar (SAR) images to circumvent the lack of real measurements. To obtain robust and versatile ATR models, simulation needs to generate massive datasets that encompass all the variability found in real measurements. Thus, we need a simulator that finds a good tradeoff between execution speed, computational resource consumption, and physical representativeness. In this work, we demonstrate that the Salsa simulator addresses this issue. We ran computing performance tests to show that Salsa can generate 21,600 synthetic images in less than 10 minutes using a single Nvidia GeForce RTX 4090 GPU. Using our ADASCA Deep Learning approach, we demonstrate that these data are sufficiently representative to train ATR models and reach state-of-the-art results on the MSTAR public dataset with an accuracy of 86 %. To illustrate how Salsa unlocks new possibilities to train ATR models, we use the simulator to conduct a study on Electromagnetic (EM) couplings between the targets and their immediate environment. We demonstrate that, if not accounted for in the training dataset, the variability of the EM couplings induced by the variability of the ground surfaces can significantly degrade the performance of ATR models, with an accuracy decrease of more than 4 %. We also show that Salsa can generate in a timely manner (i.e., in less than 4 hours using the same GPU as previously) a massive dataset of 648,000 images with a large variety of couplings to make the ATR models robust to EM coupling variations. Our ATR models can then achieve an accuracy of 87 % on the MSTAR dataset.
