ELC：用于不确定感知的雷达脉冲分类终身分类器
ELC: Evidential Lifelong Classifier for Uncertainty Aware Radar Pulse Classification
AI总结：
本文提出ELC，通过证据理论建模epistemic不确定性，结合终身学习解决雷达脉冲分类中的不确定性问题，实验显示其在低信噪比条件下提升召回率46%。
摘要：
可靠的雷达脉冲分类对于电磁战中的态势感知和决策支持至关重要。深度神经网络在雷达脉冲和射频发射机识别中表现出色；然而，它们难以高效学习新脉冲且缺乏表达预测信心的机制。本文将不确定性量化与终身学习相结合，提出证据终身分类器（ELC），通过证据理论建模epistemic不确定性。ELC与基于香农熵的贝叶斯终身分类器（BLC）均集成学习-修剪-共享机制，以实现新脉冲的持续学习和基于不确定性的选择性预测。ELC和BLC在两个合成雷达和三个射频指纹数据集上进行评估。基于证据不确定性的选择性预测在-20 dB信噪比的合成雷达脉冲数据集上将召回率提高至46%，显示出其在低信噪比条件下识别不可靠预测的有效性。这些发现表明，证据不确定性在信心与正确性之间具有强相关性，通过允许表达无知提高了ELC的可信度。
英文摘要：
Reliable radar pulse classification is essential in Electromagnetic Warfare for situational awareness and decision support. Deep Neural Networks have shown strong performance in radar pulse and RF emitter recognition; however, on their own they struggle to efficiently learn new pulses and lack mechanisms for expressing predictive confidence. This paper integrates Uncertainty Quantification with Lifelong Learning to address both challenges. The proposed approach is an Evidential Lifelong Classifier (ELC), which models epistemic uncertainty using evidence theory. ELC is evaluated against a Bayesian Lifelong Classifier (BLC), which quantifies uncertainty through Shannon entropy. Both integrate Learn-Prune-Share to enable continual learning of new pulses and uncertainty-based selective prediction to reject unreliable predictions. ELC and BLC are evaluated on 2 synthetic radar and 3 RF fingerprinting datasets. Selective prediction based on evidential uncertainty improves recall by up to 46% at -20 dB SNR on synthetic radar pulse datasets, highlighting its effectiveness at identifying unreliable predictions in low-SNR conditions compared to BLC. These findings demonstrate that evidential uncertainty offers a strong correlation between confidence and correctness, improving the trustworthiness of ELC by allowing it to express ignorance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
