# ELC: Evidential Lifelong Classifier for Uncertainty Aware Radar Pulse Classification

arXiv: https://arxiv.org/abs/2604.06958 · HTML: https://mlsanity.com/paper/2604.06958/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers](https://mlsanity.com/paper/1804.03294/) (2018)
- [Optimal strategies for reject option classifiers](https://mlsanity.com/paper/2101.12523/) (2021)
- [Learn-Prune-Share for Lifelong Learning](https://mlsanity.com/paper/2012.06956/) (2020)
- [An evidential classifier based on Dempster-Shafer theory and deep learning](https://mlsanity.com/paper/2103.13549/) (2021)
- [Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression](https://mlsanity.com/paper/2203.00517/) (2022)
- [Multi-task Learning for Radar Signal Characterisation](https://mlsanity.com/paper/2306.13105/) (2023)
- [RadYOLOLet: Radar Detection and Parameter Estimation Using YOLO and WaveLet](https://mlsanity.com/paper/2309.12094/) (2023)
- [Few-Shot Radar Signal Recognition through Self-Supervised Learning and Radio Frequency Domain Adaptation](https://mlsanity.com/paper/2501.03461/) (2025)

## Built on by (most cited first, 1 of 1)

- [Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification](https://mlsanity.com/paper/2610.18404/) (2026)

## Abstract (arXiv)

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.