# Multi-task Learning for Radar Signal Characterisation

arXiv: https://arxiv.org/abs/2306.13105 · HTML: https://mlsanity.com/paper/2306.13105/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Deep Architectures for Modulation Recognition](https://mlsanity.com/paper/1703.09197/) (2017)
- [Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification](https://mlsanity.com/paper/2101.10254/) (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)
- [Unsupervised Representation Learning of Structured Radio Communication Signals](https://mlsanity.com/paper/1604.07078/) (2016)

## Built on by (most cited first, 10 of 10)

- [Multi-stage Learning for Radar Pulse Activity Segmentation](https://mlsanity.com/paper/2312.09489/) (2023)
- [EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding](https://mlsanity.com/paper/2508.18785/) (2025)
- [Few-Shot Radar Signal Recognition through Self-Supervised Learning and Radio Frequency Domain Adaptation](https://mlsanity.com/paper/2501.03461/) (2025)
- [AIMC-Spec: A Benchmark Dataset for Automatic Intrapulse Modulation Classification under Variable Noise Conditions](https://mlsanity.com/paper/2601.08265/) (2026)
- [The Turing Synthetic Radar Dataset: A dataset for pulse deinterleaving](https://mlsanity.com/paper/2602.03856/) (2026)
- [MERLIN: Building Low-SNR Robust Multimodal LLMs for Electromagnetic Signals](https://mlsanity.com/paper/2603.08174/) (2026)
- [ELC: Evidential Lifelong Classifier for Uncertainty Aware Radar Pulse Classification](https://mlsanity.com/paper/2604.06958/) (2026)
- [An Algorithm for Fixed Budget Best Arm Identification with Combinatorial Exploration](https://mlsanity.com/paper/2502.01429/) (2025)
- [Position-Aware Self-supervised Representation Learning for Cross-mode Radar Signal Recognition](https://mlsanity.com/paper/2602.11196/) (2026)
- [Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification](https://mlsanity.com/paper/2610.18404/) (2026)

## Abstract (arXiv)

Radio signal recognition is a crucial task in both civilian and military applications, as accurate and timely identification of unknown signals is an essential part of spectrum management and electronic warfare. The majority of research in this field has focused on applying deep learning for modulation classification, leaving the task of signal characterisation as an understudied area. This paper addresses this gap by presenting an approach for tackling radar signal classification and characterisation as a multi-task learning (MTL) problem. We propose the IQ Signal Transformer (IQST) among several reference architectures that allow for simultaneous optimisation of multiple regression and classification tasks. We demonstrate the performance of our proposed MTL model on a synthetic radar dataset, while also providing a first-of-its-kind benchmark for radar signal characterisation.