Multi-task Learning for Radar Signal Characterisation (2306.13105v2)
Abstract: 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.
- Cognitive Electronic Warfare: An Artificial Intelligence Approach, Artech House, 2021.
- “Maximum-Likelihood Classification for Digital Amplitude-Phase Modulations,” IEEE Transactions on Communications, vol. 48, no. 2, pp. 189–193, 2000.
- “Adaptive Generation of Decision Functions for Classification of Digitally Modulated Signals,” in Proceedings of the IEEE 1988 National Aerospace and Electronics Conference. IEEE, 1988, pp. 1010–1014.
- “Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels,” IEEE Transactions on Wireless Communications, vol. 7, no. 8, pp. 3098–3105, Aug. 2008.
- “Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio,” in 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. IEEE, 2007, pp. 212–215.
- “Expert RF Feature Extraction to Win the Army RCO AI Signal Classification Challenge,” in Proceedings of the 18th Python in Science Conference, 2019, pp. 8–14.
- “Deep and Ensemble Learning to Win the Army RCO AI Signal Classification Challenge,” in Proceedings of the 18th Python in Science Conference, 2019, pp. 21–26.
- “Convolutional radio modulation recognition networks,” in Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings 17. 2016, pp. 213–226, Springer.
- “Unsupervised representation learning of structured radio communication signals,” in 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE). 2016, pp. 1–5, IEEE.
- “Deep architectures for modulation recognition,” in 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). 2017, pp. 1–6, IEEE.
- “Over-the-Air Deep Learning Based Radio Signal Classification,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 168–179, 2018, IEEE.
- “Automatic Modulation Classification: A Deep Architecture Survey,” IEEE Access, vol. 9, pp. 142950–142971, 2021.
- “Attention Is All You need,” Advances in Neural Information Processing Systems, vol. 30, 2017.
- “AST: Audio Spectrogram Transformer,” in Proceedings of Interspeech 2021, 2021, pp. 571–575.
- “Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification,” in ICC 2021-IEEE International Conference on Communications. 2021, pp. 1–7, IEEE.
- “Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression,” Physical Communication, vol. 54, pp. 101793, 2022, Elsevier.
- Radar Signals, John Wiley & Sons, 2004.
- Richard A. Caruana, “Multitask Learning: A Knowledge-Based Source of Inductive Bias,” in Proceedings of the Tenth International Conference on Machine Learning. 1993, pp. 41–48, Citeseer.
- Jonathan Baxter, “A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling,” Machine learning, vol. 28, pp. 7–39, 1997.
- “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” ICLR 2021 - Ninth International Conference on Learning Representations, 2021.