# LSTM Framework for Classification of Radar and Communications Signals

arXiv: https://arxiv.org/abs/2305.03192 · HTML: https://mlsanity.com/paper/2305.03192/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2023.

## Built on by (most cited first, 3 of 3)

- [Few-Shot Radar Signal Recognition through Self-Supervised Learning and Radio Frequency Domain Adaptation](https://mlsanity.com/paper/2501.03461/) (2025)
- [The Turing Synthetic Radar Dataset: A dataset for pulse deinterleaving](https://mlsanity.com/paper/2602.03856/) (2026)
- [End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing](https://mlsanity.com/paper/2606.24075/) (2026)

## Abstract (arXiv)

Although radar and communications signal classification are usually treated separately, they share similar characteristics, and methods applied in one domain can be potentially applied in the other. We propose a simple and unified scheme for the classification of radar and communications signals using Long Short-Term Memory (LSTM) neural networks. This proposal provides an improvement of the state of the art on radar signals where LSTM models are starting to be applied within schemes of higher complexity. To date, there is no standard public dataset for radar signals. Therefore, we propose DeepRadar2022, a radar dataset used in our systematic evaluations that is available publicly and will facilitate a standard comparison between methods.