# Radar Pulse Deinterleaving with Transformer Based Deep Metric Learning

arXiv: https://arxiv.org/abs/2503.13476 · HTML: https://mlsanity.com/paper/2503.13476/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [A Radar Signal Deinterleaving Method Based on Semantic Segmentation with Neural Network](https://mlsanity.com/paper/2110.13706/) (2021)
- [A New Radar Signal Multiparameter-Based Deinterleaving Method](https://mlsanity.com/paper/2208.09786/) (2022)

## Built on by (most cited first, 2 of 2)

- [The Turing Synthetic Radar Dataset: A dataset for pulse deinterleaving](https://mlsanity.com/paper/2602.03856/) (2026)
- [Physics-guided deep metric learning with continuous time embeddings for open-world radar pulse de-interleaving](https://mlsanity.com/paper/2609.25057/) (2026)

## Abstract (arXiv)

When receiving radar pulses it is common for a recorded pulse train to contain pulses from many different emitters. The radar pulse deinterleaving problem is the task of separating out these pulses by the emitter from which they originated. Notably, the number of emitters in any particular recorded pulse train is considered unknown. In this paper, we define the problem and present metrics that can be used to measure model performance. We propose a metric learning approach to this problem using a transformer trained with the triplet loss on synthetic data. This model achieves strong results in comparison with other deep learning models with an adjusted mutual information score of 0.882.