# Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

arXiv: https://arxiv.org/abs/2207.06592 · HTML: https://mlsanity.com/paper/2207.06592/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 57 references that are in the map.
- First posted on arXiv 14 Jul 2022.

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

- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [How to train your MAML](https://mlsanity.com/paper/1810.09502/) (2018)
- [Triplet-Center Loss for Multi-View 3D Object Retrieval](https://mlsanity.com/paper/1803.06189/) (2018)
- [Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks](https://mlsanity.com/paper/1907.03289/) (2019)
- [Radio Frequency Fingerprint Identification for LoRa Using Spectrogram and CNN](https://mlsanity.com/paper/2101.01668/) (2020)
- [Physical Layer Security in UAV Systems: Challenges and Opportunities](https://mlsanity.com/paper/1909.06785/) (2019)

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

- [A Generic Machine Learning Framework for Radio Frequency Fingerprinting](https://mlsanity.com/paper/2510.09775/) (2025)
- [HSCP: A Two-Stage Spectral Clustering Framework for Resource-Constrained UAV Identification](https://mlsanity.com/paper/2512.08983/) (2025)
- [Few-Shot Specific Emitter Identification via Integrated Complex Variational Mode Decomposition and Spatial Attention Transfer](https://mlsanity.com/paper/2512.16786/) (2025)

## Abstract (arXiv)

Specific emitter identification (SEI) is a highly potential technology for physical layer authentication that is one of the most critical supplement for the upper-layer authentication. SEI is based on radio frequency (RF) features from circuit difference, rather than cryptography. These features are inherent characteristic of hardware circuits, which difficult to counterfeit. Recently, various deep learning (DL)-based conventional SEI methods have been proposed, and achieved advanced performances. However, these methods are proposed for close-set scenarios with massive RF signal samples for training, and they generally have poor performance under the condition of limited training samples. Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL). Specifically, the proposed method consists of feature embedding and classification. The former is based on metric learning with complex-valued convolutional neural network (CVCNN) for extracting discriminative features with compact intra-category distance and separable inter-category distance, while the latter is realized by an ensemble classifier. Simulation results show that if the number of samples per category is more than 5, the average accuracy of our proposed method is higher than 98%. Moreover, feature visualization demonstrates the advantages of our proposed method in both discriminability and generalization. The codes of this paper can be downloaded from GitHub(https://github.com/BeechburgPieStar/Few-Shot-Specific-Emitter-Identification-via-Deep-Metric-Ensemble-Learning)