Robust Specific Emitter Identification With Sample Selection and Regularization Under Label Noise

doi:10.1109/JIOT.2024.3453297

Robust Specific Emitter Identification With Sample Selection and Regularization Under Label Noise

2024-12-15

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DOI：10.1109/JIOT.2024.3453297

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Deep learning (DL), renowned for its superior feature extraction capabilities, has remarkably succeeded in specific emitter identification (SEI), especially when supported by high-quality labeled data. However, obtaining accurate signal labels in complex electromagnetic environments is challenging, and manual labeling is prone to errors, underscoring the need for robust DL-based SEI methods that can handle label noise. These methods prevent neural networks from overfitting noisy labels, thereby boosting identification performance. Yet, research in this area is still limited. Our study introduces a robust label-noise SEI approach and the sample selection and regularization (SSR) method. This involves a two-stage adaptive sample selection (ASS) driven by confidence learning. The first stage entails coarse-grained separation of true and false labels through direct deep neural network (DNN) training. In the second stage, semi-supervised learning (SSL) utilizes a regularization-inspired loss, incorporating label smoothing regularization (LSR) and entropy minimization (EM), for fine-grained sample selection. The DNN is ultimately trained on precisely selected true-labeled samples. Comparative experiments on the automatic dependent surveillance-broadcast (ADS-B) and Wi-Fi data sets demonstrate that our SSR method outperforms the existing methods in identification accuracy, particularly at a 20% label-noise ratio, achieving 86.00% accuracy with the ADS-B data set, and 99.38% with the Wi-Fi data set. The code is available at: https://github.com/sleepeach/SSR-SEI.

Keywords:

Noise measurement

Feature extraction

Training

Robustness

Noise

Artificial neural networks

Entropy

Confidence learning

deep learning (DL)

noisy label

regularization

sample selection

semi-supervised learning (SSL)

specific emitter identification (SEI)

Robust Specific Emitter Identification With Sample Selection and Regularization Under Label Noise

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