Transfer Learning-Based Specific Emitter Identification for ADS-B over Satellite System

Author:

Liu Mingqian1ORCID,Chai Yae1,Li Ming2,Wang Jiakun1,Zhao Nan3

Affiliation:

1. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China

2. Guangxi Intelligent Electromagnetic Spectrum Sensing and Control Research Center of Engineering Technology, Guilin Changhai Development Co., Ltd., Guilin 541001, China

3. School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China

Abstract

In future aviation surveillance, the demand for higher real-time updates for global flights can be met by deploying automatic dependent surveillance–broadcast (ADS-B) receivers on low Earth orbit satellites, capitalizing on their global coverage and terrain-independent capabilities for seamless monitoring. Specific emitter identification (SEI) leverages the distinctive features of ADS-B data. High data collection and annotation costs, along with limited dataset size, can lead to overfitting during training and low model recognition accuracy. Transfer learning, which does not require source and target domain data to share the same distribution, significantly reduces the sensitivity of traditional models to data volume and distribution. It can also address issues related to the incompleteness and inadequacy of communication emitter datasets. This paper proposes a distributed sensor system based on transfer learning to address the specific emitter identification. Firstly, signal fingerprint features are extracted using a bispectrum transform (BST) to train a convolutional neural network (CNN) preliminarily. Decision fusion is employed to tackle the challenges of the distributed system. Subsequently, a transfer learning strategy is employed, incorporating frozen model parameters, maximum mean discrepancy (MMD), and classification error measures to reduce the disparity between the target and source domains. A hyperbolic space module is introduced before the output layer to enhance the expressive capacity and data information extraction. After iterative training, the transfer learning model is obtained. Simulation results confirm that this method enhances model generalization, addresses the issue of slow convergence, and leads to improved training accuracy.

Funder

National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi

Key Research and Development Program of Shaanxi

Guangxi Key Research and Development Program

Publisher

MDPI AG

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