Radar Emitter Identification with Multi-View Adaptive Fusion Network (MAFN)

Radar emitter identification (REI) aims to extract the fingerprint of an emitter and determine the individual to which it belongs. Although many methods have used deep neural networks (DNNs) for an end-to-end REI, most of them only focus on a single view of signals, such as spectrogram, bi-spectrum,...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-04, Vol.15 (7), p.1762
Main Authors: Yang, Shuyuan, Peng, Tongqing, Liu, Huiling, Yang, Chen, Feng, Zhixi, Wang, Min
Format: Article
Language:English
Subjects:
adaptive fusion
Ambiguity
Analysis
Artificial neural networks
Classification
Decision theory
Design specifications
Dirichlet problem
Discriminant analysis
Electromagnetism
Emitters
evidence theory
Feature extraction
multi-view features
Neural networks
Radar
radar emitter identification
Radar systems
Signal processing
Waveforms
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Summary:Radar emitter identification (REI) aims to extract the fingerprint of an emitter and determine the individual to which it belongs. Although many methods have used deep neural networks (DNNs) for an end-to-end REI, most of them only focus on a single view of signals, such as spectrogram, bi-spectrum, signal waveforms, and so on. When the electromagnetic environment varies, the performance of DNN will be significantly degraded. In this paper, a multi-view adaptive fusion network (MAFN) is proposed by simultaneously exploring the signal waveform and ambiguity function (AF). First, the original waveform and ambiguity function of the radar signals are used separately for feature extraction. Then, a multi-scale feature-level fusion module is constructed for the fusion of multi-view features from waveforms and AF, via the Atrous Spatial Pyramid Pooling (ASPP) structure. Next, the class probability is modeled as Dirichlet distribution to perform adaptive decision-level fusion via evidence theory. Extensive experiments are conducted on two datasets, and the results show that the proposed MAFN can achieve accurate classification of radar emitters and is more robust than its counterparts.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15071762