Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Network
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Abstract (arXiv)
This paper proposes a knowledge-and-data-driven graph neural network-based collaboration learning model for reliable aircraft recognition in a heterogeneous radar network. The aircraft recognizability analysis shows that: (1) the semantic feature of an aircraft is motion patterns driven by the kinetic characteristics, and (2) the grammatical features contained in the radar cross-section (RCS) signals present spatial-temporal-frequency (STF) diversity decided by both the electromagnetic radiation shape and motion pattern of the aircraft. Then a STF graph attention convolutional network (STFGACN) is developed to distill semantic features from the RCS signals received by the heterogeneous radar network. Extensive experiment results verify that the STFGACN outperforms the baseline methods in terms of detection accuracy, and ablation experiments are carried out to further show that the expansion of the information dimension can gain considerable benefits to perform robustly in the low signal-to-noise ratio region.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 15 Apr 2022.
Builds on
11 of 40 references.
References · 11
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
- 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
- 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
- 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
- 2018Graph Convolutional Networks for Text Classificationcited by 345
- 2019Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecastingcited by 69
- 2020Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networkscited by 64
- 2020Tensor Graph Convolutional Networks for Text Classificationcited by 49
- 2020Variational Temporal Deep Generative Model for Radar HRRP Target Recognitioncited by 2
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