  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.
