Radar target recognition using graph-based features and GNN
Ismail I. Jouny · 2025
This paper develops a graph model for the impulse response (or high range resolution profile) of a radar target. This graph model is based on the number of scatterers, distance between scatterers, sequence and degree of dispersion of each scatterer. This graph model is then fed into a graph neural network for target recognition. The paper examines the performance of this graph-based target recognition system using real commercial aircraft backscatter (as recorded in a compact range). Issues of azimuth ambiguity, azimuth mismatch, missing features, and noise contamination are addressed in terms of the impact on target recognition performance. The paper compares the performance of a graph convolutional neural network with that of a traditional convolutional neural network.