MSVFDT Features Based Classification Method for Aircraft with Low-resolution Radars

Aircraft-induced radar echo nonlinear modulation (from fuselage vibrations, attitude changes, and rotating parts) can be characterized via multifractal measures. This paper proposes an improved multi-scale variance fractal dimension trajectory (MSVFDT) algorithm, derived from variance fractal dimension trajectory (VFDT), to extract aircraft echo features. Combined with support vector machine (SVM), MSVFDT-based classification experiments on diverse aircraft echoes demonstrate effective target recognition with low computational cost. The method efficiently distinguishes aircraft types by quantifying multifractal signatures of structural and dynamic modulations, offering practical applicability in resource-constrained radar systems.