Aircraft Target Classification Based on Multifractal Features of Aircraft Echoes under Suppression of Airframe Section

As a kind of complicated targets, the nonrigid vibration of aircraft, their attitude change, and the rotation of their rotating parts will induce complicated nonlinear modulation on their echoes from low-resolution radars. If one performs the multifractal analysis of measures on an aircraft echo, it may offer a refined description of the echo nonlinear modulation characteristics. However, the airframe translational components that occupy the main body of the echo and have little effect on target classification and recognition will have a very adverse effect on the extraction of such features. In the case of Doppler compensation for the echo, the notch algorithm is proposed to suppress the airframe translational section. On this basis, the multifractal measure analysis method is used to analyze and extract the echo characteristics, and the target classification experiments of various types of aircraft echoes are carried out based on the proposed multifractal features combined with support vector machine (SVM). The experimental results show that the echo multifractal features under suppression of airframe section can be used to classify different types of aircraft targets effectively, and the correct classification rate has been significantly improved compared with the features extracted without airframe section compensation.