Target Classification with Low-resolution Surveillance Radars Based on Multifractal Features

The multifractal characteristics of return signals from aircraft targets in conventional radars offer a fine description of dynamic characteristics which induce the targets’ echo structure; therefore they can provide a new way for aircraft target classification and recognition with low-resolution surveillance radars. On basis of introducing the mathematical model of return signals from aircraft targets in conventional radars, the paper analyzes the multifractal characteristics of the return signals as well as the extraction method of their multifractal features by means of the multifractal analysis of measures, and puts forward a multifractal-feature-based classification method for three types of aircraft targets (including jet aircrafts, propeller aircrafts and helicopters) from the viewpoint of pattern classification. The analysis shows that the conventional radar return signals from the three types of aircraft targets have significantly different multifractal characteristics, and the defined characteristic parameters can be used as effective features for aircraft target classification and recognition. The results of classification experiments validate the proposed method.