Low-resolution Airborne Radar Aircraft Target Classification

Abstract

Target classification is particularly important in modern and future airborne radar. Nowadays, most investigations of radar target classification are based on wideband radar signals, which have higher requirements for SNR and radar systems, and are sensitive to the angles. Modern airborne radars require narrowband tracking and target classification; hence, an algorithm based on the narrowband fractal features and the amplitude modulation of a two-dimensional distribution is presented. Experimental data and Support Vector Machine (SVM) are used to verify the algorithm, and the classification results validate the proposed method, which show that jet aircrafts, propeller aircrafts, and helicopters can be classified with an average discrimination rate greater than 92%.

Keywords

• Airborne radar
• Low-resolution
• Aircraft target classification
• Fractal feature
• Amplitude modulation feature
• Support Vector Machine (SVM)