Genetic algorithm-tuned adaptive pruning SVDD method for HRRP-based radar target recognition

Abstract

ABSTRACTA novel machine learning method named adaptive pruning support vector data description (APSVDD) is developed to classify the FFT-magnitude feature of complex high-resolution range profile (HRRP), motivated by the problem of radar automatic target recognition (RATR). The APSVDD method not only inherits the advantage of least square support vector machine (LSSVM) model, which owns low computational complexity with linear equality constraints so that it is convenient to prune the boundary of SVDD dynamically and rapidly, but also overcomes the shortcoming of ability to deal with outliers in SVDD so that it can enclose targets and exclude outliers simultaneously. Genetic algorithm (GA) tunes the pruning direction of ‘shear’ dynamically, reducing the empirical risk. And fuzzy membership contributes to decision of classes for multiclass fuzzy areas. Besides, similar to the LSSVM, the distribution information within classes is found by least square method and applied for adjusting the pruning depth of ‘shear’ in APSVDD. Hence, there will be a remarkable improvement in recognition accuracy and generalization performance. Numerical experiments based on two publicly UCI datasets and remotely sensed data of four aircrafts can demonstrate the feasibility, repeatability and superiority of the proposed method. The APSVDD is ideal for HRRP-based radar target recognition.