Multi-scale feature-based fuzzy-support vector machine classification using radar range profiles

摘要

High resolution range profile possesses great significance in automatic target recognition (ATR) research and application due to its efficiency superiority. Feature extraction is one of the major ATR research directions. Existing feature extraction techniques only concern the range profile in original scale, which constrains the cognitive level of classifiers. This study goes beyond this limitation and introduces scale space theory in the feature extraction of range profiles. The edge points of range profiles are extracted and a novel multi-scale target classification method based on fuzzy-support vector machine classifier is proposed. Range profiles of three aircraft models are selected to validate the classification method. Experiment results demonstrate that the addition of edge points is favourable to enhance range profile samples' separability. Comparison of classification performance between original and multiple scales validates multi-scale technique's feasibility in range profile classification problem. The weighting factor developed according to the quadratic entropy optimises range profile samples' membership degree distribution and brings further promotion on classification performance.