SPARSE TIME-FREQUENCY REPRESENTATION BASED FEATURE EXTRACTION METHOD FOR LANDMINE DISCRIMINATION

Low-frequency ultra-wideband synthetic aperture radar is a promising technology for landmine detection. According to the scattering characteristics of body-of-revolution (BOR) along with azimuth angles, a discriminator based on Bayesian decision rule is proposed, which uses sequential features, i.e., double-hump distance. First, the algorithm estimates the target scatterings in all azimuth angles based on regions of interest. Second, sequential aspect features are extracted by sparse time-frequency representation. Third, the distributions of features are obtained by training samples, and then the posterior probability of landmine class is computed as an input to the classifler adopting Mahalanobis distance. The experimental results indicate that the proposed algorithm is efiective in BOR target discrimination.