This paper uses the fractional Fourier transform to identify dispersive scatterers in a GPR signature. The motive is that certain dispersive scatterers that uniquely define an object can be better localized in the fractional Fourier spectrum. These scattering features that are extracted via the fraction Fourier transform are then presented to a nonparametric landmine detection scheme. The motive is to detect landmines based on specific scattering mechanisms thus avoiding the use of the significant ground return as a primary feature

In this paper, we discuss a modified Prony's algorithm for use in buried land mine detection from ground penetrating radar (GPR) data. The algorithm finds the dominant complex natural resonances (CNRs) for vectors of time-domain GPR data. We consider the identification of objects present in two-dimensional images. The CNR features of an unknown image are processed through distance-based detectors with the CNR features of known objects from an object library. We analyze our algorithm performance versus SNR and also analyze performance for different distance detectors.

This paper presents an application of blind signal separation to the field of digital signatures and watermarking of audio signals. The second-order blind identification (SOBI) algorithm presented by Belouchrani et al. can be used to retrieve a watermark or to identify a signature. [1]. Multiple source signals (one of which acts as a digital signature) are arbitrarily mixed with grossly different proportions and transmitted. Only the linear mixtures are observed and no information is known about the prior mixing. This process allows for digital verification of the original message, as well as the ability to transmit hidden, buried messages.

A method for characterizing radar signatures using the wavelet transform is developed based on the principle of scattering centers. The adopted target representation is based on the colored bright spot approach, which is closely related to wavelet analysis. It is shown how to estimate the number, location, level of integration or differentiation, and scattering intensity of point scatterers using wavelet decomposition. The proposed target analysis method is tested using experimentally measured radar signals.>

Features extracted from the bispectrum of radar signals are used for classification of unknown radar targets. The classification performance compared with the performance of other classifiers that are not based on higher-order spectral processing of the measured radar data. The radar signals used are experimental measurements that correspond to scattering from real radar targets. The data is corrupted with different types of disturbances that are likely to occur in a typical radar system. Although the number of data samples is relatively small and may be insufficient to produce very accurate bispectral estimates, it is concluded that the bispectrum classifier may outperform other known classifiers under conditions of colored noise and non-Gaussian noise.>