Joint time-frequency analysis of electromagnetic backscattered data

The application of joint time-frequency techniques for the analysis of electromagnetic backscattered data is reviewed. In the joint time-frequency feature space, discrete time events such as scattering centers, discrete frequency events such as target resonances, and dispersive mechanisms due to surface waves and guided modes can be simultaneously displayed. We discuss the various joint time-frequency representations including the short-time Fourier transform, wavelet transform, Wigner-Ville distribution, windowed super-resolution algorithms and the adaptive spectrogram. Emphasis is placed on how these algorithms can be used to represent with good resolution the scattering phenomenology in electromagnetic data. We highlight an application of joint time-frequency processing for radar image enhancement and feature extraction. It is shown that by applying joint time-frequency processing to the conventional inverse synthetic aperture radar(ISAR) imagery, it is possible to remove non-point scattering features in the image, leading to a cleaned image containing only physically meaningful point scatterers. The extracted frequency-dependent mechanisms can be displayed in an alternative feature space to facilitate target identification.