Model-based object recognition by wave-oriented data processing

Object recognition can be parametrized systematically through physically robust wave objects by linking features (observables) in scattered field data with features on the object (target) giving rise to the data. The wave objects are broadly separated into global (mode) and local (wavefront) categories. Their parametrization requires different wave-oriented signal-processing algorithms which are implemented conveniently in relevant subdomains of the configuration (space-time) spectrum (wavenumber-frequency) phase space. Projection of scattering data onto the phase space is achieved via Gaussian-windowed Fourier transforms, wavelet transforms, and windowed model-based (superresolution) algorithms. Example results are presented here for time-domain modes excited by an open cavity as well as by periodic and quasi-periodic structures, with data processed in the time-frequency phase space. Additionally, we consider frequency-domain modes (leaky modes supported by a dielectric slab) which are processed in the space-wavenumber phase space. For some situations, it is more appropriate to process the entire database simultaneously (without windowing), and we have used such techniques for certain modal and wavefront parametrizations. Concerning modal 'footprinting', results are presented for superresolution processing of measured short-pulse scattering data from resonant targets embedded in foliage (foliage penetrating radar); in these examples we extract late-time target resonant frequencies. We have also applied superresolution algorithms to wavefront-based processing, and results are presented here for model targets.

Duke Scholars

Author Lawrence Carin Electrical and Computer Engineering