In this paper, we describe a model-based, non-linear reconstruction method for mapping internal building structures using through-wall radar data. We based the model on canonical geometry constructs that are commonly used in construction practices. These constructs are then formulated as sets of simple scattering mechanisms, which can be estimated from the data. Our non-linear approach employs an iterative, conditional estimation method as a function of the intervening structures between the sensor and the object under consideration. Specific associations of scattering mechanisms are then used to re-create various building structures such as walls, doors, stairs, etc. We discuss some examples of estimating specific scattering mechanisms and a model-based reasoning approach for assembling them to reconstruct the interior structure of a building.

We develop and investigate several novel multiresolution algorithms for detecting coherent radar targets embedded in clutter. These multiresolution detectors exploit the fact that prominent target scatterers interfere in a characteristic manner as resolution is changed, while multiresolution clutter signatures are random. We show, both on simulated and collected synthetic aperture radar data, that these multiresolution algorithms yield significant detection improvements over single-pixel, single-resolution constant false alarm rate (CFAR) methods that use only the finest available resolution.

Reviews the characteristics of hyperspectral imaging sensors and describes several important data exploitation applications in remote sensing. The authors then focus on a particular signal processing application, material identification, and propose a novel algorithm based on multiresolution wavelet techniques. Finally, they demonstrate the multiresolution material identification algorithm on data collected with a all-band hyperspectral sensor.

We demonstrate the utility of a multiresolution approach for target detection in SAR imagery. Man-made objects exhibit characteristic phase and amplitude fluctuations as the image resolution is varied, while natural terrain has a random signature. We construct a number of detection strategies: an optimal invariant multiresolution detector based on a derived multiresolution increments process; and a generalized likelihood ratio detector to differentiate between a first-order autoregressive multiresolution increments process and white noise. We show that these schemes significantly outperform a standard energy detector operating on the finest available SAR resolution.

We apply a Wald sequential hypothesis test to synthetic aperture radar (SAR) phase history data. A 2D autoregressive (AR) signal model is applied to the SAR phase history data to exploit the signatures produced by cultural objects. We model the sequential probability ratio decision statistic as a Brownian motion process with drift. The test can then be simply approximated as a test for the drift in the Brownian motion process. We show that the required aperture extent (number of pulses) to determine the presence of a target within the scene is significantly smaller than the full aperture.

Examines the problem of detecting a known target in clutter and shows that a multiresolution-based detector significantly outperforms a more conventional single-resolution detector. The authors then apply a sampling strategy that allows one to choose, for each fixed n, those n resolutions that maximize detection performance. This optimal multiresolution sampling strategy suggests that wavelet or wavelet packet decompositions which typically use dyadic or triadic resolution splits are not necessarily optimal bases for coherent radar signature representation.>