We develop a radar-based automatic target recognition approach for
partially occluded objects. The approach may be variously posed as
an optimization problem in the phase history, scene reflectivity
and feature domains. The latter consists of point scattering
features estimated from the phase histories or corresponding
images. We adopt simple occlusion models in which the physical
scattering responses (isotropic scattering centers, attributed
scatterers, etc.) can be occluded in any combination. The
formulation supports the use of prior occlusion models (e.g., that
occlusion is spatially correlated rather than randomly
distributed). We introduce a physics-based noise covariance model
for use in cost or objective functions. Occlusion model estimation
is a combinatorial problem since the optimal subset of scatterers
must be discovered from a potentially much larger set. Further,
the number of occluded scatterers must be estimated as a part of
the solution. We apply a genetic algorithm to solve the
combinatorial problem, and we provide a simple demonstration
example using synthetic data.
KEYWORDS: Data modeling, Performance modeling, Automatic target recognition, Detection and tracking algorithms, Synthetic aperture radar, Model-based design, Systems modeling, Monte Carlo methods, Sensors, Feature extraction
Performance of automatic target recognition (ATR) systems depends on numerous factors including the mission description, operating conditions, sensor modality, and ATR algorithm itself. Performance prediction models sensitive to these factors could be applied to ATR algorithm design, mission planning, sensor resource management, and data collection design for algorithm verification. Ideally, such a model would return measures of performance (MOPs) such as probability of detection (Pd), correct classification (Pc), and false alarm (Pfa), all as a function of the relevant predictor variables. Here we discuss the challenges of model-based and data-based approaches to performance prediction, concentrating especially on the synthetic aperture radar (SAR) modality. Our principal conclusion for model-based performance models (predictive models derived from fundamental physics- and statistics-based considerations) is that analytical progress can be made for performance of ATR system components, but that performance prediction for an entire ATR system under realistic conditions will likely require the combined use of Monte Carlo
simulations, analytical development, and careful comparison to MOPs from real experiments. The latter are valuable for their high-fidelity, but have a limited range of applicability. Our principal conclusion for data-based performance models (that fit empirically derived MOPs) offer a potentially important means for extending the utility of empirical results. However, great care must be taken in their construction due to the necessarily sparse sampling of operating conditions, the high-dimensionality of the input
space, and the diverse character of the predictor variables. Also the applicability of such models for extrapolation is an open question.
The recently developed physics-based "mean field" formalism for
efficiently computing the time-domain response of compact metallic
targets is applied to the solution of model inverse problems for
remote classification of buried UXO-like targets. The formalism is
first used to compute model forward scattering data, in the form
of time-domain decay curves as measured by EMI or magnetic field,
for a sequence of canonical ellipsoidal target shapes of various
geometries. This data is subsequently used as input to a genetic
algorithm-based inversion routine, in which the target parameter
model space, comprised of target shape, conductivity, location,
orientation, etc., is efficiently searched to find the best fit to
the data. Global search procedures, such as genetic algorithms,
typically require the forward scattering solution for hundreds, or
perhaps thousands, of candidate target models. To be practical,
these forward solutions must be rapidly computable. Our solution
approach has been specifically designed to meet this requirement. Of special interest is the ability of the inversion algorithm to
distinguish robustly between UXO-like targets, modelled here as
cylindrically shaped prolate spheroids, and, say, flat sheet-like
clutter targets, modelled as very thin oblate spheroids.
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