Signature Prediction and Uncertainty Analysis for Radar-based MDA Applications

The objectives of this proposal are to develop and demonstrate a novel physics-based computational electromagnetics (CEM) algorithm for accurate and efficient predication of radar signatures of MDA objects of interest (MOIs) and to implement and demonstrate a robust stochastic collocation-based algorithm for efficiently quantifying the effect of geometrical and material uncertainties on the signature prediction. The proposed CEM algorithm is based on the hybrid finite element and boundary integral method, which has been known for its high accuracy and great capabilities to model complex geometries and anisotropic, inhomogeneous, composite materials, both of which are critical for modeling of MOIs. To significantly enhance the efficiency of signature prediction, the proposed algorithm incorporates a novel numerical scheme to exploit either the continuous or the discrete rotational symmetry present in most MOIs and a novel frequency-sweep technique based on adaptive solution space projection to compute broadband radar signature. The proposed uncertainty analysis employs a stochastic collocation method that permits the direct use of the proposed CEM algorithm to compute radar signature for significantly fewer samples than required by the traditional Monte-Carlo method. The hybridization of these novel advanced algorithms will result in a highly accurate and efficient tool to compute the radar signatures of MOIs and to quantify potential errors due to geometrical/material modeling uncertainty.