Aircraft identification using a bilinear surface representation of radar data

Abstract

Low frequency radar scattering data is used for the identification of aircraft. It is shown that such radar data lies on two-dimensional surfaces in n-space. A bilinear approximation for these surfaces is described. Surface intersections using this approximation can be found simply and directly without solving a system of n simultaneous nonlinear equations. This intersection information can be used to show separability and effect feature reduction. The approximation is utilized to construct a modified nearest neighbor algorithm, which is evaluated by computer simulation experiments. These experiments showed a phenomenon of “bias”, where one aircraft data surface is more susceptible to misclassification in the presence of noise than the surface corresponding to another aircraft. This “bias” observed is shown to be related to the surface characteristics of the data surfaces involved, specifically proximity and relative curvature of corresponding points on the two surfaces.