Statistical Inference for Change-of-Aperture Problems in Command and Control

Abstract : A map is a powerful way of summarizing spatial data; in the arena of Command and Control (C2), great maps can produce knowledgable command decisions. Two approaches to statistically optimal mapping are taken. The first develops spatial multiresolution Kalman filtering of data at various apertures, and the second develops constrained optimal spatial prediction to answer nonlinear C2 questions consistently. Finally, the temporal component is introduced to allow updating of current maps based on newly acquired C2 data.