Multidimensional synthetic estimation filter

Abstract

The synthetic estimation filter (SEF) crafts an affine variation into its response to a changing parameter (e.g. scale or rotation). Sets of such filters are used in an estimation correlator to reduce the number of filters required for a given tracking accuracy. By overspecifying the system (one more SEF than parameters to be tracked), the ratio of correlation responses between filters forms a robust estimator into the spanned domain of the parameters. Previous results dealt with a laboratory correlator which could track a single parameter. This paper explores the SEF and the estimator's extension to more dimensions. A 2D example is given in which a reduction of filters from 25 to 3 is demonstrated to span a 4-degree square portion of pose space.