An analysis of object designation performance using GNN and GNP correlation

摘要

Many tracking systems have the requirement to transfer information about a particular tracked object between two systems. The general approach to this involves generation of an object map by the system designating the particular track followed by receipt of the map and correlation to the local track picture of the second system. Correlation performance is in general limited by a number of factors: random track errors added by each system, miss-registration of the two systems' coordinate frames, and miss-match between the numbers of objects tracked by the two systems. Two correlation algorithms are considered for this problem: Global Nearest Neighbor (GNN) and Global Nearest Pattern (GNP). Four basic failure modes are identified for the GNP formulation, and three of these explain failures in the GNN formulation. Analytic expressions are derived for each of these modes, and a comparison of each to Monte-Carlo experiment is provided to demonstrate overall validity