On the quality estimation of optimal multiple criteria data association solutions

In this paper, we present a method to estimate the quality (trustfulness) of the solutions of the classical optimal data association (DA) problem associated with a given source of information (also called a criterion). We also present a method to solve the multi-criteria DA problem and to estimate the quality of its solution. Our approach is new and mixes classical algorithms (typically Murty's approach coupled with Auction) for the search of the best and the second best DA solutions, and belief functions (BF) with PCR6 (Proportional Conflict Redistribution rule # 6) combination rule drawn from DSmT (Dezert-Smarandache Theory) to establish the quality matrix of the global optimal DA solution. In order to take into account the importances of criteria in the fusion process, we use weighting factors which can be derived by different manners (ad-hoc choice, quality of each local DA solution, or inspired by Saaty's Analytic Hierarchy Process (AHP)). A simple complete example is provided to show how our method works and for helping the reader to verify by him or herself the validity of our results.