Target Tracking with Generalized Data Association based on the General DSm Rule of Combination
نویسندگان
چکیده
The objective of this paper is to present an approach for target tracking, which incorporates the advanced concept of generalized data (kinematics and attribute) association to improve track maintenance performance in complicated situations (closely spaced targets), when kinematics data are insufficient for correct decision making. It uses Global Nearest Neighbour-like approach and Munkres algorithm to resolve the generalized association matrix. The main peculiarity consists in applying the principles of Dezert-Smarandache theory of plausible and paradoxical reasoning to model and process the utilized attribute data. The new general Dezert-Smarandache hybrid rule of combination is used to deal with particular integrity constraints associated with some elements of the free Dedekind’s distributive lattice. The aim of the performed study is to provide coherent decision making process related to generalized data association and to improve the overall tracking performance.
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