The concept of target tracking, a part of level 1 data fusion, is to combine measures from various sensors to form a coherent picture of the scene. A key component of the fusion problem is data association, the assignment of various measurements to existing target tracks. For the typical case in target association where both the target tracks and the measurements are described with Gaussian random variables, the standard association uses the chi 2  metric, a weighted inner product of the residual formed by an estimated measurement and the true measurement. There are cases where the measurements are not well described as Gaussian random variables, including those from sensors that have uncertainties that are better approximated as uniform distributions or where the Gaussian distribution is corrupted by sensor blockage or target constraints. Based upon the proven concept of the chi 2  metric, a straightforward fuzzy-logic-based association method is developed that can emulate this metric for Gaussian measurements but can be modified to address problems where the Gaussian assumption on the track and/or measurement is not appropriate
['Stephen C. Stubberud', 'Kathleen A. Kramer']
Data Association for Multiple Sensor Types Using Fuzzy Logic