Data Association for Multiple Sensor Types Using Fuzzy Logic

Target association in sensor data fusion often assumes that both the target tracks and the measurements are described with Gaussian random variables. For some sensor reports, such as passive acoustics, this assumption creates a poor approximation. For the association of measurement to target track, significant errors can occur as a uniform distribution is warped such that the centroid is weighted significantly more than the edges when a single Gaussian is used as the approximation. Using the chi-squared metric to associate a new measurement to an existing target track under this condition can increase the likelihood of association or reduce it significantly. In this paper, a fuzzy-logic approach to data association is enhanced. The original approach showed that it could emulate the chi-squared metric comparing two Gaussian random variables. Here, the approach is enhanced to handle the cases of two uniformly distributed random variables and the case where a uniform measurement is compared to a Gaussian track