Abstract—Correct association of observations with objects is one of the most important and difficult tasks in algorithms for tracking multiple targets in the presence of false observations. We consider one modification of the standard Kalman filter

Correct association of observations with objects is one of the most important and difficult tasks in algorithms for tracking multiple targets in the presence of false observations. We consider one modification of the standard Kalman filter which aims to reduce the tracking error by explicitly taking into account the fact that the probability of correct association is less than one. Through computer simulations, we analyze the performance of this method and assess if, and under what conditions, it can improve upon the standard Kalman filter.