Performance analysis of multiple model kalman filters for target tracking

Target tracking is a computer vision problem on which many studies have been done and research on this topic is still ongoing. The main tasks of target tracking systems can be listed as determining the position, velocity or acceleration of one or more moving targets. Target tracking relies on a recursive prediction using noisy measurements from the radar in order to calculate the next movement of the target. Usually the data measured by monitoring devices is not precise, as the measurements have some kind of measurement noise depending on the sensor. Therefore, the measurement noise of the sensors complicates the target tracking and it is necessary to filter the noise in order to estimate the real path of the moving targets and improve the estimation of the trajectories of the targets. Although the measurement coming from the radar is in polar coordinates, in modern target tracking applications, since the motion of the target is linear in the Cartesian system, the state estimation of the next movement of the target is done in the Cartesian coordinate system. In this thesis, various Kalman Filters were investigated to monitor polar measurements with Cartesian coordinates, remodel noise and then compare the performances of these filters. In addition, since the target does not depend on a single movement model in real life, systems that enable the interaction of more than one movement model are used. Furthermore, the motion models and sample tracking scenarios used to evaluate the performance of these filters were defined, and the performance of multiple filter systems was evaluated through simulations made on different scenarios.