Performance comparison of target tracking algortihms in underwater environment

Target tracking is one the most fundamental elements of a radar system. The aim of target tracking is the reliable estimation of a targetpsilas true state based on a time history of noisy sensor observations. In real life, the sensor data may include substantial noise. This noise can render the raw sensor data unsuitable to be used directly. Instead, we must filter the noise, preferably in an optimal manner. For land, air and surface marine vehicles, very successful filtering methods are developed. However, because of the significant differences in the underwater propagation environment and the associated differences in the corresponding sensors, the successful use of similar principles and techniques in an underwater scenario is still an active topic of research. A comparative study of the effects of the underwater environment on a number of tracking algorithms is the focus of the present thesis. The tracking algorithms inspected are: the Kalman filter, the extended Kalman filter and the particle filter. We also investigate in particular the IMM extension to these filters. These algorithms are tested under two representative scenarios.