Maneuvering target tracking by using particle filter

The aim of this research is to track a maneuvering target, e.g. a ship, an aircraft, and so on. We use a state-space representation to model this situation. The dynamics of the target is represented by a system model, firstly in continuous time, though a discretized system model is actually to be used in practice. The position of the target is measured by radar, and this process is described by a nonlinear observation model in polar coordinates. To follow abrupt changes in the target´s motion due to sudden operations of the acceleration pedal, braking and steering, we propose the use of heavy-tailed non-Gaussian distribution for the system noise. Consequently, the model we use is a nonlinear non-Gaussian state-space model. A particle filter is used to estimate the target state of the nonlinear non-Gaussian model. The usefulness of the method is shown by simulation