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.