Maneuvering target tracking based on SDE driven by garch volatility

In this paper, we consider the challenging problem of tracking a maneuvering target with abrupt accelerations and introduce a new model based on GARCH process. We formulate the acceleration of dynamic model by stochastic differential equation (SDE) with adaptive coefficients and stochastic volatility. Our adaptive state space approach provides a novel dynamic model that naturally facilitates the physical constraints from the target acceleration jumps to the high maneuvering dynamics model in a probabilistic form, thereby achieving improved tracking accuracy and efficiency compared to competing techniques. Finally, the effectiveness and capabilities of our proposed strategy are demonstrated and validated through a simulation study.