Adaptive Path Design of a Moving Radar

We consider the problem of designing the trajectory of a radar mounted on a moving platform. We develop an adaptive algorithm that, at each time step, optimally selects the radar path in response to the estimated and predicted target parameters to improve the tracking accuracy. We derive our approach under a framework of sequential Bayesian filtering. We apply a sequential Monte Carlo method (particle filter) to implement the filter for the case of nonlinear measurement models. We design the criterion for the path optimization based on the posterior Cramér-Rao bound.