Search for dynamic targets with uncertain probability maps

This paper extends a recently developed statistical framework for UAV search with uncertain probability maps to the case of dynamic targets. The probabilities used to encode the information about the environment are typically assumed to be exactly known in the search theory literature, but they are often the result of prior information that is both erroneous and delayed, and will likely be poorly known to mission designers. Our previous work developed a new framework that accounted for the uncertainty in the probability maps for stationary targets, and this paper extends the approach to more realistic dynamic environments. The dynamic case considers probabilistic target motion, creating uncertain probability maps (UPMs) that take into account both poor knowledge of the probabilities and the propagation of their uncertainty through the environment. A key result of this paper is a new algorithm for implementing UPM's in real-time, and it is shown in various simulations that this algorithm leads to more cautious information updates that are less susceptible to false alarms. The paper also provides insights on the impact of the design parameters on the responsiveness of the new algorithm. Several numerical examples are presented to demonstrate the effectiveness of the new framework