Mutual Information Based Risk-Aware Active Sensing in an Urban Environment

The risk-aware path planning problem is considered, which aims to locate a target in a congested urban environment and facilitate aid in the decision making on target interdiction. The target is modeled as a ground vehicle moving randomly within a road network and following traffic rules. To locate the target, a heterogeneous sensor network composed of passive sensors (e.g., Static traffic cameras and mobile human observers) and active sensors (e.g., A UAV) is tasked to cooperatively search for the target. A sample-based Bayesian filter is developed to fuse various sensor measurements to estimate the target state. To facilitate the decision making on target interdiction, a notion of risk is considered, which evaluates the incurred loss of target interdiction at certain locations based on incomplete information of target state and urban factors (e.g., The proximity to critical areas such as populated shopping malls, schools, military, or government buildings). As opposed to the static traffic cameras and the randomly walking human observers that passively provide target measurements, the UAV actively plans its path, based on mutual information, to maximize the in formativeness of future measurements. In contrast to classical target tracking that only focuses on reducing the uncertainty of target state, the risk is encoded in the particle weights to guide the motion of UAV to improve target state estimation and, ultimately, reduce the risk of decision on target interdiction. Simulation results are provided to demonstrate the integrated sensing framework and the risk-aware path planning algorithm.