Vision‐based aircraft detection technology may provide a credible sensing option for automated detect and avoid in small‐to‐medium size fixed‐wing unmanned aircraft systems (UAS). Reliable vision‐based aircraft detection has previously been demonstrated in sky‐region sensing environments. This paper describes a novel vision‐based system for detecting aircraft below the horizon in the presence of ground clutter. We examine the performance of our system on a data set of 63 near collision encounters we collected between a camera‐equipped manned aircraft and a below‐horizon target. In these 63 encounters, our system successfully detects all aircraft, at an average detection range of 1890 m (with a standard error of 43 m and no false alarms in 1.1 h). Furthermore, our system does not require access to inertial sensor data (which significantly reduces system cost) and operates at over 12 frames per second.
We consider the problem of computing parameters of player cost functionals such that given state and control trajectories constitute an open-loop Nash equilibrium for a noncooperative differential game. We propose two methods for solving this inverse differential game problem and novel conditions under which our methods compute unique cost-functional parameters. Our conditions are analogous to persistence of excitation conditions in adaptive control and parameter estimation. The efficacy of our methods is illustrated in simulations. Index Terms-Game theory, inverse differential games, inverse optimal control, optimal control.
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