Target Tracking in the Presence of Intermittent Measurements by a Network of Mobile Cameras

A controller, estimator, and predictor framework is developed for tracking a moving target using a network of mobile cameras, with non-overlapping fields-of-views and operating regions. Using a Lyapunov-based switched systems approach, the proposed framework is proven to be robust to intermittent feedback, and estimates of the target’s pose and motion model are proven to remain bounded, provided that minimum and maximum dwell-time conditions are satisfied (i.e., minimum time the target must be observed and maximum time the target may be unobserved, respectively). This framework allows for teams of cooperative agents to track a moving target in complex environments, while increasing the effective target tracking area and guaranteeing tracking performance.