Sequential bayesian classification decisions for mobile sensors

This work is motivated by the U.S. Air Forces Intelligence, Surveillance and Reconnaissance (ISR) mission, where an Unmanned Aerial Vehicle (UAV), or an agent, is to fly over a number of unidentified objects within a given search area, collect information using onboard sensors, and classify the objects. The problem is challenging because the mission time is limited, the agent is only provided with partial a priori information, and the amount of information that the sensor can measure is dependent on the range and the azimuth of the explorer with respect to the object. A sequential decision problem (path planning) is posed that incorporates the potential loss of the classification outcome that is made by an autonomous moving agent. The problem is solved using stochastic dynamic programming. The resulting path exploits the interaction between the agent kinematics, informatics, and classification. Numerical simulation results that validate the concept are presented.