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An approach to estimation for hybrid systems is presented that utilizes uncertain perceptional information about the system's mode to improve tracking of its mode and continuous states. This results in significant improvements in situations where previously reported methods of estimation for hybrid systems perform poorly due to poor distinguishability of the modes. Even in applications where the modes are more easily distinguished, the approach presented can improve performance by decreasing the mode estimation delay of the estimator. Here, state tracking is achieved using a new form of Rao-Blackwellized particle filter called the mode-observed Gaussian Particle Filter. This new filter extends existing hybrid estimation algorithms to admit uncertain but discrete mode-related observations in addition to the information available from more traditional sensors. The framework for estimation using both traditional and perceptional information is applicable to any stochastic hybrid system with mode-related perceptional observations available. Furthermore, the computational efficiency of the Rao-Blackwellized particle filter is maintained, making this filter suitable for real-time implementation. An application that motivates this research is an automatic underwater robotic observation system that follows and films individual deep ocean animals. In that context, to improve tracking of agile animals, a mode-observed Gaussian Particle Filter is applied to supplement measurements of relative position and water-relative velocities of the specimen with perception of what the animal is doing (via visual cues). In addition to improving position and velocity tracking, this filter improves estimation of animal behavior modes to facilitate potentially the automated collection of behavioral data. Performance of the new filter is demonstrated using data from simulation as well as field data collected when tracking actual specimens in the ocean.
Date of Publication: April 2011