This work addresses the task of active sensing, or information-seeking control of mobile sensor platforms. Formulation of a control objective in terms of information gain allows mobile sensors to be both autonomous and easily reconfigurable to include a variety of sensor and target models. Tracking a moving target using a camera mounted on a fixed-wing unmanned aircraft is considered, but the control formulation is not specific to this choice of sensor or estimation task. A control formulation is developed which minimizes the entropy of an estimate distribution over a receding horizon subject to stochastic non-linear models for both the target motion and sensors. Previous similar work has been restricted to either a stationary target, a horizon of length one, or Gaussian estimates. The prediction of conditional entropy is shown to be inherently complex, and a computationally efficient sequential Monte Carlo method is developed. The entropy prediction depends on this Monte Carlo method as well as a novel approach for entropy calculation in the context of particle filtering. These methods are verified through simulation and post-processing of experimental flight data.
Particle filter based information-theoretic active sensing
Auf Teilchenfilter basierende informationstheoretische aktive Sensorerfassung
Robotics and Autonomous Systems ; 58 , 5 ; 574-584
2010
11 Seiten, 13 Bilder, 2 Tabellen, 27 Quellen
Article (Journal)
English
Information-Theoretic Tracking Control Based on Particle Filter Estimate
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