Information-Theoretic Tracking Control Based on Particle Filter Estimate

The contribution of this work is a control formulation for a mobile sensor to track a target using an information-theoretic cost function based on a particle filter estimate of the target state. The particle filter representation fully models the non-linearity and limited field of view of the sensor and is able to search for a lost target by updating the estimate to eliminate areas which have been searched. An entropy calculation is developed which reflects the uncertainty of the particle filtering density for the purpose of tracking, and is then combined with a sampling method to predict the expected entropy of the target state estimate under a proposed control. When sensor motion is constrained, such as for a fixed-wing aircraft, a long planning horizon can provide better performance than single step planning approaches. Exact prediction of information-theoretic costs for non-linear models is not generally feasible in real time, and so approximate methods will be required to predict the expected estimate entropy for receding horizon control. Simulation results demonstrate the accuracy of the prediction method and the effectiveness of the information-theoretic control. Initial experimental results verify the appropriateness of the particle filter for tracking a mobile target from an unmanned aircraft.

[1]  Hugh F. Durrant-Whyte,et al.  Dynamic space reconfiguration for Bayesian search and tracking with moving targets , 2008, Auton. Robots.

[2]  Wolfram Burgard,et al.  Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM After Actively Closing Loops , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[3]  J. Karl Hedrick,et al.  INFORMATION-THEORETIC SENSOR MOTION CONTROL FOR DISTRIBUTED ESTIMATION , 2007 .

[4]  Christophe Andrieu,et al.  Particle methods for change detection, system identification, and control , 2004, Proceedings of the IEEE.

[5]  Martin Ulmke,et al.  Data Fusion for Ground Moving Target Tracking , 2006, 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[6]  J.K. Hedrick,et al.  Convoy protection using multiple unmanned aerial vehicles: organization and coordination , 2005, Proceedings of the 2005, American Control Conference, 2005..

[7]  Moshe Kress,et al.  Unmanned Aerial Vehicles , 2009 .

[8]  R. Sengupta,et al.  Vision Based Following of Locally Linear Structures using an Unmanned Aerial Vehicle , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[9]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[10]  Mark Campbell,et al.  A Vision Based Geolocation Tracking System for UAV ’ s , 2006 .

[11]  G. Conte,et al.  Utilizing Model Structure for Efficient Simultaneous Localization and Mapping for a UAV Application , 2008, 2008 IEEE Aerospace Conference.

[12]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[13]  Claire J. Tomlin,et al.  Distributed Cooperative Search using Information-Theoretic Costs for Particle Filters, with Quadrotor Applications ∗ , 2006 .

[14]  Salah Sukkarieh,et al.  The Demonstration of a Cooperative Control Architecture for UAV Teams , 2006, ISER.

[15]  J. Karl Hedrick,et al.  A multiple UAV system for vision-based search and localization , 2008, 2008 American Control Conference.

[16]  Raja Sengupta,et al.  Target detection and position likelihood using an aerial image sensor , 2008, 2008 IEEE International Conference on Robotics and Automation.

[17]  Vijay Kumar,et al.  Information Driven Coordinated Air-Ground Proactive Sensing , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[18]  Tomonari Furukawa,et al.  Belief Driven Manipulator Control for Integrated Searching and Tracking , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.