UAV Communication Management and Coordination for Multitarget Tracking
Abstract
The research conducted under this grant concerned the application of the theory of partially observable Markov decision processes (POMDPs) to the design of guidance algorithms for controlling the motion of unmanned aerial vehicles (UAVs) with on-board sensors to improve tracking of multiple ground targets. While POMDP problems are intractable to solve exactly, principled approximation methods can be devised based on the theory that characterizes optimal solutions. A new approximation method called nominal belief-state optimization (NBO) was proposed. When combined with other application-specific approximations and techniques within the POMDP framework, NBO produced a practical design that coordinated the UAVs to achieve good long-term mean-squared-error tracking performance in the presence of occlusions and dynamic constraints The flexibility of the design was demonstrated by extending the objective to reduce the probability of a track swap in ambiguous situations, with the positive side-effect of improving the mean-squared-error tracking performance as well.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 26, 2009
- Accession Number
- ADA495739
Entities
People
- Edwin K. Chong
- Scott A. Miller
- Zachary A. Harris