Skip to main content
  • Research Article
  • Open access
  • Published:

A POMDP Framework for Coordinated Guidance of Autonomous UAVs for Multitarget Tracking


This paper discusses 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 onboard 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), combined with other application-specific approximations and techniques within the POMDP framework, produces a practical design that coordinates 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 is demonstrated by extending the objective to reduce the probability of a track swap in ambiguous situations.

Publisher note

To access the full article, please see PDF.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Scott A. Miller.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Miller, S.A., Harris, Z.A. & Chong, E.K. A POMDP Framework for Coordinated Guidance of Autonomous UAVs for Multitarget Tracking. EURASIP J. Adv. Signal Process. 2009, 724597 (2009).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: