This paper deals with the problem of coordinating a team of mobile sensor platforms searching for a single mobile non-evading target. It follows the general Bayesian active sensor network approach introduced in [2] where each decision maker plans locally based on an equivalent representation of the target state probability density function (PDF). This paper focuses on the prediction stage of the decentralized Bayesian filter. It looks at how different types of realistic external constraints may affect the target motion and how they may be taken into account in the process model. Two general classes of constraints are identified soft and hard. A few constraint examples from each class are given to illustrate their impact on the evolution of the target state PDF. Multiple constraints of various types can be combined to increase the accuracy of the predicted PDF estimate, thus affecting the individual trajectories of the search platforms. The effectiveness of the framework is demonstrated for a team of airborne search vehicles looking for a drifting target lost in a storm at sea.
[1]
Alexei Makarenko,et al.
Information-theoretic coordinated control of multiple sensor platforms
,
2003,
2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).
[2]
Eric Nettleton,et al.
Real time Multi-UAV Simulator
,
2003,
2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).
[3]
Hugh F. Durrant-Whyte,et al.
Optimal Search for a Lost Target in a Bayesian World
,
2003,
FSR.
[4]
Lawrence D. Stone,et al.
Bayesian Multiple Target Tracking
,
1999
.
[5]
Hugh F. Durrant-Whyte,et al.
Coordinated decentralized search for a lost target in a Bayesian world
,
2003,
Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).
[6]
N. Gordon,et al.
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
,
1993
.
[7]
S. Grime,et al.
Data fusion in decentralized sensor networks
,
1994
.