Target search and tracking using a fleet of UAVs in presence of decoys and obstacles

This paper addresses the problem of searching and tracking of an a priori unknown number of indistinguishable targets spread over some geographical area using a fleet of UAVs. State perturbations and measurement noises are assumed to belong to bounded sets. In the monitored geographical area, some false targets (decoys) are present and may be erroneously considered as targets when observed under specific conditions. Moreover, obstacles in the search area constrain the displacements of the targets, alter the UAVs’ trajectories, reduce their fields of view, and limit their communications. While the UAVs can detect targets or decoys when observation conditions are satisfied, they cannot identify them individually.The search process relies on a robust bounded-error estimation approach which aim is to evaluate a set guaranteed to contain the actual states of already localized true targets and a set containing the states of targets still to be discovered. These two sets are used by each UAV to determine their control inputs in a distributed way to minimize future estimation uncertainty.Simulations involving several UAVs illustrate that the pro-posed robust set-membership estimator and distributed control laws make it possible to efficiently search and track targets in the presence of decoys in a cluttered area.

[1]  Pei Li,et al.  A potential game approach to multiple UAV cooperative search and surveillance , 2017 .

[2]  Eric Walter,et al.  Guaranteed Set Computation with Subpavings , 2001 .

[3]  Jay H. Lee,et al.  Model predictive control: past, present and future , 1999 .

[4]  Silja Meyer-Nieberg,et al.  Moving target search optimization - A literature review , 2019, Comput. Oper. Res..

[5]  Didier Dumur,et al.  Model predictive control of cooperative vehicles using systematic search approach , 2014 .

[6]  Isaac Kaminer,et al.  Search-Trajectory Optimization: Part I, Formulation and Theory , 2016, J. Optim. Theory Appl..

[7]  J. Bather,et al.  Tracking and data fusion , 2001 .

[8]  Jianda Han,et al.  Active Persistent Localization of a Three-Dimensional Moving Target Under Set-Membership Uncertainty Description Through Cooperation of Multiple Mobile Robots , 2015, IEEE Transactions on Industrial Electronics.

[9]  Marko Bacic,et al.  Model predictive control , 2003 .

[10]  Siegfried M. Rump,et al.  INTLAB - INTerval LABoratory , 1998, SCAN.

[11]  Hélène Piet-Lahanier,et al.  A set-membership approach to find and track multiple targets using a fleet of UAVs , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[12]  Hélène Piet-Lahanier,et al.  Cooperative guidance of a fleet of UAVs for multi-target discovery and tracking in presence of obstacles using a set membership approach , 2019 .

[13]  Lihua Xie,et al.  Multi-Agent Cooperative Target Search , 2014, Sensors.

[14]  Luc Jaulin,et al.  Guaranteed Characterization of the Explored Space of a Mobile Robot by Using Subpavings , 2013, NOLCOS.

[15]  Antonios Tsourdos,et al.  Constrained Multiple Model Bayesian Filtering for Target Tracking in Cluttered Environment , 2017 .

[16]  Jason R. Marden,et al.  Revisiting log-linear learning: Asynchrony, completeness and payoff-based implementation , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[17]  R. Olfati-Saber,et al.  Distributed Kalman Filter with Embedded Consensus Filters , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[18]  James F. Blinn,et al.  Me and My (Fake) Shadow , 1988 .

[19]  Isaac Kaminer,et al.  Search-Trajectory Optimization: Part II, Algorithms and Computations , 2015, Journal of Optimization Theory and Applications.