No abstract

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 proposed 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.

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This paper addresses the cooperative search of static ground targets by a group of Unmanned Aerial Vehicles (UAVs) over some region of interest. The search strategy dependents on the availability and accuracy of the information collected. When a target is detected, a probabilistic description of the measurement noise is usually considered, as well as probabilities of false alarm and non-detection, which may prove difficult to characterize a priori.An alternative modeling is introduced here. The ability to detect and identify a target depends deterministically on the point of view from which the target is observed. Introducing the notion of detectability sets for targets, we propose a robust distributed set-membership estimator to provide set estimates of target locations. The obtained set estimates are guaranteed to contain all target locations when the search is completed. The target search is formulated as a multi-agent cooperative control problem where the control inputs are obtained using a Model Predictive Control (MPC) approach minimizing a measure of the set estimates representing the detection performance. The proposed set estimator and cooperative control scheme are distributed, i.e., accounting only for information from neighbors within communication range. The effectiveness of the proposed algorithm is illustrated by simulation.