This article considers the cooperative search and track of moving targets by unmanned aerial vehicles (UAVs) over some regions of interest (RoIs). The RoI contains obstacles that may partly hide the targets. UAVs are not aware of the obstacles' locations and cannot determine when an obstacle limits their field of view. In this article, no map of the RoI is built. This reduces the computational complexity but makes the selection of an appropriate point of view to observe a specific part of the RoI difficult. Showing the absence of a target at a given location of the RoI is then challenging. To address this problem, we introduce a detectability set for each point of the RoI as the set of all UAV locations from where that point is visible. The detectability sets are unknown to the UAVs and evolve with time when the environment is time varying. We assume that each detectability set contains at least one half-cone with a minimal aperture, which translates the fact that targets are never fully occluded by the environment. We prove that a finite number of$L$simultaneous observations are sufficient to guarantee the presence or absence of targets at a given location. This leads to a partition of the fleet into groups of$L$UAVs that simultaneously collect measurements on a specific zone of the RoI. Assuming that state perturbations and measurement noises are bounded, a distributed set-membership estimator is used to evaluate set estimates for potential target locations. The trajectories of the UAVs are designed using a model-predictive control approach to reduce the estimation uncertainty. Simulations illustrate the performance of the proposed approach in the presence of unknown static or moving obstacles.

In this paper, an interal observer for both state and unknown input estimation is proposed for a Linear Time Invariant (LTI) system. It generalizes previous work done on the subject. Such an observer is constructed under some specific conditions. Among those conditions, a rank condition is needed to ensure the independence between the state estimation and the unknown input. In this paper, former proposed rank condition is relaxed thanks to the use of another one, less restrictive. Moreover, the proposed observer can be applied for systems with the unknown input in both the state and measurement equations. Examples are given to discuss and illustrate the proposed approach.

That paper addresses the problem of estimating the state and the unknown input in a continuous-time Linear Parameter Varying (LPV) system, in the presence of Gaussian white noises affecting both state and measurement equations. This estimation is made via the use of a Minimum Variance Unbiased Observer (as in Kalman Filtering). After recalling some useful results, the observer equations for state and unknown input estimation are established, and then an example is given in order to illustrate the talk.