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This paper presents a set-membership approach for the coordinated control of a fleet of UAVs aiming to search and track an a priori unknown number of targets spread over some delimited geographical area. The originality of the approach lies in the description of the perturbations and measurement uncertainties via bounded sets. A set-membership approach is used to address the localization and tracking problem. At each time step, sets guaranteed to contain the actual state of already localized targets are provided. A set containing the states of targets still to be discovered is also evaluated. These sets are then used to evaluate the control input to apply to the UAVs so as to minimize the estimation uncertainty at the next time step. Simulations considering several UAVs show that the proposed set-membership estimator and the associated control input optimization are able to provide good localization and tracking performance for multiple targets.

In this study, a new approach is proposed for accurate target identification, required by the phased array radar systems that are employed in through-the-wall imaging applications. In radar imaging, the effects of the multipath propagation are materialised in fake impressions of the true target, known as ghost images. The developed algorithm removes these ambiguities related to the existence of a target by cancelling, in the final radar image, the ghost images by means of improving the global signal-to-spurious-ratio (SSR) and the target-to-ghost-ratio (TGR) that characterise the real target signature. The development of the approach is based on beamforming and on coherent signal processing upon the returned signal and clutters, for a phased array monostatic radar system. The performances of the algorithm are measured in terms of both the global SSR gain and the TGR and they are verified through simulations and measurements in scenarios that quantify the robustness of the approach. The obtained numerical results concerning the global SSR gain and the TGR certify that the proposed method improves the target localisation and removes the ghost images for radars that operate in rich scattering environments. In the end, the limitations of the approach are also presented.

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