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In this work a robust and scalable cooperative multi-agent searching and tracking framework is proposed. Specifically, we study the problem of cooperative searching and tracking of multiple moving targets by a group of autonomous mobile agents with limited sensing capabilities. We assume that the actual number of targets present is not known a priori and that target births/deaths can occur anywhere inside the surveillance region thus efficient search strategies are required to detect and track as many targets as possible. To address the aforementioned challenges we recursively compute and propagate in time the searchingand-tracking (SAT) density. Using the SAT-density, we then develop decentralized cooperative look-ahead strategies for efficient searching and tracking of an unknown number of targets inside a bounded surveillance area.

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Drones, including remotely piloted aircraft or unmanned aerial vehicles, have become extremely appealing over the recent years, with a multitude of applications and usages. However, they can potentially present major threats for security and public safety, especially when they fly across critical infrastructures and public spaces. This work investigates a novel counter-drone solution by proposing a multi-agent framework in which a team of pursuer drones cooperate in order to track and jam a rogue drone. Within the proposed framework, a joint mobility and power control solution is developed to optimize the respective decisions of each cooperating agent in order to best track and intercept the moving rogue drone. Both centralized and distributed variants of the joint optimization problem are developed and extensive simulations are conducted to evaluate the performance of the problem variants and to demonstrate the effectiveness of the proposed solution.