Distributed Estimation and Control for Jamming an Aerial Target With Multiple Agents

Abstract

This work proposes a distributed estimation and control approach in which a team of aerial agents equipped with radio jamming devices collaborate in order to intercept and concurrently track-and-jam a malicious target, while at the same time minimizing the induced jamming interference amongst the team. Specifically, it is assumed that the malicious target maneuvers in 3D space, avoiding collisions with obstacles and other 3D structures in its way, according to a stochastic dynamical model. Based on this, a track-and-jam control approach is proposed which allows a team of distributed aerial agents to decide their control actions online, over a finite planning horizon, to achieve uninterrupted radio-jamming and tracking of the malicious target, in the presence of jamming interference constraints. The proposed approach is formulated as a distributed model predictive control (MPC) problem and is solved using mixed integer quadratic programming (MIQP). Extensive evaluation of the system's performance validates the applicability of the proposed approach in challenging scenarios with uncertain target dynamics, noisy measurements, and in the presence of obstacles.

Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents

“…Consequently, the centralized formulation requires at each time-step information (i.e., search maps and future plans) from all agents in order to produce the joint search plans by optimizing the objective shown in Eqn. (6). This is possible in the centralized version of the problem since all the information is available at the time of planning and moreover the problem is solved jointly among all agents on a central system.…”

UAV-based Receding Horizon Control for 3D Inspection Planning

“…The same principle is applied to implement collision avoidance constraints amongst the team of agents as shown in Eq. (10k), which requires that during all time-steps the agent's j positional state must reside outside the convex-hull of agent's i safety area A i , i ̸ = j, i ∈ [1, .., N ], where A i is the inscribed dodecahedron around x i,p k+κ|k which approximates a spherical safety area with certain radius around the agent [38]. Finally, the mission is terminated when τ ∈T Q(τ ) = |T |.…”

“…The interest in unmanned aerial vehicles (UAVs), and their utilization in various applications domains such as security [1]- [4], emergency response [5]- [7], monitoring/searching [8]- [12], air-traffic management [13], [14], and automated infrastructure inspection [15]- [18] has peaked in the last years. The majority of the application domains mentioned above require some form of coverage planning [19], which in general requires the UAV to be able to plan an optimal trajectory for the coverage of a specific set of points or objects inside an area of interest.…”

Coordinated CRLB-based Control for Tracking Multiple First Responders in 3D Environments

“…Increased mobility, flexibility, and rapid deployment are highly desirable properties in many application domains. Nowadays, unmanned aerial vehicles (UAVs) have demonstrated their potential in a wide variety of applications including surveillance and security tasks [1]- [4], searchand-rescue missions [5]- [7], and situational awareness for first responders [8]. To meet the requirements in the abovementioned applications, the UAV agents often carry on-board various sensors such as direction-finders that passively scan the spectrum to detect and resolve the direction of a target transmission, and radars that provide the direction and/or distance by processing the reflections on objects by either purposefully transmitted signals (i.e., active radar) or by ambient signals of opportunity (i.e., passive radar).…”