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

TL;DR

The paper tackles tracking and jamming a malicious aerial target in a 3D environment with obstacles using a team of UAVs equipped with radio jammers. It introduces a distributed model predictive control framework that operates online over a finite horizon $T$, while target state estimation is performed via a particle filter and fused with covariance intersection to share a robust joint belief. The approach enforces inter-agent jamming constraints by convexifying sensing regions with a 12-faced dodecahedron and formulating the problem as a MIQP, enabling exact solutions with off‑the‑shelf solvers. Results demonstrate scalable performance in uncertain dynamics, noisy measurements, and obstacle-rich scenarios, highlighting the method’s potential for real-time multi-agent counter-drone operations and its sensitivity to weight configurations and communication. Future work includes real-world deployment considerations, directional antennas, energy constraints, and extension to multiple targets with assignment-based coordination.

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.

Figures (8)

• Figure 1: A team of distributed autonomous aerial agents concurrently track and radio-jam a malicious target, while avoiding the jamming interference amongst them and collisions with obstacles in the environment.
• Figure 2: Overview of the proposed tracking-and-jamming system architecture.
• Figure 3: (a)-(b) The $20$ vertices of a regular dodecahedron (centered at the origin with $R=1$) formed from the vertices of a cube and $3$ planes according to Eq. \ref{['eq:dodecahedron_construction']}, (c) The agent's sensing area i.e., a dodecahedron with circumradius $R$.
• Figure 4: Concurrent tracking-and-jamming scenario with $3$ agents and $1$ target (agents are marked with $\blacklozenge$, with agent $1$, $2$, and $3$ colored blue, turquoise, and green respectively. The ground truth target trajectory (marked with $\bullet$) is colored yellow and the estimated trajectory (marked with $\blacksquare$) is colored orange. The clear shapes $\diamond$ and $\circ$ indicate the agents and target predicted states, respectively).
• Figure 5: Concurrent tracking-and-jamming scenario with $3$ agents and $1$ target with disabled jamming interference constraints. While the target detection-and-jamming objective is maximized as shown in (a), the agents come critically close to each other, causing destructive jamming interference as shown in (b)-(d).