A MARL Based Multi-Target Tracking Algorithm Under Jamming Against Radar
Ziang Wang, Lei Wang, Qi Yi, Yimin Liu
TL;DR
This work addresses robust multi-target tracking by a UAV swarm in the presence of radar jamming. It formulates a Dec-POMDP-based MARL approach (MAPPO) to jointly optimize UAV trajectories and radar modes (active vs. passive) while accounting for jammer-enabled DOA measurements and non-convex constraints, using a simulated annealing-based mechanism to enforce feasibility. The method leverages CRLB-based tracking metrics, combining them into a TE objective to guide learning, and demonstrates superior performance over MATD3 and MADDPG baselines in simulations with jammed and non-jammed targets. The results suggest practical viability for jammer-aware UAV-MTT and illustrate how constraint-tuning can enhance learning stability without sacrificing tracking quality; code is available at the provided repository.
Abstract
Unmanned aerial vehicles (UAVs) have played an increasingly important role in military operations and social life. Among all application scenarios, multi-target tracking tasks accomplished by UAV swarms have received extensive attention. However, when UAVs use radar to track targets, the tracking performance can be severely compromised by jammers. To track targets in the presence of jammers, UAVs can use passive radar to position the jammer. This paper proposes a system where a UAV swarm selects the radar's active or passive work mode to track multiple differently located and potentially jammer-carrying targets. After presenting the optimization problem and proving its solving difficulty, we use a multi-agent reinforcement learning algorithm to solve this control problem. We also propose a mechanism based on simulated annealing algorithm to avoid cases where UAV actions violate constraints. Simulation experiments demonstrate the effectiveness of the proposed algorithm.