A MARL Based Multi-Target Tracking Algorithm Under Jamming Against Radar

TL;DR

This paper introduces a multi-agent reinforcement learning approach for UAV swarms to adaptively select between active and passive radar modes for multi-target tracking in jamming environments, improving robustness.

Contribution

It presents a novel MARL-based control algorithm for UAV swarms to optimize radar mode selection under jamming, addressing a complex optimization problem with a simulated annealing mechanism.

Findings

The proposed algorithm effectively tracks multiple targets despite jamming.

Simulation results show improved tracking performance over baseline methods.

The system dynamically adapts radar modes to counteract jamming effects.

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.