Active Interception for Multi-Target Encirclement by Heterogeneous UAVs: An LSTM-Enhanced Independent PPO Algorithm

In recent years, multi-UAV systems have demonstrated broad applications in both security and civilian domains, where cooperative encirclement has emerged as a key research focus. However, existing work predominantly addresses single-target scenarios with homogeneous UAVs using passive tracking strategies, which are inadequate for handling highly maneuverable targets. To overcome these limitations, this paper proposes an active interception decision framework integrating LSTM networks with an off-policy independent actor–critic framework employing a PPO-style clipped surrogate objective, referred to as LIPPO. It aims to address the complex problem of heterogeneous UAV swarms encircling multiple continuously learning targets. The framework employs an LSTM module for real-time trajectory prediction and uses the predicted future positions as interception points, shifting the paradigm from passive tracking to proactive interception. At the decision level, LIPPO adopts a hybrid architecture where each UAV acts as an independent learner, while a shared experience pool enables efficient knowledge transfer across the swarm. Comprehensive simulations demonstrate LIPPO’s superiority. In complex scenarios, it achieves an encirclement success rate up to 10 percentage points higher than non-predictive baselines and reduces energy consumption by nearly 28% compared to centralized training multi-agent reinforcement learning algorithms. These results confirm that LIPPO’s active interception is both effective and efficient.