mmDif-DRL:Diffusion-Deep Reinforcement Learning for Collaborative mmWave Positioning

Abstract

In emergency rescue scenarios, such as fires where smoke and dust reduce visibility, millimeter-wave (mmWave) radar offers advantages over vision-based localization due to its penetration capability and adaptability to harsh environments. Nevertheless, signal sparsity, multipath interference, and the high cost of data annotation limit its effectiveness in loop closure detection, while conventional trajectory optimization algorithms often lack adaptability, reducing localization accuracy. To address these challenges, this work presents mmDif-DRL, a cooperative and autonomous localization system based on mmWave radar. First, this work proposes a multimodal loop closure detection model by integrating mmWave point clouds and geomagnetic signals. In this model, a Transformer encoder is employed to capture temporal dependencies, and a bidirectional cross-attention mechanism enables effective multimodal fusion. Second, this work proposes a diffusion-based data generation strategy to provide sufficient training samples for the loop closure detection model, while also reducing the need for real data collection and manual annotation. Third, this work proposes a deep reinforcement learning (DRL)-based trajectory optimization strategy, integrated with a Graph-based Simultaneous Localization and Mapping (GraphSLAM) algorithm to achieve robust global optimization. This approach balances adaptability in complex environments with accurate localization. Experiments show that mmDif-DRL achieves an average loop closure detection F1-score of 94.44% and reduces the average localization error to 0.21 m across three indoor scenarios, demonstrating its robustness under challenging conditions.