深度学习辅助的灾后搜救任务无人机定位框架
Deep Learning-Assisted UAV Localization Framework for Post-Disaster Search and Rescue Missions
浏览论文内容
中文总结 AI 辅助
该研究提出一种采用CNN-LSTM信道分类与多场景适配定位求解器的深度学习辅助无人机定位框架，可在灾后未知传播条件下实现高精度快速定位，相关数据集已公开。
中文摘要 AI 辅助
在搜救（SAR）行动中，精准定位被困幸存者可说是最具挑战性的问题，尤其是当基础设施被毁，且搜救团队仅能依靠来自智能手机的低功率信标信号进行搜索时。本文提出一种基于无人机的集中式协作定位框架，该框架采用深度学习方法进行信道分类，并实现适配三维环境的目标估计。基于NYUSIM生成的867.5 MHz数据集，采用CNN-LSTM分类器对无人机与目标之间的各链路进行标注。随后，这些标注会激活特定的定位求解器：视距（LOS）场景采用一阶泰勒展开的加权最小二乘（WLS）方法，非视距（NLOS）场景采用带迭代优化的LSRE-SOCP方法，混合环境则采用基于混合投影的方案。通过全面的仿真实验表明，所提框架可显著降低运行时间，且即使在发射功率和路径损耗条件未知的情况下，仍能实现高定位精度。该方法可适用于多种传播场景，便于实际部署。本文将NYUSIM生成的完整数据集公开，以支持未来面向灾害场景的无线定位研究。
英文摘要
The precise locating of trapped victims is arguably the most challenging issue in SAR operations, particularly when infrastructure is destroyed and SAR teams only have low-power beacon signals from smartphones to search with. This paper presents a framework for centralized and cooperative UAV-based localization with deep learning-based channel classification and 3D environment-adaptive target estimation. A CNN-LSTM classifier is employed based on a dataset generated at 867.5 MHz with NYUSIM. This classifier labels the individual links of a UAV to a target. Then, these labels activate specific localization solvers: a first-order Taylor-expanded WLS method for LOS settings, an LSRE-SOCP method with iterative refinement for NLOS, and a hybrid projection-based scheme for mixed environments. Through exhaustive simulations, this paper shows that the proposed framework significantly reduces runtime and yields high localization accuracy even when the transmit power and path-loss conditions are unknown. The method can be used in diverse propagation scenarios, making it easy to deploy in reality. The whole NYUSIM-generated dataset is made publicly available to support future research on disaster-aware wireless localization.
发表机构
- University of California, Santa Cruz（加州大学圣克鲁兹分校）
机构由 AI 辅助整理，请以论文原文为准。
