物联网学报2026，Vol.10Issue(2)：53-64,12.DOI:10.11959/j.issn.2096-3750.WLW25112
基于大语言模型的超宽带雷达多任务学习方法
UWB-LLM:ultra-wideband radar multi-tasking learning based on large language model
摘要
Abstract
Ultra-wideband(UWB)radar enables people counting and vital signs monitoring with wireless sensing.How-ever,existing methods rely on statistical features or lightweight neural networks,which have limited ability to migrate and generalize across multiple tasks.In view of the powerful cross-modal learning capability demonstrated by large lan-guage model(LLM),a UWB radar multi-task learning framework based on LLM was proposed.This approach success-fully mapped the temporal features of radar signals into the embedding space of LLM,and by integrating the mixture of experts(MoE)mechanism and low-rank adaptation(LoRA)strategy,UWB-LLM performed parameter-efficient fine-tuning(PEFT)for multi-task learning.Experiments were conducted on both self-collected and publicly available datasets for people counting task as well as respiration,electrocardiogram(ECG),and continuous blood pressure estimation tasks,respectively.Compared with the state-of-the-art algorithms,UWB-LLM achieves average accuracy improvements of 37.62%for people counting and 7.47%,18.16%,and 14.70%for the three vital sign estimation tasks.关键词
大语言模型/超宽带雷达/人数检测/生命体征监测/多任务学习/混合专家Key words
LLM/UWB radar/people counting/vital signs monitoring/multi-task learning/MoE分类
信息技术与安全科学引用本文复制引用
饶翀,姜夕康,郭嘉航,李蕾,张琳..基于大语言模型的超宽带雷达多任务学习方法[J].物联网学报,2026,10(2):53-64,12.基金项目
国家自然科学基金资助项目(No.61971056) The National Natural Science Foundation of China(No.61971056) （No.61971056）
