自动化学报2025，Vol.51Issue(2)：271-286,16.DOI:10.16383/j.aas.c240221
行人惯性定位新动态:基于神经网络的方法、性能与展望
Emerging Trends in Pedestrian Inertial Positioning:Neural Network-based Methods,Performance and Prospects
摘要
Abstract
Pedestrian inertial positioning(IP),which estimates a pedestrian's position through measurement se-quences from an inertial measurement unit(IMU),has become an important solution for pedestrian autonomous po-sitioning in indoor environments or areas with satellite signal blockages in recent years.However,traditional iner-tial positioning methods are prone to drift issues during double integration due to the influence of error sources,which to some extent limits the application of pedestrian inertial positioning in long-term,long-distance real-world motion.Fortunately,neural network(NN)-based methods can learn pedestrian motion patterns from historical IMU data and correct the drift caused by inertial measurement values during integration.Therefore,this paper presents a comprehensive review of recent developments in pedestrian inertial positioning based on deep neural network(DNN).First,a brief introduction to traditional inertial positioning methods is provided;Next,the latest research on end-to-end(ETE)neural inertial positioning methods and neural inertial positioning methods incorporating do-main knowledge is reviewed;Following that,the benchmark datasets and evaluation metrics for pedestrian inertial positioning are summarized,and the advantages and disadvantages of some representative methods are analyzed and compared;Finally,the key challenges and difficulties that need to be addressed in this field are summarized,and the critical challenges and development trends of pedestrian inertial positioning based on DNN are discussed,aiming to provide useful references for subsequent research.关键词
惯性测量单元/位置跟踪/神经网络/行人航位推算/自主导航/移动设备Key words
Inertial measurement unit(IMU)/position tracking/neural network(NN)/pedestrian dead reckoning(PDR)/autonomous navigation/mobile devices引用本文复制引用
李岩,施忠臣,侯燕青,戚煜华,谢良,陈伟,陈洪波,闫野,印二威..行人惯性定位新动态:基于神经网络的方法、性能与展望[J].自动化学报,2025,51(2):271-286,16.基金项目
国家自然科学基金(62332019,62076250),国家重点研发计划(2023YFF1203900,2020YFA0713502)资助Supported by National Natural Science Foundation of China(62332019,62076250)and National Key Research and Develop-ment Program of China(2023YFF1203900,2020YFA0713502) （62332019,62076250）
