全球定位系统2025，Vol.50Issue(5)：41-50,10.DOI:10.12265/j.gnss.2025074
深度学习优化的智能手机运动状态识别与自适应非完整性约束车载导航
Deep learning-assisted smartphone motion state recognition and adaptive non-holonomic constraint for vehicle navigation
摘要
Abstract
Under GNSS-denied environments,the inertial navigation of smartphones faces significant cumulative errors.Traditional non-holonomic constraints(NHC)are also ineffective during dynamic maneuvers such as turns.To address these issues,this paper proposes a deep learning-assisted NHC enhancement method.This approach utilizes a fusion model of a one-dimensional convolutional residual network(ResNet-1D)and a long short-term memory(LSTM)network to accurately recognize vehicle straight and turning states from smartphone low cost micro-electro-mechanical system(MEMS)inertial measurement unit(IMU)data.This recognition enables dynamic adjustment of the observation noise covariance in the NHC of the extended Kalman filter(EKF),strengthening constraints during straight driving and relaxing them during turns.Experimental results show that the proposed state recognition model improves turning recognition accuracy from about 65%to approximately 95%.Furthermore,real-world test data demonstrate that,under GNSS-denied conditions,the method achieves a 36.4%to 74.6%improvement in horizontal relative positioning accuracy compared to the traditional Z-axis gyroscope integration method.This study enhances smartphone navigation performance in complex environments through intelligent motion state recognition and adaptive NHC adjustment,offering a new approach for low-cost,high-precision positioning.关键词
手机车载导航/深度学习/车辆运动状态识别/非完整性约束(NHC)/低成本微机电系统(MEMS)/组合导航Key words
smartphone vehicle navigation/deep learning/vehicle motion state recognition/NHC/low-cost MEMS/integrated navigation分类
测绘与仪器引用本文复制引用
卢梓豪,冯译苇,栗广才..深度学习优化的智能手机运动状态识别与自适应非完整性约束车载导航[J].全球定位系统,2025,50(5):41-50,10.基金项目
国家自然科学基金(42204021) （42204021）
国家重点研发计划(2021YFC3000500) （2021YFC3000500）
国家自然科学基金国际合作研究与交流项目(42361134580,42311530062) （42361134580,42311530062）
