摘要： 多运动模式行人航位推算算法采用零速校正消除累计误差，但零速校正错判会导致误差。针对该问题，提出一种基于长短时记忆网络的自适应零速检测算法。构建长短时记忆网络提取不同运动模式下零速区间的三轴加速度、三轴角速度和时序特征，通过优化零速检测算法实现多运动模式下的自适应零速检测，在此基础上采用差分进化算法融合蓝牙等定位方式对累积误差进行数据修正。实验结果表明，与传统卡尔曼滤波算法相比，本文算法定位绝对误差由6.43 m减少到0.94 m，相对误差由1.246%降至0.182%。

关键词: 行人航位推算, 长短时记忆网络, 零速检测, 自适应算法, 差分进化算法

Abstract: Zero velocity correction is used to eliminate the cumulative error inthe multi-mode Pedestrian Dead Reckoning(PDR) algorithm.To solve the problem,this paper proposes an adaptive zero velocity detection algorithm based on Long Short-Term Memory(LSTM) network.An LSTM network is constructed to extract the triaxial acceleration,triaxial angular velocity and time sequence characteristics of zero velocity interval under different motion modes.The adaptive zero velocity detection is realized by optimizing the zero velocity detection algorithm.On this basis,the Differential Evolution(DE) Algorithm is used to fuse positioning methods such as Bluetooth to correct the accumulated errors.Experimental results show that compared with the traditional Kalman filter algorithm,the proposed algorithm reduces the absolute positioning error from 6.43 m to 0.94 m,and the relative error from 1.246% to 0.182%.

Key words: Pedestrian Dead Reckoning(PDR), Long Short-Term Memory(LSTM)network, zero velocity detection, adaptive algorithm, Differential Evolution(DE) algorithm

中图分类号: 

• TP212.9