College of Intelligent System Science and Engineering, Harbin Engineering University, Harbin 150001, China.
Beijing Institute of Control and Electronic Technology, Beijing 100032, China.
Sensors (Basel). 2021 Apr 3;21(7):2500. doi: 10.3390/s21072500.
In recent years, the application of deep learning to the inertial navigation field has brought new vitality to inertial navigation technology. In this study, we propose a method using long short-term memory (LSTM) to estimate position information based on inertial measurement unit (IMU) data and Global Positioning System (GPS) position information. Simulations and experiments show the practicability of the proposed method in both static and dynamic cases. In static cases, vehicle stop data are simulated or recorded. In dynamic cases, uniform rectilinear motion data are simulated or recorded. The value range of LSTM hyperparameters is explored through both static and dynamic simulations. The simulations and experiments results are compared with the strapdown inertial navigation system (SINS)/GPS integrated navigation system based on kalman filter (KF). In a simulation, the LSTM method's computed position error Standard Deviation (STD) was 52.38% of what the SINS computed. The biggest simulation radial error estimated by the LSTM method was 0.57 m. In experiments, the LSTM method computed a position error STD of 23.08% using only SINSs. The biggest experimental radial error the LSTM method estimated was 1.31 m. The position estimated by the LSTM fusion method has no cumulative divergence error compared to SINS (computed). All in all, the trained LSTM is a dependable fusion method for combining IMU data and GPS position information to estimate position.
近年来，深度学习在惯性导航领域的应用为惯性导航技术带来了新的活力。在本研究中，我们提出了一种使用长短时记忆网络（LSTM）基于惯性测量单元（IMU）数据和全球定位系统（GPS）位置信息来估计位置信息的方法。仿真和实验表明，该方法在静态和动态情况下都具有实用性。在静态情况下，模拟或记录车辆停止数据。在动态情况下，模拟或记录均匀直线运动数据。通过静态和动态仿真探索了 LSTM 超参数的取值范围。将仿真和实验结果与基于卡尔曼滤波（KF）的捷联惯性导航系统（SINS）/GPS 组合导航系统进行了比较。在仿真中，LSTM 方法计算的位置误差标准偏差（STD）是 SINS 计算的 52.38%。LSTM 方法估计的最大仿真径向误差为 0.57 米。在实验中，LSTM 方法仅使用 SINS 计算出的位置误差 STD 为 23.08%。LSTM 方法估计的最大实验径向误差为 1.31 米。与 SINS（计算）相比，LSTM 融合方法估计的位置没有累积发散误差。总之，经过训练的 LSTM 是一种可靠的融合方法，可用于结合 IMU 数据和 GPS 位置信息来估计位置。
