Adaptive Multi-Sensor Data Fusion Positioning Algorithm Based On Long Short Term Memory Neural Networks

Abstract

In the indoor Non-Line-Of-Sight (NLOS) positioning scenes, improve the accuracy and robustness of positioning can be the main challenge. To solve this challenge, an adaptive algorithm based on long short-term memory neural networks (LSTM) modified multi-sensor data fusion positioning is proposed by us. First, we compare the single sensor and multi-sensor data fusion methods to show the superiority of multi-sensor data fusion, which determined our next research. Then, in multi-sensor data fusion scene, in order to reduce inertial measurement unit (IMU) cumulative errors and ultra-wide band (UWB) measured errors, LSTM is used for data modeling and error prediction based on the measured values of IMU and UWB. Finally, on the foundation of the prediction results, the accuracy of IMU and UWB fusion positioning is enhanced by adjusting the coefficients. Experimental results reveal that the algorithm of adaptive adjusting the coefficients by LSTM reduces the root mean square error (RMSE) and average error contrasted with the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF).