Improved Pedestrian Location Method for the Indoor Environment Based on MIMU and sEMG Sensors

Abstract

In order to guarantee the pedestrian location accuracy for the indoor environment, an improved pedestrian location method based on the improved Mahony complementary filter algorithm is proposed by the data fusion of microinertial measurement unit (MIMU) and sEMG sensors, which are installed on the lower limbs. First, the pedestrian location method for the indoor environment is introduced by the data fusion of MIMU and surface electromyography; and then, BP neural network is applied to adjust the parameters in real time for PI controller of Mahony complementary filter algorithm, which can obtain the current heading angle for the location. Finally, the indoor environment experiments for pedestrian location are carried out. The heading angle estimating experiment result shows that the indoor heading angle for the pedestrian can be exactly calculated by the improved Mahony complementary filter algorithm. The indoor location experiment shows that the average relative location error is 2.34 m, and the average relative location error rate is 2.27%.