METHODS AND SYSTEMS FOR ADAPTIVE PEDESTRIAN INERTIAL NAVIGATION

Processes and systems for adaptive pedestrian inertial navigation are provided. Configurations can adjust to various navigation scenarios, including different floor types and different gait paces. A combination of IMU data partition, principal component analysis (PCA), and artificial neural network may be used to perform the floor type detection. Floor type results may be used in the multiple-model extended Kalman filter. In each extended Kalman filter, an adaptive threshold is used for the stance phase detection to enable the detector to adjust to gait frequency without tuning design parameters during navigation. A floor type classification of high accuracy is demonstrated, and the position error in a velocity-changing navigation system using adaptive threshold is reduced.