Abstract: For wearable pedestrian strapdown inertial navigation and location devices, the different devices need different pedestrian dead reckoning (PDR) parameters, and the parameters are not such the optimal value that it affects the accuracy. A self-pedestrian navigation and location method based on the 12-dimensional zero-velocity state update intelligent algorithm is proposed, in which three dimensional errors of velocity, angular speed, location and geomagnetism are introduced as the system observations and an intelligent estimator which is formed by the support vector machine (SVM) and Kalman filter is established to estimate the system state error, and therefore the system accuracy is improved. By the experimental verification with using the self-developed IMU sensor, the results prove that this method observes system status effectively and estimates system errors intelligently. Comparing with the traditional ZUPT, the proposed method can reduce the horizontal error by an average of 40% and the spatial error by an average of 45%.

Key words: SINS, wearable, pedestrian self-navigation, IMU, PDR