A two-stage classification method for improved positioning using low-cost inertial sensors

Abstract

The two-stage subtractive clustering Takagi-Sugeno (2SC-TS) method is proposed which completely replaces the established method of inertial navigation with classification models. The classifiers are designed by the subtractive clustering algorithm, an unsupervised learning method. The accuracy of the trajectories is compared against three competitive data-driven methods on three independent experimental datasets. The results show how 2SC-TS generates trajectories with approximately 20% lower positional error compared with the single-stage version (SC-TS), and halves the error produced by competitive deep learning methods. The proposed method may help improve the positioning of people and robots carrying low-cost inertial sensors.