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Aiming at the inherent problems of heading divergence and error accumulation in indoor inertial positioning of pedestrian field, we proposed a new indoor positioning error correction method for pedestrian multi-motions recognition. This method is aimed at pedestrians’ seven common indoor movements. Periodically, divide the motion data of the accelerometer in the sensitive-axis-direction (vertical geodetic direction) of MEMS-IMU worn on the pedestrian’s waist. And acquire the feature vector through feature extraction by the hybrid-orders fraction domain transformation. The effective features with high identification degree under the different optimal order transformations are mixed and matched. Then, these are sent to each sub-classifier to complete the classification process by the dichotomy and finish the processes of subsequent machine learning. The error correction of the heading angle and positioning is carried out combining with the improved HDE algorithm, the innovative floor constraints’ method, and the motion states’ transition correction technique. The final indoor positioning experiment results show that the classification effect of this motion detection method is better than the traditional methods’. The average classification accuracy can reach up to 97%. And the method reduces the computing requirements for hardware. By using the floor constraint method, the vertical height difference can achieve the effect of complete reset of the origin. The trajectory lines of the movement in the same floor are well displayed. At the same time, the best horizontal error positioning result is only 1.72 m during the total travel distance of 412.40 m (TTD ≈ 0.42%).

An Indoor Positioning Error Correction Method of Pedestrian Multi-Motions Recognized by Hybrid-Orders Fraction Domain Transformation