A positioning system in the absence of GPS is important in establishing indoor directional guidance and localization. Inertial Measuring Units (IMUs) can be used to detect the movement of a pedestrian. In this paper, we present a three-dimensional (3D) indoor positioning system using foot mounted low cost Micro-Electro-Mechanical System (MEMS) sensors to locate the position and attitude of a person in 3D view, and to plot the path travelled by the person. The sensors include accelerometers, gyroscopes, and a barometer. The pedestrians motion information is collected by accelerometers and gyroscopes to achieve Pedestrian Dead-Reckoning (PDR) which is used to estimate the pedestrian's rough position. A zero velocity update (ZUPT) algorithm is developed to detect the standing still moment. A Kalman filter is combined with the ZUPT to eliminate non-linear errors in order to obtain accurate positioning information of a pedestrian. The information collected by the barometer is integrated with the accelerometer data to detect the altitude changes and to obtain accurate height information. The main contribution of this research is that the approach proposed fuses barometer and accelerometer in Kalman filter to obtain accurate height information, which has improved the accuracy at x axis and y axis. The proposed system has been tested in several simulated scenarios and real environments. The distance errors are around 1%, and the positioning errors are less than 1% of the total travelled distance. Results indicate that the proposed system performs better tha n other similar systems using the same low-cost IMUs.
In this paper, we present a smartphone-based hand-held indoor positioning system. The system collects data using the accelerometer, gyroscope and gravity virtual sensor sensors embedded in the smartphone. The accelerometer and gravity data are used to detect zero vertical speed and calculate the vertical displacement of each walking step, and then the Pythagorean Theorem is applied to calculate the step length of every step. Gyroscope data is used to estimate the direction angle. The step length and the direction angle of each step is combined to determine the coordinates of each step. A Kalman filter is used to reduce the vertical speed offset caused by accelerometer drift errors. The testing results show good performance of the proposed system.
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