Abstract
Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value.
Highlights
An indoor pedestrian positioning system is a system for real-time access to pedestrian location information in an enclosed environment [1]
Indoor positioning technology is roughly categorized into wireless and inertial positioning technology
The errors of inertial navigation are unaffected by the external environment, but the inertial navigation system is prone to cumulative errors over an extended period of time
Summary
An indoor pedestrian positioning system is a system for real-time access to pedestrian location information in an enclosed environment [1]. Adaptive filtering pedestrian is running or jumping, the errors in the pedestrian attitude cannot be effectively offset algorithms have been adopted to reduce the drifts and errors, including the fuzzy logic adaptive and, in turn, affect the accuracy of the velocity and position measurements [28]. Filter of a single measurement can be selected and the error of slow change in the system cannot be algorithm, based on the simplified Sage–Husa adaptive filtering algorithm and the anti-outlier filter, effectively identified [34]. This paper is organized as follows: Section 2 begins by modeling, analyzing, and outliers, the covariance is adopted to judge divergence filtering, and and the reconstructing errors matching of the inertial sensors using waveletofvariance theactivation wavelet function decomposition is taken to weight the measurement vector.
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