Abstract
Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.
Highlights
The expansion of location-based services (LBS) and applications has led to extensive interest in ubiquitous localization which may rely on widely used smart phones
In order to improve the tracking performances of pedestrian dead reckoning (PDR), a method based on robust adaptive Kalman filtering (RAKF) is proposed for heading estimation
The results demonstrate that our proposed is slightly slower than the the algorithms are implemented using C#, and the corresponding software runs on an 2.7 Hz Intel Core proposed RAKF improves the accuracy of heading estimation effectively
Summary
The expansion of location-based services (LBS) and applications has led to extensive interest in ubiquitous localization which may rely on widely used smart phones. Regarding estimation accuracy, KFs are considered as the better choices other than conventional CFs. Yuan et al [14] proposed a quaternion-based unscented Kalman filter (UKF) for heading estimation using a tiny multi-sensor system. Deng et al [4] proposed a quaternion-based EKF for heading estimation using smartphone-embedded sensors According to their tests, location trace deviated from the ground truth significantly after a turn of 180 degrees without the assistance of WiFi localization. Visible light positioning results are used as the measurements to correct the states estimated by the PDR method From their experiments, we find that the location trace produced by PDR diverges from the true path severely, but the result of hybrid algorithm is much better. In order to improve the tracking performances of PDR, a method based on robust adaptive Kalman filtering (RAKF) is proposed for heading estimation.
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