Use of Magnetic Field for Mitigating Gyroscope Errors for Indoor Pedestrian Positioning

Ma, Ming; Song, Qian; Gu, Yu; Zhou, Zhimin

doi:10.3390/s18082592

Cited by 12 publications

(9 citation statements)

References 32 publications

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“…where, Z AR k = ψ k that can be calculated by Equation 32, H AR k = 1 0 0 0 1×4 . In this paper, Equations (38) and (39) were constructed to predict and update the KF for heading estimation.…”

Section: The Filter Design Of Kf Based On Ar Model For Heading Estimamentioning

confidence: 99%

“…The geomagnetic field suffers severe perturbations in indoor environments, causing the magnetic field magnitude to not be constant. However, locations are possible where these magnetic field magnitudes are constant, and these situations can be considered as QS [38]. The heading calculated by magnetic field intensity in QS can achieve an accurate result.…”

Section: The Detection Of a Qs Magnetic Fieldmentioning

confidence: 99%

“…In this paper, the position estimation is solved based on the UKF that reference the method proposed in [38]. After the heading is updated and the step-length is estimated, these values can be used to estimate the position based on the UKF.…”

Section: Filter Design Of Ukf For Position Estimationmentioning

confidence: 99%

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A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

2019

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With the popularity of smartphones and the development of microelectromechanical system (MEMS), the pedestrian dead reckoning (PDR) algorithm based on the built-in sensors of a smartphone has attracted much research. Heading estimation is the key to obtaining reliable position information. Hence, an adaptive Kalman filter (AKF) based on an autoregressive model (AR) is proposed to improve the accuracy of heading for pedestrian dead reckoning in a complex indoor environment. Our approach uses an autoregressive model to build a Kalman filter (KF), and the heading is calculated by the gyroscope, obtained by the magnetometer, and stored by previous estimates, then are fused to determine the measurement heading. An AKF based on the innovation sequence is used to adaptively adjust the state variance matrix to enhance the accuracy of the heading estimation. In order to suppress the drift of the gyroscope, the heading calculated by the AKF is used to correct the heading calculated by the outputs of the gyroscope if a quasi-static magnetic field is detected. Data were collected using two smartphones. These experiments showed that the average error of two-dimensional (2D) position estimation obtained by the proposed algorithm is reduced by 40.00% and 66.39%, and the root mean square (RMS) is improved by 43.87% and 66.79%, respectively, for these two smartphones.

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Section: The Filter Design Of Kf Based On Ar Model For Heading Estimamentioning

confidence: 99%

Section: The Detection Of a Qs Magnetic Fieldmentioning

confidence: 99%

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A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

2019

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“…To suppress the heading drift, magnetic field is often used as a stable orientation reference. But the magnetic field inside buildings changes rapidly while space and time changes, and it is also sensitive to some metal objects or electrical equipment [16], [17]. As most buildings have regular structures, researchers proposed to use corridor's directions in buildings as constraints, which has been proved to be an effective method for the heading drift of INS (Inertial Navigation System)-based PDR system.…”

Section: Introductionmentioning

confidence: 99%

The Improved Constraint Methods for Foot-Mounted PDR System

Zhang

Yuan

2020

IEEE Access

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The Zero-velocity Update (ZUPT) aided Extended Kalman Filter (EKF) is commonly used in the classical INS-based foot-mounted PDR (Pedestrian Dead Reckoning) system. However, in the realistic test, the system still often suffers from drift, which is mainly caused by two reasons: failed detection of the stationary phase in the dynamic pedestrian gait and the heading drift which is a poorly observable variable of the ZUPT method. In this paper, in order to overcome these problems, three improved constraint algorithms have been proposed respectively for the detection of the stationary phase, constraint of heading drift and constraint of the height divergence. Firstly, for the problem of failed detection of the stationary phase, a novel stationary phase detection method is proposed which mainly by finding the minimum detector T in each gait cycle to determine the stationary phase, rather than totally based on threshold comparison principle in the traditional method. Comparing with the traditional method, the proposed method can detect the stationary phase of each gait cycle accurately under various pedestrian movements. Secondly, for the heading divergence problem, an improved method is proposed based on the existing HDE (Heuristic Drift Elimination) method, which uses the position error rather than heading error to restrain the trajectory divergence. Comparing with the traditional method, the proposed method can better constrain the heading to the correct angle. At last, for the problem of height divergence, an effective method has been proposed to determine the state of pedestrians by using the slope between adjacent one/several footsteps. At the same time, the slope of the plane and stairs is used to restrict the height divergence. In experiment, the proposed method can be more effectively to distinguish the pedestrian's state. Especially, when pedestrian walks on the stairs, the slope of the stairs can constrain the height divergence obviously.

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“…Fingerprint positioning and localization have the advantages of simple construction, low cost, easy implementation, and low maintenance, which is a universal method for positioning and localization based on MF, Wi-Fi, and Bluetooth. Today, there have been many studies on MF fingerprint [ 14 , 15 ] positioning and localization [ 16 , 17 ], Wi-Fi fingerprint positioning and localization [ 18 , 19 ], as well as Bluetooth fingerprint positioning and localization [ 20 , 21 ], but studies on the integration [ 22 , 23 ] of MF, Wi-Fi and Bluetooth fingerprints for positioning and localization have been comparatively few. Nevertheless, all the MF, Wi-Fi, and Bluetooth signals are present in certain indoor environments, such as office buildings, supermarkets, warehouses.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor

Yuan

Zha

Zhang

2020

Sensors

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The complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight Coefficients K-Nearest Neighbor (WCKNN), which integrates magnetic field, Wi-Fi and Bluetooth fingerprints for positioning and localization. The MTL fuses the features of different types of fingerprints to search the potential relationship between them. It also exploits the synergy between the tasks, which can boost up positioning and localization performance. Then the WCKNN predicts another position of the fingerprints in a certain class determined by the obtained location. The final position is obtained by fusing the predicted positions using a weighted average method whose weights are the positioning errors provided by positioning error prediction models. Experimental results indicated that the proposed method achieved 98.58% accuracy in classifying locations with a mean positioning error of 1.95 m.

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Use of Magnetic Field for Mitigating Gyroscope Errors for Indoor Pedestrian Positioning

Cited by 12 publications

References 32 publications

A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

The Improved Constraint Methods for Foot-Mounted PDR System

Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor

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