Research on the Forward and Reverse Calculation Based on the Adaptive Zero-Velocity Interval Adjustment for the Foot-Mounted Inertial Pedestrian-Positioning System

Wang, Qiuying; Guo, Zhi; Sun, Zhiguo; Cui, Xufei; Liu, Kaiyue

doi:10.3390/s18051642

Cited by 23 publications

(11 citation statements)

References 26 publications

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“…Therefore, in order to make ZUPT algorithm work effectively in improving the performance of foot-mounted pedestrian navigation system and meet the needs of pedestrian navigation system in different environments, we need to find a zero velocity detection method with higher environmental adaptability and robustness. In the process of walking, due to physiological characteristics, the foot movement state relative to the ground is in a process of dynamic and static alternation [7,8,10]. In order to analyze the characteristics of foot movement more clearly, we filmed the complete movement state of foot during walking with a camera.…”

Section: Problem Analysismentioning

confidence: 99%

“…The foot-mounted pedestrian navigation technology based on human walking gait is a hot topic for many scholars [3][4][5][6][7].The core method is to suppress the error divergence of MIMU devices and improve the accuracy of pedestrian positioning by identifying the movement state of foot in walking and taking advantage of the characteristic that the sole of foot is stationary on the ground. This is also a common method named ZUPT in pedestrian positioning [8][9][10][11][12]. However, the effective use of this method is based on the premise of accurate and timely judgment of human walking gait characteristics.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Zero Velocity Detection Method Based on the Plantar Pressure

Wang¹,

Guo²,

Wu³

et al. 2020

Preprint

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The zero velocity update(ZUPT) algorithm is the core of a foot-mounted pedestrian navigation system. The zero velocity detection method is the premise and guarantee of the effective application of the ZUPT algorithm. To make ZUPT work properly, it is necessary to detect zero velocity intervals correctly. The detection accuracy of the existing zero velocity detection methods is easy to be affected by users and environment. A novel zero velocity detection method based on the plantar pressure is proposed in this paper, which has higher detection accuracy and better environmental adaptability. First, the paper analyzes the motion characteristics of foot during walking. Second, the inherent relationship between the plantar pressure and the gait change during walking is studied based on the pressure sensor. Then, the model of the zero velocity detection method using the plantar pressure is established. Finally, the indoor and outdoor multi-scene experiments show that this method not only has a high detection accuracy, but also has good adaptability to users and walking environment.

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Section: Problem Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Zero Velocity Detection Method Based on the Plantar Pressure

Wang¹,

Guo²,

Wu³

et al. 2020

Preprint

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“…The FIPP consists of two parts: Trajectory Calculating (Section 2.1.1) and the ZUPT algorithm (Section 2.1.2). The FIPP is shown in Figure 1 [35].…”

Section: Principle Of Fippmentioning

confidence: 99%

“…In theory, the heading can be obtained using the magnetic field strength measurement. However, there is a measurement deviation in the magnetometer due to the presence of magnetic interference, resulting in an inaccurate conversion heading, which makes it a sub-optimal solution to be used as a reference to locate the misalignment angle [35]. Many papers are devoted to solving the magnetic interference problem and have obtained optimized results [36,37].…”

Section: Introductionmentioning

confidence: 99%

Research on the Heading Calibration for Foot-Mounted Inertial Pedestrian-Positioning System Based on Accelerometer Attitude

et al. 2019

Self Cite

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Foot-mounted inertial pedestrian positioning (FIPP) plays an important role for facilitating pedestrian activities. It is suitable for indoor environment applications where global navigation satellite systems are unavailable such as during firefighting and military actions. However, the positioning error of FIPP can increase rapidly due to the measurement noise of the sensors. Zero Velocity Update (ZUPT) is an error correction method proposed to solve this accumulative error. However, the heading misalignment angle, which results in a continuous increase in the positioning error, cannot be estimated by ZUPT. In order to solve this problem, the improved ZUPT based on the Improved Attitude Algorithm (IAA) according to accelerometer measurements is proposed in this paper. When a pedestrian is in the stance phase, the horizontal attitude is estimated by using accelerometer measurements. According to the relationship between the heading misalignment angle and horizontal attitude, the heading misalignment angle is obtained by a series of mathematical derivations. By taking the velocity error and the attitude misalignment angle as observations, the heading misalignment angle and positioning error can be estimated and compensated for through the Kalman filter. Finally, we use MTI-G710 sensor manufactured by XSENS for the actual test and the experiment results show that the proposed method is effectively correct.

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“…e core of this method is to suppress the attitude and position errors in the standing phase. An extended Kalman filter (EKF) assists the pedestrian location of ZUPT to compensate for the errors caused by the integral characteristics of acceleration and angular velocity through observation [15][16][17]. However, the accuracy of this method is affected by the dimension of the state variables and cannot accurately estimate the attitude and position.…”

Section: Introductionmentioning

confidence: 99%

Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter

Liu

et al. 2020

Complexity

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The Foot-mounted Inertial Pedestrian-Positioning System (FIPPS) based on the Micro-Inertial Measurement Unit (MIMU) is a good choice for the forest fire fighters when the Global Navigation Satellite System is unavailable. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial navigation. However, the performance of ZUPT is highly affected by the low accuracy and high noise of the MIMU. The accuracy of conventional ZUPT for attitude alignment is reduced by the zero offset of acceleration and the drift of a gyroscope during the standing phase. An initial alignment algorithm based on Adaptive Gradient Descent Algorithm (AGDA) is proposed. In the stepping phase, the extended Kalman filter (EKF) is often used to correct attitude and position in track estimation. However, the measurement noise of the EKF is influenced by the high-frequency acceleration and angular velocity. Thus, the accuracy of the attitude and position will decrease. A double-constrained extended Kalman filtering (DEKF) is proposed. An adaptive parameter positively correlated with the acceleration and angular velocity is set, and the measurement noise in the DEKF is adaptively adjusted. The performance of the proposed method is verified by implementing the pedestrian test trajectory using MPU-9150 MIMU manufactured by InvenSense. The results show that the attitude error of the AGDA is 33.82% less than that of the conventional GDA. The attitude error of DEKF is 21.70% less than that of the conventional EKF. The experimental results verify the effectiveness and applicability of the proposed method.

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Research on the Forward and Reverse Calculation Based on the Adaptive Zero-Velocity Interval Adjustment for the Foot-Mounted Inertial Pedestrian-Positioning System

Cited by 23 publications

References 26 publications

A Novel Zero Velocity Detection Method Based on the Plantar Pressure

A Novel Zero Velocity Detection Method Based on the Plantar Pressure

Research on the Heading Calibration for Foot-Mounted Inertial Pedestrian-Positioning System Based on Accelerometer Attitude

Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter

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