A Zero Velocity Detection Algorithm Using Inertial Sensors for Pedestrian Navigation Systems

The paper focuses on pedestrian navigation with foot-mounted strapdown inertial navigation systems (SINS). Zero velocity updates (ZUPT) during the stance phase are commonly applied in such systems to improve the accuracy. Zero velocity data are processed by the extended Kalman filter (EKF). Zero velocity condition is written in two forms: in reference and body frames. The first form traditional for pedestrian navigation is shown to provide an inconsistent EKF. The second form provides a correct ZUPT algorithm, which is naturally written in so-called dynamic errors. The analyzed algorithm for data fusion from two SINS is based on the bound on foot-to-foot distance. It is shown how EKF inconsistency can be manifested, and how it can be avoided by proceeding back to dynamic errors. The results are obtained analytically using observability theory and covariance analysis.

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An Indoor Localization Method Based on the Combination of Indoor Map Information and Inertial Navigation with Cascade Filter

Journal of Sensors ◽

10.1155/2021/7621393 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yushuai Zhang ◽

Jianxin Guo ◽

Feng Wang ◽

Rui Zhu ◽

Liping Wang

Keyword(s):

Indoor Localization ◽

Inertial Navigation ◽

Detection Algorithm ◽

Position Estimation ◽

Position Error ◽

Indoor Navigation ◽

Bayesian Filter ◽

Pedestrian Navigation ◽

Zero Velocity ◽

Average Position

The specific objective of this study is to propose a low-cost indoor navigation framework with nonbasic equipment by combining inertial sensors and indoor map messages. The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions’ errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53 m compared to the ZUPT-aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54 m as compared to the ZUPT-aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real-world scenarios.

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A zero velocity intervals detection algorithm based on sensor fusion for indoor pedestrian navigation

2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) ◽

10.1109/itnec.2017.8284765 ◽

2017 ◽

Cited By ~ 8

Author(s):

Ming Ma ◽

Qian Song ◽

Yanghuan Li ◽

Zhimin Zhou

Keyword(s):

Sensor Fusion ◽

Detection Algorithm ◽

Pedestrian Navigation ◽

Zero Velocity

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Using Constraints for Shoe Mounted Indoor Pedestrian Navigation

Journal of Navigation ◽

10.1017/s0373463311000518 ◽

2011 ◽

Vol 65 (1) ◽

pp. 15-28 ◽

Cited By ~ 34

Author(s):

Khairi Abdulrahim ◽

Chris Hide ◽

Terry Moore ◽

Chris Hill

Keyword(s):

Kalman Filter ◽

Average Distance ◽

Low Cost ◽

Navigation Systems ◽

Pedestrian Navigation ◽

Zero Velocity ◽

Average Return ◽

Measurement Units ◽

Heading Estimation ◽

Mems Imu

Shoe mounted Inertial Measurement Units (IMU) are often used for indoor pedestrian navigation systems. The presence of a zero velocity condition during the stance phase enables Zero Velocity Updates (ZUPT) to be applied regularly every time the user takes a step. Most of the velocity and attitude errors can be estimated using ZUPTs. However, good heading estimation for such a system remains a challenge. This is due to the poor observability of heading error for a low cost Micro-Electro-Mechanical (MEMS) IMU, even with the use of ZUPTs in a Kalman filter. In this paper, the same approach is adopted where a MEMS IMU is mounted on a shoe, but with additional constraints applied. The three constraints proposed herein are used to generate measurement updates for a Kalman filter, known as ‘Heading Update’, ‘Zero Integrated Heading Rate Update’ and ‘Height Update’.The first constraint involves restricting heading drift in a typical building where the user is walking. Due to the fact that typical buildings are rectangular in shape, an assumption is made that most walking in this environment is constrained to only follow one of the four main headings of the building. A second constraint is further used to restrict heading drift during a non-walking situation. This is carried out because the first constraint cannot be applied when the user is stationary. Finally, the third constraint is applied to limit the error growth in height. An assumption is made that the height changes in indoor buildings are only caused when the user walks up and down a staircase. Several trials were shown to demonstrate the effectiveness of integrating these constraints for indoor pedestrian navigation. The results show that an average return position error of 4·62 meters is obtained for an average distance of 1557 meters using only a low cost MEMS IMU.

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A Reconstruction Filter for Saturated Accelerometer Signals Due to Insufficient FSR in Foot-mounted Inertial Navigation System

10.36227/techrxiv.16745839 ◽

2021 ◽

Author(s):

Chi-Shih Jao ◽

Andrei M. Shkel

Keyword(s):

Inertial Sensors ◽

Inertial Navigation ◽

Gaussian Process Regression ◽

Navigation Systems ◽

Analog Device ◽

Bias Drift ◽

Pedestrian Navigation ◽

Angular Velocities ◽

Reconstruction Filter ◽

Commercial Off The Shelf

In pedestrian inertial navigation, one possible placement of Inertial Measurement Units (IMUs) is on a footwear. This placement allows to limit the accumulation of navigation errors due to the bias drift of inertial sensors and is generally a preferable placement of sensors to achieve the highest precision of pedestrian inertial navigation. However, inertial sensors mounted on footwear experience significantly higher accelerations and angular velocities during regular pedestrian activities than during more conventional navigation tasks, which could exceed Full Scale Range (FSR) of many commercial-off-the-shelf IMUs, therefore degrading accuracy of pedestrian navigation systems. This paper proposes a reconstruction filter to mitigate localization error in pedestrian navigation due to insufficient FSR of inertial sensors. The proposed reconstruction filter approximates immeasurable accelerometer's signals with a triangular function and estimates the size of the triangles using a Gaussian Process regression. To evaluate performance of the proposed reconstruction filter, we conducted two series of indoor pedestrian navigation experiments with a VectorNav VN-200 IMU and an Analog Device ADIS16497-3 IMU. In the first series of experiments, forces experienced by the IMUs did not exceed the FSRs of the sensors, while in the second series, the forces surpassed the FSR of the VN-200 IMU and saturated the accelerometer's readings. The saturated readings reduced the accuracy of estimated positions using the VN-200 by 1.34× and 3.37× along horizontal and vertical directions. When applying our proposed reconstruction filter to the saturated measurements, the navigation accuracy was increased by 5% horizontally and 50% vertically, as compared to using unreconstructed signals.

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An Improved Bayesian Zero-Velocity Detection Algorithm for Pedestrian Navigation Based on MIMU

10.1109/ccdc52312.2021.9601671 ◽

2021 ◽

Author(s):

XiaoYu Zhang ◽

Shaowu Dai ◽

Hongde Dai ◽

WenJie Quau ◽

Yang Zhao

Keyword(s):

Detection Algorithm ◽

Pedestrian Navigation ◽

Zero Velocity

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An Adaptive Zero Velocity Detection Algorithm Based on Multi-Sensor Fusion for a Pedestrian Navigation System

Sensors ◽

10.3390/s18103261 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3261 ◽

Cited By ~ 19

Author(s):

Ming Ma ◽

Qian Song ◽

Yang Gu ◽

Yanghuan Li ◽

Zhimin Zhou

Keyword(s):

Sensor Fusion ◽

Navigation System ◽

Detection Algorithm ◽

Stair Climbing ◽

Detection Methods ◽

Recognition Method ◽

Pedestrian Navigation ◽

Zero Velocity ◽

Motion Modes ◽

Detection Approach

The zero velocity update (ZUPT) algorithm is an effective way to suppress the error growth for a foot-mounted pedestrian navigation system. To make ZUPT work properly, it is necessary to detect zero velocity intervals correctly. Existing zero velocity detection methods cannot provide good performance at high gait speeds or stair climbing. An adaptive zero velocity detection approach based on multi-sensor fusion is proposed in this paper. The measurements of an accelerometer, gyroscope and pressure sensor were employed to construct a zero-velocity detector. Then, the adaptive threshold was proposed to improve the accuracy of the detector under various motion modes. In addition, to eliminate the height drift, a stairs recognition method was developed to distinguish staircase movement from level walking. Detection performance was examined with experimental data collected at varying motion modes in real scenarios. The experimental results indicate that the proposed method can correctly detect zero velocity intervals under various motion modes.

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Enhanced Heuristic Drift Elimination with Adaptive Zero-Velocity Detection and Heading Correction Algorithms for Pedestrian Navigation

Sensors ◽

10.3390/s20040951 ◽

2020 ◽

Vol 20 (4) ◽

pp. 951 ◽

Cited By ~ 1

Author(s):

Ruihui Zhu ◽

Yunjia Wang ◽

Baoguo Yu ◽

Xingli Gan ◽

Haonan Jia ◽

...

Keyword(s):

Inertial Sensors ◽

Angular Rate ◽

Dead Reckoning ◽

Detection Accuracy ◽

Phase Detection ◽

Pedestrian Navigation ◽

Zero Velocity ◽

Accumulated Error ◽

Pedestrian Dead Reckoning ◽

Dominant Direction

As pedestrian dead-reckoning (PDR), based on foot-mounted inertial sensors, suffers from accumulated error in velocity and heading, an improved heuristic drift elimination (iHDE) with a zero-velocity update (ZUPT) algorithm was proposed for simultaneously reducing the error in heading and velocity in complex paths, i.e., with pathways oriented at 45°, curved corridors, and wide areas. However, the iHDE algorithm does not consider the changes in pedestrian movement modes, and it can deteriorate when a pedestrian walks along a straight path without a pre-defined dominant direction. To solve these two problems, we propose enhanced heuristic drift elimination (eHDE) with an adaptive zero-velocity update (AZUPT) algorithm and novel heading correction algorithm. The relationships between the magnitude peaks of the y-axis angular rate and the detection thresholds were established only using the readings of the three-axis accelerometer and the three-axis gyroscopic, and a mechanism for constructing temporary dominant directions in real time was introduced. Real experiments were performed and the results showed that the proposed algorithm can improve the still-phase detection accuracy of a pedestrian at different movement motions and outperforms the iHDE algorithm in complex paths with many straight features.

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Dual MIMU Pedestrian Navigation by Inequality Constraint Kalman Filtering

10.20944/preprints201611.0106.v1 ◽

2016 ◽

Author(s):

Wei Shi ◽

Yang Wang ◽

Yuanxin Wu

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Inertial Navigation ◽

Inequality Constraint ◽

Time Range ◽

Navigation Systems ◽

Inertial Navigation Systems ◽

Pedestrian Navigation ◽

External Assistance ◽

Constraint Method

The foot-mounted inertial navigation system is an important application of pedestrian navigation as it in principle does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial sensors with ZUPT (zero-velocity update) and range decomposition constraint perform better than in either single way. This paper recommends that the distance of separation between the position estimates of feet-mounted inertial navigation systems be restricted in the ellipsoidal constraint which relates to the maximum step and leg height. The performance of the proposed method is studied utilizing experimental data. The results indicate that the method can effectively correct the dual navigation systems’ position over the existing spherical constraint.

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