MEMS Based Pedestrian Navigation System

In this paper we present a micro-electrical mechanical system (MEMS) based pedestrian navigation system (PNS) for seamless positioning. The sub-algorithms for the PNS are developed and the positioning performance is enhanced using the modified receding horizon Kalman finite impulse response filter (MRHKF). The PNS consists of a biaxial accelerometer and a biaxial magnetic compass mounted on a shoe. The PNS detects a step using a novel technique during the stance phase and simultaneously calculates walking information. Step length is estimated using a neural network whose inputs are the walking information. The azimuth is calculated using the magnetic compass, the walking information and the tilt compensation algorithm. Using the proposed sub-algorithms, seamless positioning can be accomplished. However, the magnetic compass based azimuth may have an error that varies according to the surrounding magnetic field. In this paper, the varying error is compensated using the MRHKF filter. Finally, the performance enhanced seamless positioning is achieved, and the performance is verified by experiment.

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Pedestrian Navigation System with Trinal-IMUs for Drastic Motions

Sensors ◽

10.3390/s20195570 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5570

Author(s):

Yiming Ding ◽

Zhi Xiong ◽

Wanling Li ◽

Zhiguo Cao ◽

Zhengchun Wang

Keyword(s):

Navigation System ◽

Inertial Sensor ◽

Estimation Error ◽

Simultaneous Localization And Mapping ◽

Step Length ◽

Mapping Method ◽

Zero Crossing ◽

Pedestrian Navigation ◽

Length Estimation ◽

Localization And Mapping

The combination of biomechanics and inertial pedestrian navigation research provides a very promising approach for pedestrian positioning in environments where Global Positioning System (GPS) signal is unavailable. However, in practical applications such as fire rescue and indoor security, the inertial sensor-based pedestrian navigation system is facing various challenges, especially the step length estimation errors and heading drift in running and sprint. In this paper, a trinal-node, including two thigh-worn inertial measurement units (IMU) and one waist-worn IMU, based simultaneous localization and occupation grid mapping method is proposed. Specifically, the gait detection and segmentation are realized by the zero-crossing detection of the difference of thighs pitch angle. A piecewise function between the step length and the probability distribution of waist horizontal acceleration is established to achieve accurate step length estimation both in regular walking and drastic motions. In addition, the simultaneous localization and mapping method based on occupancy grids, which involves the historic trajectory to improve the pedestrian’s pose estimation is introduced. The experiments show that the proposed trinal-node pedestrian inertial odometer can identify and segment each gait cycle in the walking, running, and sprint. The average step length estimation error is no more than 3.58% of the total travel distance in the motion speed from 1.23 m/s to 3.92 m/s. In combination with the proposed simultaneous localization and mapping method based on the occupancy grid, the localization error is less than 5 m in a single-story building of 2643.2 m2.

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Real-time accurate pedestrian tracking using extended finite impulse response filter bank for tightly coupling recent inertial navigation system and ultra-wideband measurements

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651818754977 ◽

2018 ◽

Vol 232 (4) ◽

pp. 464-472 ◽

Cited By ~ 3

Author(s):

Yuan Xu ◽

Hamid Reza Karimi ◽

Yueyang Li ◽

Fengyu Zhou ◽

Lili Bu

Keyword(s):

Impulse Response ◽

Navigation System ◽

Filter Bank ◽

Inertial Navigation System ◽

Finite Impulse Response ◽

Inertial Navigation ◽

Finite Impulse Response Filter ◽

Ultra Wideband ◽

Pedestrian Tracking ◽

Impulse Response Filter

To satisfy the increasing demands of the accuracy for the human localization, in this work, we propose a pedestrian tracking method by tightly coupling recent inertial navigation system–based and ultra-wideband–based measurements. In this mode, the difference between the distances derived from the inertial navigation system–based and ultra-wideband–based system is used as the observation of the data fusion filter. Moreover, in order to improve the performance of the extended finite impulse response filter, which depends on the averaging horizon ([Formula: see text]) when the error state vector ([Formula: see text]) is determined due to the model, the extended finite impulse response filter bank is employed to be the fusion center for pedestrian tracking, which used the Mahalanobis distance to find the optimal [Formula: see text] at each time index [Formula: see text]. Test experiments illustrate that the extended finite impulse response filter bank–based tightly coupled inertial navigation system/ultra-wideband–integrated method is able to achieve real-time estimation, and its accuracy is similar to the extended finite impulse response with the ideal [Formula: see text] which is calculated off-line.

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A Real-Time GNSS/PDR Navigation System for Mobile Devices

Remote Sensing ◽

10.3390/rs13081567 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1567

Author(s):

Michele Basso ◽

Alessio Martinelli ◽

Simone Morosi ◽

Fabrizio Sera

Keyword(s):

Energy Saving ◽

Navigation System ◽

Dead Reckoning ◽

Step Length ◽

The Body ◽

Global Navigation Satellite Systems ◽

Motion Direction ◽

Trade Off ◽

Pedestrian Navigation ◽

Position Accuracy

In this article, a smart pedestrian navigation system is developed to be implemented in a common smartphone. The main phases that characterize a pedestrian navigation system that is based on dead reckoning are introduced. A suitable Phase-Locked Loop is designed and the algorithm to estimate the direction of the user’s motion between one step and the next is developed. Finally, a suitable multi-rate Kalman filter (KF) is considered to merge the information from the pedestrian dead reckoning (PDR) navigation with the data provided by the global navigation satellite systems (GNSS). The proposed GNSS/PDR navigation system is implemented in Simulink as a finite-state machine and allows to define a trade-off between energy-saving and performance improvement in terms of position accuracy. The presented pedestrian navigation system is independent of the body-worn location of the smartphone and implements a compensation strategy of the systematic errors that are committed on the step-length estimation and the determination of the motion direction. Moreover, several tests are performed by walking in urban and suburban environments: the results show that a suitable trade-off between energy-saving and position accuracy can be reached by switching the GNSS receiver on and off.

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Deep neural network‐based adaptive zero‐velocity detection for pedestrian navigation system

Electronics Letters ◽

10.1049/ell2.12339 ◽

2021 ◽

Author(s):

Liqiang Zhang ◽

Boxuan Chen ◽

Hu Li ◽

Yu Liu

Keyword(s):

Neural Network ◽

Navigation System ◽

Deep Neural Network ◽

Pedestrian Navigation ◽

Zero Velocity

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Robust Self-Contained Pedestrian Navigation by Fusing the IMU and Compass Measurements via UFIR Filtering

Journal of Electrical and Computer Engineering ◽

10.1155/2018/8401967 ◽

2018 ◽

Vol 2018 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Meng Hou ◽

Yuan Xu ◽

Xiao Liu

Keyword(s):

Inertial Measurement Unit ◽

Stance Phase ◽

Finite Impulse Response ◽

Measurement Unit ◽

Test Results ◽

Robust Performance ◽

Pedestrian Navigation ◽

Noise Statistics ◽

Navigation Scheme ◽

Real Test

In order to overcome the poor observability of yaw measurement for foot-mounted inertial measurement unit (IMU), an integrated IMU+Compass scheme for self-contained pedestrian navigation is presented. In this mode, the compass measurement is used to provide the accurate yaw to improve the accuracy of the attitude transformation matrix for the foot-mounted IMU solution. And then, when the person is in a stance phase during walk, a unbiased finite impulse response (UFIR) filter based on the self-contained pedestrian navigation scheme is investigated, which just needs the state vector size MU and the filtering horizon size NU, while ignoring the noise statistics compared with the Kalman filter (KF). Finally, a real test has been done to verify the performance of the proposed self-contained pedestrian navigation using the IMU and compass measurements via UFIR filter. The test results show that the proposed filter has robust performance compared with the KF.

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A Novel Pedestrian Dead Reckoning Algorithm for Multi-Mode Recognition Based on Smartphones

Remote Sensing ◽

10.3390/rs11030294 ◽

2019 ◽

Vol 11 (3) ◽

pp. 294 ◽

Cited By ~ 8

Author(s):

Limin Xu ◽

Zhi Xiong ◽

Jianye Liu ◽

Zhengchun Wang ◽

Yiming Ding

Keyword(s):

Neural Network ◽

Estimation Method ◽

Dead Reckoning ◽

Step Length ◽

Correction Method ◽

Step Detection ◽

Pedestrian Navigation ◽

Length Estimation ◽

Multi Mode ◽

Pedestrian Dead Reckoning

With the rapid development of smartphone technology, pedestrian navigation based on built-in inertial sensors in smartphones shows great application prospects. Currently, most smartphone-based pedestrian dead reckoning (PDR) algorithms normally require a user to hold the phone in a fixed mode and, thus, need to correct the gyroscope heading with inputs from other sensors, which restricts the viability of pedestrian navigation significantly. In this paper, in order to improve the accuracy of the traditional step detection and step length estimation method for different users, a state transition-based step detection method and a step length estimation method using a neural network are proposed. In order to decrease the heading errors and inertial sensor errors in multi-mode system, a multi-mode intelligent recognition method based on a neural network was constructed. On this basis, we propose a heading correction method based on zero angular velocity and an overall correction method based on lateral velocity limitation (LV). Experimental results show that the maximum positioning errors obtained by the proposed algorithm are about 0.9% of the total path length. The proposed novel PDR algorithm dramatically enhances the user experience and, thus, has high value in real applications.

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