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

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.

scholarly journals Pedestrian Navigation System with Trinal-IMUs for Drastic Motions

Sensors ◽  
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.

MEMS Based Pedestrian Navigation System

2005 ◽  
Vol 59 (1) ◽  
pp. 135-153 ◽  
Author(s):  
Seong Yun Cho ◽  
Chan Gook Park
Keyword(s):  
Neural Network ◽  
Navigation System ◽  
Stance Phase ◽  
Finite Impulse Response ◽  
Step Length ◽  
Magnetic Compass ◽  
Pedestrian Navigation ◽  
Novel Technique ◽  
Seamless Positioning ◽  
Impulse Response Filter

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.

The Standing Calibration Method of MEMS Gyro Bias for Autonomous Pedestrian Navigation System

2016 ◽  
Vol 70 (3) ◽  
pp. 607-617 ◽  
Author(s):  
Yanshun Zhang ◽  
Xu Yang ◽  
Xiangming Xing ◽  
Zhanqing Wang ◽  
Yunqiang Xiong
Keyword(s):  
Navigation System ◽  
Angular Rate ◽  
Dead Reckoning ◽  
Calibration Method ◽  
Micro Electro Mechanical System ◽  
Human Motion ◽  
Pedestrian Navigation ◽  
Initial Stage ◽  
Angular Velocities ◽  
Gyro Bias

In a waist-worn Pedestrian Navigation System (PNS) based on Dead-Reckoning (DR), heading drift caused by Micro-Electro-Mechanical System (MEMS) gyro bias is an essential factor affecting DR accuracy. Considering the characteristics of pedestrian navigation and the poor bias repeatability of MEMS gyros, this paper presents a standing calibration method for MEMS gyro bias. The current gyro biases for each boot can be calibrated accurately in the initial stage before walking. Since the attitude angles calculated by the output data from magnetic sensor and accelerometers do not drift, this paper applies the reverse algorithm of attitude updating to calculate the angular velocities of human motion. Then the gyro biases at each moment can be acquired by subtraction operation between the measured angular velocities from gyros and the calculated angular velocities of human motion. Finally, in order to restrain the random error caused by sensor noise, the calculated biases in the initial stage are smoothed, and consequently the optimal estimate of current gyro biases after each boot can be obtained. Still and dynamic turntable experiments and a walking experiment are performed to compare and analyse the proposed method and the Zero Angular Rate Update (ZARU) method. Experimental results show that the proposed method can also calibrate the gyro bias accurately in the case of body swaying.

Electronic Compass Calibration in Pedestrian Navigation System

2012 ◽  
Vol 490-495 ◽  
pp. 1246-1250
Author(s):  
Xiao Juan Zhang ◽  
Xi Sheng Li ◽  
Yi Bo Feng
Keyword(s):  
Magnetic Field ◽  
Navigation System ◽  
Direct Method ◽  
Dead Reckoning ◽  
Pedestrian Navigation ◽  
Movement Characteristic ◽  
Calibration Algorithm ◽  
Heading Angle ◽  
Compass Calibration ◽  
Electronic Compass

In this paper, a kind of pedestrian navigation system (PNS) that based on Earth’s magnetic field is introduced, and the error of the build-in electronic compass is analyzed, and an efficient calibration algorithm is presented. The PNS is determined pedestrian’s movement locus by calculating the heading angle and analyzing the movement characteristic, and then using the dead reckoning algorithm to combine the information together. The precision of PNS is affected by the error of the electric compass, because the heading angle is calculated from the magnetic field data measured by the compass. In order to reduce the measure error, a direct method which is used to calibrate the compass, based on ellipsoid fitting, is developed.

scholarly journals Combining a Modified Particle Filter Method and Indoor Magnetic Fingerprint Map to Assist Pedestrian Dead Reckoning for Indoor Positioning and Navigation

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 185
Author(s):  
Fang-Shii Ning ◽  
Yu-Chun Chen
Keyword(s):  
Magnetic Field ◽  
Particle Filter ◽  
Dead Reckoning ◽  
Step Length ◽  
Indoor Positioning ◽  
Global Navigation Satellite Systems ◽  
Filter Method ◽  
Indoor Environments ◽  
Positioning Accuracy ◽  
Pedestrian Dead Reckoning

Although advancement has been observed in global navigation satellite systems and these systems are widely used, they cannot provide effective navigation and positioning services in covered areas and areas that lack strong signals, such as indoor environments. Therefore, in recent years, indoor positioning technology has become the focus of research and development. The magnetic field of the Earth is quite stable in an open environment. Due to differences in building and internal structures, this type of three-dimensional vector magnetic field is widely available indoors for indoor positioning. A smartphone magnetometer was used in this study to collect magnetic field data for constructing indoor magnetic field maps. Moreover, an acceleration sensor and a gyroscope were used to identify the position of a mobile phone and detect the number of steps travelled by users with the phone. This study designed a procedure for measuring the step length of users. All obtained information was input into a pedestrian dead reckoning (PDR) algorithm for calculating the position of the device. The indoor positioning accuracy of the PDR algorithm was optimised using magnetic gradients of magnetic field maps with a modified particle filter algorithm. Experimental results reveal that the indoor positioning accuracy was between 0.6 and 0.8 m for a testing area that was 85 m long and 33 m wide. This study effectively improved the indoor positioning accuracy and efficiency by using the particle filter method in combination with the PDR algorithm with the magnetic fingerprint map.

A method for dead reckoning parameter correction in pedestrian navigation system

2003 ◽  
Vol 52 (1) ◽  
pp. 209-215 ◽  
Author(s):  
R. Jirawimut ◽  
P. Ptasinski ◽  
V. Garaj ◽  
F. Cecelja ◽  
W. Balachandran
Keyword(s):  
Navigation System ◽  
Dead Reckoning ◽  
Pedestrian Navigation ◽  
Parameter Correction

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

2019 ◽  
Vol 11 (3) ◽  
pp. 294 ◽  
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.

scholarly journals RTK/Pseudolite/LAHDE/IMU-PDR Integrated Pedestrian Navigation System for Urban and Indoor Environments

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1791
Author(s):  
Ruihui Zhu ◽  
Yunjia Wang ◽  
Hongji Cao ◽  
Baoguo Yu ◽  
Xingli Gan ◽  
...  
Keyword(s):  
Real Time ◽  
High Precision ◽  
Navigation System ◽  
Carrier Phase ◽  
Inertial Measurement Unit ◽  
Dead Reckoning ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Inertial Measurement ◽  
Pedestrian Navigation

This paper presents an evaluation of real-time kinematic (RTK)/Pseudolite/landmarks assistance heuristic drift elimination (LAHDE)/inertial measurement unit-based personal dead reckoning systems (IMU-PDR) integrated pedestrian navigation system for urban and indoor environments. Real-time kinematic (RTK) technique is widely used for high-precision positioning and can provide periodic correction to inertial measurement unit (IMU)-based personal dead reckoning systems (PDR) outdoors. However, indoors, where global positioning system (GPS) signals are not available, RTK fails to achieve high-precision positioning. Pseudolite can provide satellite-like navigation signals for user receivers to achieve positioning in indoor environments. However, there are some problems in pseudolite positioning field, such as complex multipath effect in indoor environments and integer ambiguity of carrier phase. In order to avoid the limitation of these factors, a local search method based on carrier phase difference with the assistance of IMU-PDR is proposed in this paper, which can achieve higher positioning accuracy. Besides, heuristic drift elimination algorithm with the assistance of manmade landmarks (LAHDE) is introduced to eliminate the accumulated error in headings derived by IMU-PDR in indoor corridors. An algorithm verification system was developed to carry out real experiments in a cooperation scene. Results show that, although the proposed pedestrian navigation system has to use human behavior to switch the positioning algorithm according to different scenarios, it is still effective in controlling the IMU-PDR drift error in multiscenarios including outdoor, indoor corridor, and indoor room for different people.

scholarly journals A Novel Step Length Estimator Based on Foot-Mounted MEMS Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4447 ◽  
Author(s):  
Zhuangsheng Zhu ◽  
Shibo Wang
Keyword(s):  
Inertial Navigation ◽  
Dead Reckoning ◽  
Step Length ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Pedestrian Navigation ◽  
Gait Characteristics ◽  
Motion State ◽  
Pedestrian Dead Reckoning ◽  
Indoor And Outdoor

Pedestrian Dead Reckoning (PDR)-based pedestrian navigation technology is an important part of indoor and outdoor seamless positioning services. To improve the performance of PDR, we have conducted research on a step length estimator. Firstly, based on the basic theory of inertial navigation, we analyze in detail the errors in traditional Strapdown Inertial Navigation Systems (SINSs) caused by the unique motion state of pedestrians. Then, according to the fact that the inertial data from the foot can directly reflect the gait characteristics, we conduct a step length estimator that does not rely on SINS. The experimental results show that accuracy of the proposed method is between 0.6% and 1.4% with a standard deviation of 0.25%.

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