Threshold-less Zero-Velocity Detection Algorithm for Pedestrian Dead Reckoning

This research proposes an algorithm that improves the position accuracy of indoor pedestrian dead reckoning, by compensating the position error with a magnetic field map-matching technique, using multiple magnetic sensors and an outlier mitigation technique based on roughness weighting factors. Since pedestrian dead reckoning using a zero velocity update (ZUPT) does not use position measurements but zero velocity measurements in a stance phase, the position error cannot be compensated, which results in the divergence of the position error. Therefore, more accurate pedestrian dead reckoning is achievable when the position measurements are used for position error compensation. Unfortunately, the position information cannot be easily obtained for indoor navigation, unlike in outdoor navigation cases. In this paper, we propose a method to determine the position based on the magnetic field map matching by using the importance sampling method and multiple magnetic sensors. The proposed method does not simply integrate multiple sensors but uses the normalization and roughness weighting method for outlier mitigation. To implement the indoor pedestrian navigation algorithm more accurately than in existing indoor pedestrian navigation, a 15th-order error model and an importance-sampling extended Kalman filter was utilized to correct the error of the map-matching-aided pedestrian dead reckoning (MAPDR). To verify the performance of the proposed indoor MAPDR algorithm, many experiments were conducted and compared with conventional pedestrian dead reckoning. The experimental results show that the proposed magnetic field MAPDR algorithm provides clear performance improvement in all indoor environments.

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Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position

Mobile Information Systems ◽

10.1155/2019/4709501 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Ying Guo ◽

Qinghua Liu ◽

Xianlei Ji ◽

Shengli Wang ◽

Mingyang Feng ◽

...

Keyword(s):

Inertial Sensors ◽

Distance Estimation ◽

Dead Reckoning ◽

Principal Component ◽

Detection Algorithm ◽

Estimation Accuracy ◽

Indoor Environments ◽

Using Data ◽

The Time Domain ◽

Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.

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A Double-Step Unscented Kalman Filter and HMM-Based Zero-Velocity Update for Pedestrian Dead Reckoning Using MEMS Sensors

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2019.2897550 ◽

2020 ◽

Vol 67 (1) ◽

pp. 581-591 ◽

Cited By ~ 9

Author(s):

Xin Tong ◽

Yan Su ◽

Zhaofeng Li ◽

Chaowei Si ◽

Guowei Han ◽

...

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Dead Reckoning ◽

Mems Sensors ◽

Double Step ◽

Zero Velocity ◽

Pedestrian Dead Reckoning

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Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

Sensors ◽

10.3390/s21134565 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4565

Author(s):

Cedric De Cock ◽

Wout Joseph ◽

Luc Martens ◽

Jens Trogh ◽

David Plets

Keyword(s):

Particle Filter ◽

Real Time ◽

Clustering Algorithm ◽

Dead Reckoning ◽

Detection Algorithm ◽

Measurement Unit ◽

Batch Mode ◽

Access Points ◽

Median Position ◽

Pedestrian Dead Reckoning

We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by 17% in real-time and 13% in batch mode, while the floor detection algorithm achieved a 99.1% and 99.7% floor number accuracy in real-time and batch mode, respectively.

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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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Drift Control of Pedestrian Dead Reckoning (PDR) for Long Period Navigation under Different Smartphone Poses

Engineering Proceedings ◽

10.3390/ecsa-8-11302 ◽

2021 ◽

Vol 10 (1) ◽

pp. 21

Author(s):

Ahmed Mansour ◽

Wu Chen ◽

Huan Luo ◽

Yaxin Li ◽

Jingxian Wang ◽

...

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Dead Reckoning ◽

Field Measurements ◽

Step Length ◽

Detection Algorithm ◽

Long Period ◽

Walking Pattern ◽

Heading Estimation ◽

Pedestrian Dead Reckoning

The inherent errors of low-cost inertial sensors cause significant heading drift that accumulates over time, making it difficult to rely on Pedestrian Dead Reckoning (PDR) for navigation over a long period. Moreover, the flexible portability of the smartphone poses a challenge to PDR, especially for heading determination. In this work, we aimed to control the PDR drift under the conditions of the unconstrained smartphone to eventually enhance the PDR performance. To this end, we developed a robust step detection algorithm that efficiently captures the peak and valley events of the triggered steps regardless of the device’s pose. The correlation between these events was then leveraged as distinct features to improve smartphone pose detection. The proposed PDR system was then designed to select the step length and heading estimation approach based on a real-time walking pattern and pose discrimination algorithm. We also leveraged quasi-static magnetic field measurements that have less disturbance for estimating reliable compass heading and calibrating the gyro heading. Additionally, we also calibrated the step length and heading when a straight walking pattern is observed between two base nodes. Our results showed improved device pose recognition accuracy. Furthermore, robust and accurate results were achieved for step length, heading and position during long-term navigation under unconstrained smartphone conditions.

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Step Detection Algorithm Using Fast Fourier Transformation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1049-1050.1218 ◽

2014 ◽

Vol 1049-1050 ◽

pp. 1218-1221

Author(s):

He Zhang ◽

Rui Peng ◽

Xiao Dong Zhao

Keyword(s):

Fourier Transformation ◽

Dead Reckoning ◽

Detection Algorithm ◽

Fast Fourier Transformation ◽

Step Detection ◽

Pedestrian Navigation ◽

Improve Accuracy ◽

Basic Portion ◽

Task Step ◽

Pedestrian Dead Reckoning

Pedestrian Dead Reckoning (PDR) is a core component in pedestrian navigation. Usually, PDR algorithms use the current position and movement information to figure out position in the future in order to accomplish the navigation task. Step detection, as a basic portion of PDR, is significant for the implementation of Pedestrian Navigation. In this paper, a step detection algorithm is designed based on the existing research in the relative area. To improve accuracy, the algorithm involves a Fast Fourier Transformation (FFT) for optimizing. At last, an experiment is conducted for this algorithm, and the error rate of step detection is less than 1%.

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Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System

Sensors ◽

10.3390/s20030651 ◽

2020 ◽

Vol 20 (3) ◽

pp. 651 ◽

Cited By ~ 1

Author(s):

Jesus D. Ceron ◽

Christine F. Martindale ◽

Diego M. López ◽

Felix Kluge ◽

Bjoern M. Eskofier

Keyword(s):

Dynamic Time Warping ◽

Dead Reckoning ◽

Detection Algorithm ◽

Time Warping ◽

Trajectory Reconstruction ◽

Position Errors ◽

Controlled Environments ◽

Dynamic Time ◽

Pedestrian Dead Reckoning ◽

New Algorithms

The evaluation of trajectory reconstruction of the human body obtained by foot-mounted Inertial Pedestrian Dead-Reckoning (IPDR) methods has usually been carried out in controlled environments, with very few participants and limited to walking. In this study, a pipeline for trajectory reconstruction using a foot-mounted IPDR system is proposed and evaluated in two large datasets containing activities that involve walking, jogging, and running, as well as movements such as side and backward strides, sitting, and standing. First, stride segmentation is addressed using a multi-subsequence Dynamic Time Warping method. Then, detection of Toe-Off and Mid-Stance is performed by using two new algorithms. Finally, stride length and orientation estimation are performed using a Zero Velocity Update algorithm empowered by a complementary Kalman filter. As a result, the Toe-Off detection algorithm reached an F-score between 90% and 100% for activities that do not involve stopping, and between 71% and 78% otherwise. Resulting return position errors were in the range of 0.5% to 8.8% for non-stopping activities and 8.8% to 27.4% otherwise. The proposed pipeline is able to reconstruct indoor trajectories of people performing activities that involve walking, jogging, running, side and backward walking, sitting, and standing.

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Real-Time Pedestrian Tracking Terminal Based on Adaptive Zero Velocity Update

Sensors ◽

10.3390/s21113808 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3808

Author(s):

Ran Wei ◽

Hongda Xu ◽

Mingkun Yang ◽

Xinguo Yu ◽

Zhuoling Xiao ◽

...

Keyword(s):

Real Time ◽

Real World ◽

Inertial Measurement Unit ◽

Dead Reckoning ◽

Measurement Unit ◽

Tracking Accuracy ◽

Zero Velocity ◽

And Performance ◽

Pedestrian Dead Reckoning ◽

Motion Types

In the field of pedestrian dead reckoning (PDR), the zero velocity update (ZUPT) method with an inertial measurement unit (IMU) is a mature technology to calibrate dead reckoning. However, due to the complex walking modes of different individuals, it is essential and challenging to determine the ZUPT conditions, which has a direct and significant influence on the tracking accuracy. In this research, we adopted an adaptive zero velocity update (AZUPT) method based on convolution neural networks to classify the ZUPT conditions. The AZUPT model was robust regardless of the different motion types of various individuals. AZUPT was then implemented on the Zynq-7000 SoC platform to work in real time to validate its computational efficiency and performance superiority. Extensive real-world experiments were conducted by 60 different individuals in three different scenarios. It was demonstrated that the proposed system could work equally well in different environments, making it portable for PDR to be widely performed in various real-world situations.

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S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Geomatics ◽

10.3390/geomatics1020010 ◽

2021 ◽

Vol 1 (2) ◽

pp. 148-176

Author(s):

Maan Khedr ◽

Naser El-Sheimy

Keyword(s):

Smart Cities ◽

Dead Reckoning ◽

Human Motion ◽

Indoor Navigation ◽

Location Based Services ◽

Navigation Systems ◽

Short Term ◽

Navigation Solution ◽

Correction Technique ◽

Pedestrian Dead Reckoning

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

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