Smartphone-based Pedestrian Dead Reckoning Research Articles

A Magnetic Interference Detection-Based Fusion Heading Estimation Method for Pedestrian Dead Reckoning Positioning

In indoor positioning, smartphone-based pedestrian dead reckoning (PDR) is a popular method. It does not require any predeployed infrastructure and can provide real-time, continuous position estimation with a known initial position. However, magnetometer sensor data are susceptible to magnetic interference from metal facilities, electrical equipment, and so on, thus reducing the accuracy of heading estimation. To solve this problem, we propose a fusion estimation method for pedestrian heading based on magnetic interference detection classification. First, a support vector machine (SVM) method is used to detect magnetic interference, and an improved relief-F feature selection method is proposed based on the information coefficient and Markov blanket condition. Second, according to SVM-based magnetic interference detection results, different methods for scenarios without and with magnetic interference are proposed to improve the accuracy of pedestrian heading estimation. The experimental results show that the proposed SVM-based magnetic interference detection accuracy reaches 98%, the average error of heading estimation is less than 4°, and the average positioning error (PE) of PDR integrating the proposed heading estimation method is 0.67 and 2.75 m when the walking distance is 84 and 252 m, respectively. The above results prove the effectiveness of the proposed method.

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones.

Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment.

FlexPDR: Fully Flexible Pedestrian Dead Reckoning Using Online Multimode Recognition and Time-Series Decomposition

Smartphone-based pedestrian dead reckoning (PDR) has been widely used indoors for continuous localization. However, the specific tracking solutions under different modes are vulnerable to mode transition and thus degrading performance. The robustness of pedestrian navigation may be weakened due to the mix of smartphone motions and walking patterns. Due to this challenge, most existing PDR methods assume that the smartphone is carried in a certain pose, such as handheld horizontally, swinging, calling, and pocketed, ignoring the short period but negative transition impact on tracking, which limits its flexibility when applying in the Internet of Things (IoT) services. To achieve a fully flexible PDR (i.e., FlexPDR), this article enhances the robustness and smoothness of pedestrian tracking during the transition between several phone poses and regular motion modes for the first time. We propose a Bayesian-based real-time multimode recognition method that does not require any posterior information, together with a time-series decomposition approach for adaptively tracking scheme-switching. The proposed FlexPDR system achieved a real-time smartphone indoor positioning with a high position accuracy of 98.11% on the specific situation that mixed mode-switching happens, which outperformed other state-of-the-art methods.

A Survey of Magnetic-Field-Based Indoor Localization

Magnetic fields have attracted considerable attention in indoor localization due to their ubiquitous and infrastructure-free characteristics. This survey provides a comprehensive review of magnetic-field-based indoor localization methods. We first introduce characteristics of the magnetic field, its advantages, and its challenges. We then describe the magnetometer model and the effect of ferromagnetic interference. We also present coordinate systems commonly used for magnetic field localization and describe their transformation relationships. We then compare the existing publicly available magnetic field benchmark datasets, present magnetometer calibration algorithms, and show how efficiently magnetic field maps can be built. We also summarize state-of-the-art magnetic field localization methods (e.g., magnetic landmarks, dynamic time warping, magnetic fingerprinting, filters, simultaneous localization and mapping, and neural network). The smartphone-based pedestrian dead reckoning approach is also reviewed.

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning.

Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04–1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17–0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.

Epidemic Contact Tracing With Campus WiFi Network and Smartphone-Based Pedestrian Dead Reckoning

Due to the rapid spreading of infectious disease COVID-19, numerous campus students are increasingly exposed to the dilemma and thus the provisioning of a safe environment becomes of vital importance. As a well-established approach, contact tracing could contain epidemic diseases spread. Since WiFi network could cover almost the whole campus and each student carries at least one WiFi capable device (i.e., smartphone), in this work, an epidemic contact tracing with campus WiFi network and smartphone-based pedestrian dead reckoning (PDR) is proposed, involving not only coarse-grained duration, but also fine-grained distance between students. First, students' location distribution and duration are captured by non-perception WiFi network logs with highly flexibility. Then, the convolutional neural network (CNN) model is utilized to real-time recognize landmarks in PDR positioning trajectory, followed by the particle filter algorithm to fuse both the PDR positioning results and detected landmarks, thereby calibrating PDR cumulative error and calculating the social distance between students. Next, we analyze the contact degree between students by integrating duration and social distance. Finally, in a campus environment with an coverage of about 600m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , we simulate a COVID-19 case study to validate proposed approach, showing that the average positioning error is reduced from 3.23m to 2.77m.

Pedestrian dead reckoning with novel heading estimation under magnetic interference and multiple smartphone postures

Accurate heading estimation using low-cost sensors becomes an important task in pedestrian dead-reckoning (PDR). The challenges of heading estimation come from three aspects; one is the unpredictable bias originated from the built-in gyroscope sensors, which leads to the accumulative error. In addition, the magnetic disturbance and the changeable posture of the smartphone can also adversely affect the performance of heading estimation. This paper presents an improved smartphone-based PDR positioning solution (iPDR) with a novel heading estimation algorithm. The cumulative error is reduced by quasi-static constraint, and the Zero Lateral Displacement Update (ZLDU)-based optimization method. And the phone interference postures constraints are applied to eliminate the effects of multiple smartphone postures. Meanwhile, the magnetic disturbance is eliminated in the heading update stage by Kalman filter. Experimental results show that the proposed iPDR proves excellent localization performance under complex environments with severe magnetic interference and different smartphone postures, and the average heading error is within 2°, and the average positioning error is within 2 m.

Indoor Positioning Using Deep-Learning-Based Pedestrian Dead Reckoning and Optical Camera Communication

The need for an accurate indoor positioning system has rapidly increased with the development of large complex malls and underground spaces. As signals from the global positioning system cannot be received inside buildings, only approximate locations can be estimated using Wi-Fi routers or cellular base station information, and exact locations cannot be determined. Therefore, a pedestrian dead reckoning (PDR) scheme using several sensors is suggested in this work. However, this scheme requires users to hold their smartphones in a specific manner; furthermore, user-dependent parameters, such as height and step length, are necessary because the sensor parameters vary. This study uses deep-learning algorithms to overcome the limitations of the existing smartphone-based PDR scheme. A convolutional neural network algorithm is used to classify the smartphone positions; then, appropriate sensor data are selected and adjusted. The long short-term memory algorithm is used to estimate the user step length. Although the PDR performance is enhanced using the deep-learning algorithm, accumulated error is unavoidable because the algorithm traces the relative position with reference to the original location. Therefore, optical camera communication is introduced to provide the reference location and periodically compensate for the accumulated PDR error. The proposed algorithm is experimentally demonstrated, and its results are obtained and analyzed.

SBAUPT: Azimuth SBUPT for Frequent Full Attitude Correction of Smartphone-Based PDR

The growing market of smart devices make them appealing for various applications. Motion tracking can be achieved using such devices, and is important for various applications such as navigation, search and rescue, health monitoring, and quality of life-style assessment. Step detection is a crucial task that affects the accuracy and quality of such applications. In this paper, a new step detection technique is proposed, which can be used for step counting and activity monitoring for health applications as well as part of a Pedestrian Dead Reckoning (PDR) system. Inertial and Magnetic sensors measurements are analyzed and fused for detecting steps under varying step modes and device pose combinations using a free-moving handheld device (smartphone). Unlike most of the state of the art research in the field, the proposed technique does not require a classifier, and adaptively tunes the filters and thresholds used without the need for presets while accomplishing the task in a real-time operation manner. Testing shows that the proposed technique successfully detects steps under varying motion speeds and device use cases with an average performance of 99.6% and outperforms some of the state of the art techniques that rely on classifiers and commercial wristband products.

3-D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning

The vision-aided pedestrian dead reckoning (PDR) systems have become increasingly popular, thanks to the ubiquitous mobile phone embedded with several sensors. This is particularly important for indoor use, where other indoor positioning technologies require additional installation or body attachment of specific sensors. This paper proposes and develops a novel 3-D passive vision-aided PDR system that uses multiple surveillance cameras and smartphone-based PDR. The proposed system can continuously track users’ movement on different floors by integrating results of inertial navigation and Faster Region-Based Convolutional Neutral Network (Faster R-CNN)-based real-time pedestrian detection, while utilizing existing camera locations and embedded barometers to provide floor/height information to identify user positions in 3-D space. This novel system provides a relatively low-cost and user-friendly solution, which requires no modifications to currently available mobile devices and also the existing indoor infrastructures at many public buildings for the purpose of 3-D indoor positioning. This paper shows the case of testing the prototype in a four-floor building, where it can provide the horizontal accuracy of 0.16 m and the vertical accuracy of 0.5 m. This level of accuracy is even better than required accuracy targeted by several emergency services, including the Federal Communications Commission (FCC). This system is developed for both Android and iOS-running devices.

Smartphone-Based 3D Indoor Pedestrian Positioning through Multi-Modal Data Fusion.

Combining research areas of biomechanics and pedestrian dead reckoning (PDR) provides a very promising way for pedestrian positioning in environments where Global Positioning System (GPS) signals are degraded or unavailable. In recent years, the PDR systems based on a smartphone’s built-in inertial sensors have attracted much attention in such environments. However, smartphone-based PDR systems are facing various challenges, especially the heading drift, which leads to the phenomenon of estimated walking path passing through walls. In this paper, the 2D PDR system is implemented by using a pocket-worn smartphone, and then enhanced by introducing a map-matching algorithm that employs a particle filter to prevent the wall-crossing problem. In addition, to extend the PDR system for 3D applications, the smartphone’s built-in barometer is used to measure the pressure variation associated to the pedestrian’s vertical displacement. Experimental results show that the map-matching algorithm based on a particle filter can effectively solve the wall-crossing problem and improve the accuracy of indoor PDR. By fusing the barometer readings, the vertical displacement can be calculated to derive the floor transition information. Despite the inherent sensor noises and complex pedestrian movements, smartphone-based 3D pedestrian positioning systems have considerable potential for indoor location-based services (LBS).

Improved Smartphone-Based Indoor Pedestrian Dead Reckoning Assisted by Visible Light Positioning

Aiming at the problem of heavy preparation work and high cost of indoor positioning technology, this paper proposes a visible light positioning (VLP)-assisted smartphone-based pedestrian dead reckoning (PDR) indoor positioning technology. This technology combines the advantages of both PDR and VLP algorithms for indoor positioning. During the pedestrian position calculation, the method of using the averaging of mode to process the heading angle data is creatively used, which improves the positioning accuracy and increases reliable operation time. At the same time, the two-level verification method consisting of cyclic redundancy check and range constraint is implemented for VLP, which uses a LED carrying a modulation code as a beacon for absolute position and the camera of smartphone to capture/decode the LED image to obtain the absolute position and periodically correct the position of the PDR positioning, thereby avoiding the accumulation of PDR positioning technology errors. The scope and reliability of PDR are demonstrated. The experiments show that this method is a kind of low cost, high robust, and high precision indoor positioning technology.

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

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.

Pedestrian Dead Reckoning Using Pocket-Worn Smartphone

Indoor pedestrian dead reckoning (PDR) extends the location-based service (LBS) to environments, where GPS or beacon signals are degraded or unavailable. A practical PDR system should consider the absence of any infrastructure or prior knowledge of the environment. This paper presents a PDR system based on a pocket-worn smartphone, which tracks a person's location through dead reckoning calculation by using the sensors embedded in smartphones. However, a smartphone-based PDR system faces various challenges, especially the heading drift due to gyroscope bias. In this paper, the gradient descent algorithm (GDA) is improved to reduce the heading drift, by fusing inertial data with only a fraction of magnetometer data that are accurate and usable. There is an 80% probability that the heading error is reduced to less than 4 degrees. Besides, a stride detection method is developed based on thigh angles, and then, a stride length estimation method is implemented in a complementary way. The experiments were conducted along with three types of reference paths, and the experimental results demonstrate that the average position errors along the three paths are 1.62%, 1.00%, and 0.92%, respectively. Despite the inherent sensor noise and complex human locomotion, the smartphone-based PDR system has great potential in pedestrian tracking.

A Smartphone-Based Pedestrian Dead Reckoning System With Multiple Virtual Tracking for Indoor Navigation

In this paper, we present a pedestrian dead reckoning (PDR) system with multiple virtual tracking for indoor navigation using a smartphone. In general, the heading error is one of the main factors causing positional errors in smartphone-based PDRs. To reduce it, methods using the assumption that walls and corridors are straight and either parallel or orthogonal to each other in man-made buildings have been studied. However, these methods are limited in a situation where a pedestrian does not walk down a corridor for a long period of time. In order to overcome the limitations of conventional algorithms using a dominant direction, the proposed PDR system with multiple virtual tracking uses the dominant direction as matching information only when it is reliable. If judging whether a pedestrian is walking in a dominant direction is uncertain at the current stage, the proposed algorithm also considers the previous situation by expanding the virtual tracks for various cases. The experimental results show the improved performance by comparing the proposed algorithm with a conventional one.

The Performance Analysis of Smartphone-Based Pedestrian Dead Reckoning and Wireless Locating Technology for Indoor Navigation Application

Recent developments in smartphone technology have increased user demand for indoors applications. The Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) are the two advanced systems for navigation technology. However, it is still difficult for GNSS to provide an accurate and practical navigation solution, especially in environments with little or no signal availability. These failings should be easy to overcome; because of their portability and multiple embedded hardware sensors, smartphones seem well positioned to make pedestrian navigation easy and convenient in any environment. Pedestrian Dead Reckoning (PDR) is one of the most commonly used technologies used for pedestrian navigation, but it is not always accurate because its positioning errors tend to accumulate over time. Therefore, this research introduces a new tool to overcome this failing; a Bluetooth Low-Energy (BLE) beacon can maintain and improve the accuracy of PDR. Moreover, a BLE beacon can be initialized from any user position in an indoor environment. The random and unpredictable positions of pedestrians inevitably result in the degradation of navigation guidance systems’ data. To rectify this problem, we have implemented activity recognition technology to notify the proposed system so as to provide a more accurate heading estimate. This study proposes a Personal Navigation System (PNS) based on this technology; it can estimate navigation solutions in real time and combines the advantages of PDR and Bluetooth positioning technology. A series of experiments were conducted to evaluate the accuracy of the system and the efficacy of our proposed algorithms. Preliminary results show the average relative precision of PDR to be about 2.5%, when using a mobile hand-held device. The error of initial position from 2-D beacon positioning is less than two meters. The proposed system works well without post-processing, and the multi-sensor activity recognition system can determine the placement of the device when it is being carried or used by someone with close to 100% accuracy.

Smartphone Inertial Sensor-Based Indoor Localization and Tracking With iBeacon Corrections

The Global Positioning System (GPS) can be readily used for outdoor localization, but GPS signals are degraded in indoor environments. How to develop a robust and accurate indoor localization system is an emergent task. In this paper, we propose a smartphone inertial sensor-based indoor localization and tracking system with occasional iBeacon corrections. Some important issues in a smartphone-based pedestrian dead reckoning (PDR) approach, i.e., step detection, walking direction estimation, and initial point estimation, are studied. One problem of the PDR approach is the drift with walking distance. We apply a recent technology, iBeacon, to occasionally calibrate the drift of the PDR approach. By analyzing iBeacon measurements, we define an efficient calibration range where an extended Kalman filter is utilized. The proposed localization and tracking system can be implemented in resource-limited smartphones. To evaluate the performance of the proposed approach, real experiments under two different environments have been conducted. The experimental results demonstrated the effectiveness of the proposed approach. We also tested the localization accuracy with respect to the number of iBeacons.

Urban Pedestrian Navigation Using Smartphone-Based Dead Reckoning and 3-D Map-Aided GNSS

This paper focuses on the pedestrian navigation in highly urbanized area, where a current smartphone and a commercial global navigation satellite system (GNSS) receiver perform poorly because of the reflection and blockage of GNSS signal by buildings and foliage. A 3-D map-aided pedestrian positioning method is previously developed to mitigate and correct the multipath GNSS signal. However, it still suffers from the low availability due to the insufficient number of satellites. We develop a smartphone-based pedestrian dead reckoning (PDR) algorithm, which is carried in the pedestrian’s trousers. This PDR is capable of not only providing continues solutions but also indicating the pedestrian motions. A closed-loop Kalman filter with adaptive tuning is proposed to integrate the 3-D map-aided GNSS method with the smartphone-based PDR system. According to the experiment results, the proposed integration system can achieve $\sim 1.5$ - and 5.5-m of positioning errors in a middle-class and deep urban canyon, respectively.

Motion Recognition based 3D Pedestrian Navigation System using Smartphone

A motion recognition-based 3D pedestrian navigation system that employs a smartphone is presented. In existing inertial measurement unit (IMU)-based pedestrian dead-reckoning (PDR) systems, sensor axes are fixed regardless of user motion, because the IMU is mounted on the shoes or helmet. On the other hand, the sensor axes of a smartphone are changed according to the walking motion of the user, because the smartphone is usually carried by hand or kept in the pocket. Therefore, the conventional PDR method cannot apply to the smartphone-based PDR system. To overcome this limitation, the walking status is detected using a motion recognition algorithm with sensor measurements from the smartphone. Then, different PDR algorithms are applied according to the recognized pattern of the pedestrian motion. The height information of the pedestrian is also estimated using the on-board barometric pressure sensor of the smartphone. The 3D position, which consists of the 2D position calculated by the PDR and the height information, is provided to the pedestrian. The proposed system has several advantages in terms of cost and accessibility. It requires no additional peripheral devices except for the smartphone, because smartphones are equipped with all the necessary sensors, such as an accelerometer, magnetometer, gyroscope, and barometric pressure sensor. This paper implements the proposed system as an android-based application. The experimental results demonstrate the performance of the proposed system and reveal a high positioning accuracy.

SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization

Indoor pedestrian tracking extends location-based services to indoor environments where GPS signal is rarely detected. Typical indoor localization method is Wi-Fi-based positioning system, which is practical showing accuracy and extending coverage. However, it involves significant costs of installing and managing wireless access points. A practical indoor pedestrian-tracking approach should consider the absence of any infrastructure or pretrained database. In this paper, we present a smartphone-based pedestrian dead reckoning, SmartPDR, which tracks pedestrians through typical dead reckoning approach using data from inertial sensors embedded in smartphones. SmartPDR does not require any complex and expensive additional device or infrastructure that most existing pedestrian tracking systems rely on. The proposed system was implemented on off-the-shelf smartphones and the performance was evaluated in several buildings. Despite inherent localization errors from low-cost noisy sensors and complicated human movements, SmartPDR successfully tracks indoor user's location, which is confirmed from the experimental results with reasonable location accuracy. Indoor pedestrian tracking system using smartphone inertial sensors can be a promising methodology validating its practical usage through real deployment.