Pedestrian Dead Reckoning Method Research Articles

Indoor positioning method for pedestrian dead reckoning based on multi-source sensors

To solve the problems of severe error accumulation and low accuracy of pedestrian trajectory estimation in traditional Pedestrian Dead Reckoning (PDR) positioning technology, this paper proposes a multi-sensor fusion indoor PDR algorithm. Firstly, a generalized likelihood ratio multi-threshold detection algorithm is employed to detect the gait of pedestrians. Then, a linear multi-source information fusion model is constructed for step length estimation. Next, the quaternion strap-down attitude solution is utilized and coupled with an improved particle filter-unscented Kalman filter algorithm to correct heading angle deviations. Finally, integrate them into the PDR algorithm to estimate the pedestrian's position. The proposed PDR method's relative positioning errors for indoor two-dimensional plane and three-dimensional space walking are 0.36 % and 0.435 %, respectively. Compared to four traditional positioning algorithms, it reduces errors by approximately 0.77 %∼1.18 % and 5.42 %∼11.69 %, respectively. Experimental results indicate that the proposed PDR method effective suppression of error accumulation, achieving more accurate indoor PDR results.

Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones

Nowadays, smartphones have become indispensable in people’s daily work and life. Since various sensors and communication chips have been integrated into smartphones, it has become feasible to provide indoor positioning using phones. This paper proposes such a solution based on a smartphone, combining Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR) in the particle filter framework to realize real-time and reliable indoor positioning. First, the smartphone’s built-in accelerometer, magnetometer, and gyroscope are used to provide data measurements and formulate a feasible method for PDR. Second, a range-free weighted centroid algorithm is proposed to realize BLE-based localization with low computation complexity. However, a single positioning technology has limitations, e.g., the cumulative error of PDR and the received signal strength fluctuation of BLE. Finally, to exploit the complementary strengths of each technology, a fusion framework utilizing a particle filter is proposed to combine PDR and BLE-based methods and provides more stable and accurate positioning results. Experiments are conducted on a floor in a campus building. Experimental results show that our proposed fused positioning method offers more accurate and stable performance in the long run compared with single PDR or BLE-based positioning. The achieved average positioning error is 1.34 m, which is reduced by 24.16% compared with PDR positioning and 10.60% compared with BLE-based positioning. Moreover, about 95% of the positioning errors are smaller than 1.7 m. The proposed fused positioning method has a vast application prospect in indoor navigation, indoor user tracking, and interactive experience for indoor visitors.

A Context-Aware Smartphone-Based 3D Indoor Positioning Using Pedestrian Dead Reckoning.

The rise in location-based service (LBS) applications has increased the need for indoor positioning. Various methods are available for indoor positioning, among which pedestrian dead reckoning (PDR) requires no infrastructure. However, with this method, cumulative error increases over time. Moreover, the robustness of the PDR positioning depends on different pedestrian activities, walking speeds and pedestrian characteristics. This paper proposes the adaptive PDR method to overcome these problems by recognizing various phone-carrying modes, including texting, calling and swinging, as well as different pedestrian activities, including ascending and descending stairs and walking. Different walking speeds are also distinguished. By detecting changes in speed during walking, PDR positioning remains accurate and robust despite speed variations. Each motion state is also studied separately based on gender. Using the proposed classification approach consisting of SVM and DTree algorithms, different motion states and walking speeds are identified with an overall accuracy of 97.03% for women and 97.67% for men. The step detection and step length estimation model parameters are also adjusted based on each walking speed, gender and motion state. The relative error values of distance estimation of the proposed method for texting, calling and swinging are 0.87%, 0.66% and 0.92% for women and 1.14%, 0.92% and 0.76% for men, respectively. Accelerometer, gyroscope and magnetometer data are integrated with a GDA filter for heading estimation. Furthermore, pressure sensor measurements are used to detect surface transmission between different floors of a building. Finally, for three phone-carrying modes, including texting, calling and swinging, the mean absolute positioning errors of the proposed method on a trajectory of 159.2 m in a multi-story building are, respectively, 1.28 m, 0.98 m and 1.29 m for women and 1.26 m, 1.17 m and 1.25 m for men.

IMPROVEMENT OF PEDESTRIAN DEAD RECKONING ALGORITHM FOR INDOOR POSITIONING BY USING STEP LENGTH ESTIMATION

Abstract. Pedestrian dead reckoning (PDR) can solve the position information by its inertial measurement unit (IMU), which is a method to achieve indoor autonomous positioning without deploying positioning base stations in advance. However, affected by the accumulated error, the positioning accuracy will decrease with the increase of the moving distance. To improve the indoor positioning accuracy, the step length estimation of PDR is improved. A PDR method combining the information on walking frequency, acceleration variance, and fixed step is proposed. Firstly, the pedestrian's walking steps are detected and estimated by the threshold peak method; secondly, the step frequency and acceleration variance are calculated, and the step length estimation model is fitted with the fixed step length of the pedestrian to estimate the step length of each step; then, the fusion algorithm of magnetometer, accelerometer, and gyroscope are used to estimate the heading; finally, combined with the step number, step length, and heading angle, the position information of walking is calculated by dead reckoning. The experimental results show that in the practical distance of 42.6 m, the accuracy of dead reckoning distance based on the combination of step frequency, acceleration variance, and fixed step length can reach 98.47%, which is better than the fixed step length and linear step length version, 5.09% higher than the linear step length and 0.35% higher than the fixed step length version.

A Continuous PDR and GNSS Fusing Algorithm for Smartphone Positioning

Pedestrian dead reckoning (PDR), used in state-of-the-art smartphones, calculates pedestrian positions by using built-in inertial sensors. However, the complex and changeable usage modes of smartphones have been obstructing the development of PDR in the field of gait detection. Since the measurement of the sensor is affected by noise, position errors will emerge, needing to be corrected periodically via external measurements. To this end, an optimization-based PDR (OBPDR) method for smartphones is proposed in this study. First, an improved finite state machine (IFSM) gait detection method is designed, which can improve the gait recognition rate and stability compared with the traditional peak detection method. Second, the step detection algorithm proposed in this paper is combined with a heading estimation to obtain the PDR dynamic model. Finally, the measurements of GNSS are fused to the PDR model, based on an adaptive extended Kalman filter (AEKF) algorithm, which can enhance the adaptability of the system to the environment in order to reduce the cumulative errors of PDR. Experiments are carried out to evaluate the performance of the proposed method. The results indicate that compared with the gait detection method, based on peak detection, and the integrated positioning method, based on an extended Kalman filter, the proposed method boasts favorable robustness and a high gait recognition rate, the recognition accuracy being kept between 97.5% and 98.5%; the average position error decreased by more than 67.25%.

Sensor fusion for indoor positioning system through improved RSSI and PDR methods

Given the growth of the Internet of Things (IoT) and smart home appliances, the concept of the Indoor Positioning System or IPS has considerably risen. The major application of IPS is in locating people in roofed places. In this type of positioning, accuracy has always been the most important challenge. The main focus of this article is to improve the indoor positioning of people. First, two positioning approaches are proposed, Radio Signal Strength Indicator (RSSI) and Pedestrian Dead Reckoning (PDR). In the RSSI positioning part, the distance between the participating nodes is calculated based on the low-fluctuating values of RSSI. A novel filter called Weight-Based Optimization (WBO) is developed to optimize these raw RSSI values. Moreover, the path loss model parameters, which are location-dependent, are calculated by employing Particle Swarm Optimization (PSO) to convert the resulting RSSI values to distances. In the PDR positioning part, the accelerometer sensor data is used to detect the person’s steps, and the compass sensor data is used to detect the heading direction. Finally, the results of RSSI and PDR methods are combined using a sensor fusion approach. The positioning accuracy resulting from the proposed IPS approach is 68 cm, which is far better than the existing methods.

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.

HeadSLAM: Pedestrian SLAM with Head-Mounted Sensors.

Research focused on human position tracking with wearable sensors has been developing rapidly in recent years, and it has shown great potential for application within healthcare, smart homes, sports, and emergency services. Pedestrian Dead Reckoning (PDR) with Inertial Measurement Units (IMUs) is one of the most promising solutions within this domain, as it does not rely on any additional infrastructure, whilst also being suitable for use in a diverse set of scenarios. However, PDR is only accurate for a limited period of time before unbounded errors, due to drift, affect the position estimate. Error correction can be difficult as there is often a lack of efficient methods for calibration. HeadSLAM, a method specifically designed for head-mounted IMUs, is proposed to improve the accuracy during longer tracking times (10 min). Research participants (n = 7) were asked to walk in both indoor and outdoor environments wearing head-mounted sensors, and the obtained HeadSLAM accuracy was subsequently compared to that of the PDR method. A significant difference (p < 0.001) in the average root-mean-squared error and absolute error was found between the two methods. HeadSLAM had a consist lower error across all scenarios and subjects in a 20 h walking dataset. The findings of this study show how the HeadSLAM algorithm can provide a more accurate long-term location service for head-mounted, low-cost sensors. The improved performance can support inexpensive applications for infrastructureless navigation.

Pedestrian Dead Reckoning Based on Walking Pattern Recognition and Online Magnetic Fingerprint Trajectory Calibration

With the explosive development of pervasive computing and the Internet of Things (IoT), indoor positioning and navigation have attracted immense attention over recent years. Pedestrian dead reckoning (PDR) is a potential autonomous localization technology that obtains position estimation employing built-in sensors. However, most existing PDR methods assume that the smartphone is held horizontally and points to the walking direction. To solve reckoning errors caused by inconsistency of headings between walking heading and pointing of smartphone, we design an accurate and robust PDR method based on walking patterns, which is identified by multihead convolutional neural networks. In addition to adaptively adjust the threshold of step detection and select the most suitable step length model according to the results of walking pattern recognition, a novel heading estimation approach independent of device orientation is proposed. To mitigate accumulative errors, we proposed an online trajectory calibration method based on forward and backward magnetic fingerprint trajectory matching. We conduct extensive and well-designed experiments in typical scenarios, and the experimental results indicate that the 75th percentile localization accuracy of the three scenarios is 1.06, 1.08, and 1.22 m, respectively, using the commercial smartphone embedded sensor without any dedicated infrastructures or training data. Despite the intricate pedestrian locomotion, the proposed PDR method has great potential in pedestrian positioning.

Deep Learning-Based PDR Scheme That Fuses Smartphone Sensors and GPS Location Changes

Pedestrian dead reckoning (PDR), a sensor-based localization method using a smartphone, combines multi-sensor data from an inertial measurement unit (IMU) generated by the movement of pedestrians and calculates the amount of movement change from a previous location using fusion of sensor data. In this study, we propose a method to improve the efficiency of a deep learning (DL)-based PDR scheme to solve problems associated with the existing PDR method. The proposed DL-PDR scheme solves the movement change of smartphone users as a regression problem by combining IMU and global positioning system (GPS) data. In this paper, we (1) describe the existing PDR methods and problems, describe the proposed DL-PDR scheme and the data collection process of the input sensor data and output GPS used for deep learning, (2) correlate the collected I/O data and conduct preprocessing to make the data suitable for learning, (3) apply data refining and data augmentation methods to provide efficient learning and prevent overfitting, and (4) Verify the performance of the proposed scheme. The localization performance between the proposed scheme and existing methods is compared in various buildings where continuous localization is possible owing to connected indoor/outdoor spaces.

HEAD: smootH Estimation of wAlking Direction with a handheld device embedding inertial, GNSS, and magnetometer sensors

Pedestrian navigation with handheld sensors is still particularly complex. Pedestrian Dead Reckoning method is generally used, but the estimation of the walking direction remains problematic because the device's pointing direction does not always correspond to the walking direction. To overcome this difficulty, it is possible to use gait modeling based approaches. But, these methods suffer from sporadic erroneous estimates and their accumulation over time. The HEAD (smootH Estimation of wAlking Direction) filter uses WAISS and MAGYQ angular estimates as observations to correct the walking direction and to obtain more robust and smooth results. TDCP updates are applied to constrain the walking direction estimation error while pseudo-ranges directly update the position. HEAD is tested by 5 subjects over 21 indoor/outdoor acquisitions (between 720 m and 1.3 km). A 54% improvement is achieved thanks to the fusion in texting mode. The median obtained angular error is 5.5 degrees in texting mode and 12 degrees in pocket mode.

Pedestrian Dead Reckoning With Wearable Sensors: A Systematic Review

Pedestrian Dead Reckoning (PDR) is the process of calculating one's current location by using the previously known position, and advancing that position over time using established or estimated speeds and trajectories (or alternatively stride lengths and directions). PDR plays an important role in modern life, including tracking locations of people and objects whenever GPS is not available. Self-contained PDR systems do not require an infrastructure, thus they can be used for rapid deployment in situations such as search and rescue, disaster relief or medical emergencies. Wearable sensors are often applied in self-contained PDR, but implementation varies in terms of the number, type and location of sensors used. Many algorithms are designed for PDR in order to reduce the error or drift of the final estimate, with various levels of success. There is a lack of comparison between these different methods and this systematic review of PDR for wearable devices provides a comprehensive overview that can inform further design optimizations. The aim of this article is to assess the quality of all available PDR literature with a focus on wearable sensors. It provides an outline of the state-of-the-art in the field by comparing the accuracy of different sensor layouts and algorithms. Further directions of research are suggested based on these results. This study also highlights the need for more standardised and robust assessment protocols to capture real-world tracking performance of PDR methods.

A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning

Indoor pedestrian localization measurement is a hot topic and is widely used in indoor navigation and unmanned devices. PDR (Pedestrian Dead Reckoning) is a low-cost and independent indoor localization method, estimating position of pedestrians independently and continuously. PDR fuses the accelerometer, gyroscope and magnetometer to calculate relative distance from starting point, which is mainly composed of three modules: step detection, stride length estimation and heading calculation. However, PDR is affected by cumulative error and can only work in two-dimensional planes, which makes it limited in practical applications. In this paper, a novel localization method V-PDR is presented, which combines VPR (Visual Place Recognition) and PDR in a loosely coupled way. When there is error between the localization result of PDR and VPR, the algorithm will correct the localization of PDR, which significantly reduces the cumulative error. In addition, VPR recognizes scenes on different floors to correct floor localization due to vertical movement, which extends application scene of PDR from two-dimensional planes to three-dimensional spaces. Extensive experiments were conducted in our laboratory building to verify the performance of the proposed method. The results demonstrate that the proposed method outperforms general PDR method in accuracy and can work in three-dimensional space.

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

This paper proposes a fusion indoor positioning method that integrates the pedestrian dead-reckoning (PDR) and geomagnetic positioning by using the genetic-particle filter (GPF) algorithm. In the PDR module, the Mahony complementary filter (MCF) algorithm is adopted to estimate the heading angles. To improve geomagnetic positioning accuracy and geomagnetic fingerprint specificity, the geomagnetic multi-features positioning algorithm is devised and five geomagnetic features are extracted as the single-point fingerprint by transforming the magnetic field data into the geographic coordinate system (GCS). Then, an optimization mechanism is designed by using gene mutation and the method of reconstructing a particle set to ameliorate the particle degradation problem in the GPF algorithm, which is used for fusion positioning. Several experiments are conducted to evaluate the performance of the proposed methods. The experiment results show that the average positioning error of the proposed method is 1.72 m and the root mean square error (RMSE) is 1.89 m. The positioning precision and stability are improved compared with the PDR method, geomagnetic positioning, and the fusion-positioning method based on the classic particle filter (PF).

A Robust Pedestrian Dead Reckoning System Using Low-Cost Magnetic and Inertial Sensors

Pedestrian dead reckoning (PDR) using inertial and magnetic measurement unit (IMMU) is a current research hotspot that can be used to provide location-based services. The main challenges of the self-contained sensor-based PDR method are mainly sensor errors and orientation estimation errors. In this paper, a novel orientation estimation algorithm and gait phase detection algorithm with strong adaptability are proposed. The variance and magnitude of the angular rate are adopted to detect the gait. The experimental results show that the gait phase detection method can accurately distinguish the swing phase and the stance phase of the foot even under running conditions. By reasonable assumptions, the attitude angles calculated in the swing phase can be calibrated while the attitude angles are in the stance phase. The calibration of velocity not only uses the zero velocity updates method but also makes reasonable assumptions about the bias error of the accelerometer, so that the velocity calculated during the whole swing phase can be calibrated. Experimental results demonstrate that the proposed self-contained IMMU is capable of providing consistent beacon-free PDR in different scenarios, achieving less than 1.2% average distance error and average end-to-end position error.

HILS: hybrid indoor localisation system using Wi‐Fi received signal strength and inertial sensor's measurements of smart‐phone

This study presents a smart-phone-based hybrid indoor localisation system (HILS), which integrates two different localisation approaches, i.e., pedestrian dead reckoning (PDR) and Wi-Fi-based localisation to compensate the limits of each other. In PDR, the localisation accuracy degrades continuously when the moving distance increases from the starting reference point. However, Wi-Fi-based trilateration method requires the Wi-Fi signals from at least three access points (APs). In this study, the authors propose a more efficient novel Heron-bilateration-based position determination (HBPD) technique, which requires the Wi-Fi signals from only two APs to locate the mobile device. The proposed HILS utilises the HBPD technique to reset the bias drift errors involved with inertial sensors, and compensate the unavailability of strong Wi-Fi signals from APs using the PDR method. In this work, an experimental testbed is developed to collect the Wi-Fi signals and evaluate the proposed method. The proposed HILS is also compared with five other benchmark methods for two different trajectories and the experimental results show that the mean localisation error is decreased up to 1.68 and 1.96 m, which indicate the improvement by 16.83 and 20.97% for both the trajectories, respectively, in comparison with the best performing method among the benchmark methods.

A Real-Time Pedestrian Dead Reckoning System With FM-Aided Motion Mode Recognition

Accurate indoor pedestrian positioning has attracted extensive research attention along with the rising popularity of indoor location-based service. Among the indoor positioning methods, pedestrian dead reckoning (PDR) stands out for requiring neither expensive infrastructure nor laborious site survey. However, with time going on, the PDR method suffers from error accumulation problem, whereas the major cause of positioning error is usually heading deviation. To tackle the error-drifting problem, a real-time indoor PDR system using inertial sensors and the frequency-modulated radio receiver is designed in this paper. First, a random forest classifier combining frequency-modulated radio signal and inertial sensor signal is devised to achieve real-time identification of straight-line and turning mode of moving. Based on the classification result, the constraint is applied to headings in straight periods and true headings are extracted from accelerometer and magnetometer measurements to reduce heading deviation in straight periods. Field experiments have been conducted at three sites in two office buildings to evaluate the performance of the proposed system. The experimental results indicate error drifting can be effectively constrained by the proposed PDR system.

Low-complexity online correction and calibration of pedestrian dead reckoning using map matching and GPS

ABSTRACTDead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change. Pedestrian Dead Reckoning (PDR) applies this concept to walking persons. The method can be used to track someone's movement in a building after a known landmark like the building's entrance is registered. Here, the movement of a foot and the corresponding direction change is measured and summed up, to infer the current position. Measuring and integrating the corresponding physical parameters, e.g. using inertial sensors, introduces small errors that accumulate quickly into large distance errors. Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths. In this paper, we use building maps to improve localization based on a single foot-mounted inertial sensor. We describe our localization method using zero velocity updates to accurately compute the length of individual steps and a Madgwick filter to determine the step orientation. Even though the computation of individual steps is quite accurate, small errors still accumulate in the long term. We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks, such as orientation and displacement drift, and discuss restrictions and disadvantages of these algorithms. We also present a method of deriving the initial position and orientation from GPS measurements. We verify our PDR correction methods analyzing the corrected and raw trajectories of six participants walking four routes of varying length and complexity through an office building, walking each route three times. Our quantitative results show an endpoint accuracy improvement of up to 60 when using likely paths and 23 when using unlikely paths. However, both approaches can also decrease accuracy in certain scenarios. We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.

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

In this paper, we propose a novel method for pedestrian dead reckoning (PDR) using microelectromechanical system magnetic, angular rate, and gravity sensors, which includes a double-step unscented Kalman filter (DUKF) and hidden Markov model (HMM)-based zero-velocity-update (ZVU) algorithm. The DUKF divides the measurement updates of the gravity vector and the magnetic field vector into two steps in order to avoid the unwanted correction for the Euler angle error. The HMM-based ZVU algorithm is developed to recognize the ZVU efficiently. Thus, the proposed PDR method can reduce the position drift caused by the heading error and fault zero-velocity measurement. Experimental results demonstrate that the proposed method achieves better yaw estimate, as well as zero-velocity measurement, and obtains more accurate dead-reckoning position than other methods in the literature.

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering.

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.