An Adaptive Heading Estimation Method based on Holding Styles Recognition Using Smartphone Sensors

Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical particle filter(PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.

A Map aided Pedestrian Dead Reckoning (PDR) algorithm is proposed to mitigate the drift errors and step detection limitations of pedestrian dead reckoning algorithm with handheld sensors in indoor and outdoor spaces. Specific to this context is the changing lever-arm between the handheld device and the pedestrian center of mass that introduces a misalignment between the inertial sensors and the walking directions. To address these challenges, an adaptive routing graph is built based on possible pedestrian's motions, which depend on personal mobility profile and surroundings. An adaptive decision process is also developed to fuse map data with GNSS positions and PDR outputs in a particle filter. The performance is assessed with 1km walk experiments. Main contributions are (1) the calibration of the PDR step length model using both GNSS and map data during straight line travels with miss/over-detected steps modeled by the particle filter; (2) the estimation of angular misalignment between the walking and the handheld unit pointing directions in geometrically constrained areas; (3) a dynamic choice of opportune periods and measurements to calibrate the PDR outputs and improve the positioning process.

Substations are critical infrastructures in modern power systems, requiring accurate and reliable personnel positioning to ensure operational safety and inspection efficiency. Pedestrian dead reckoning (PDR) technology has emerged as a preferred solution for real-time seamless positioning due to its autonomous, anti-jamming and passive characteristics. However, in practical applications, the PDR algorithm faces challenges such as the inability to provide absolute positioning due to unknown starting points and cumulative errors over time. To address these challenges, this paper proposes a head-mounted pedestrian positioning system (HPPS) integrating fisheye camera/real-time kinematic (RTK)/PDR based on factor graph optimization (FGO). First, a skyward-facing fisheye camera identifies non-line-of-sight (NLOS) signals caused by architectural obstructions, improving the absolute positioning accuracy of RTK. Next, an adaptive pedestrian gait detection and step length estimation algorithm based on head-mounted inertial measurement units (IMU) is developed to enhance PDR robustness. Finally, the FGO framework integrates the inputs of the fisheye camera, RTK, and PDR to achieve seamless indoor-outdoor positioning. Experimental results demonstrate that the proposed system achieves horizontal root mean square error (RMSE) values of less than 0.42 m and 0.35 m in two distinct outdoor substation environments and less than 1.36 m in indoor-outdoor scenarios. This work highlights the integration of precise positioning technologies within the Internet of Things (IoT) framework, advancing smart grid applications and enabling effective location tracking in complex environments.

In recent years pedestrian dead reckoning (PDR) has been a popular indoor localization technique that takes advantage of inertial navigation sensors (INS) of smartphones due to its availability. Despite the convenience of INS, noise has been a recurring problem in PDR which encourages many researches on noise filtering. This paper proposes a model that utilizes empirical mode decomposition (EMD) as the noise filter. EMD is an unconventional filter being applied in PDR to remove noise by processing the raw signal before implementing step counts, step lengths and heading estimation. The experiment made using the proposed model was able to perform the standard PDR technique without overlaps in heading estimation.

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points.

Due to the high demand of location-based services in buildings, various indoor positioning methods have been proposed. Among them, the Pedestrian Dead Reckoning (PDR) systems have received much attention because of the widespread deployment of smartphones and no requirement of infrastructure. In this paper, we propose a PDR system to track a pedestrian holding a smartphone while he/she walks in a building. To eliminate the inevitable accumulated error over time, we suggest a method of using location related information from environments. We named it ‘landmark’, which is defined as a specific area (or point) where the pedestrian passes by in a building such as a stair, a door, a corner or a crossroad. If the system detects a landmark during the walking period, then it will correct the position of the user with the pre-defined position information of the landmark. In this work, we propose one kind of landmark called the door landmark. We have developed a method to detect the landmarks only using the sensors embedded in smartphones. The door landmark can be detected by combining the features from the magnetic sensor and user’s walking behavior. Via a set of experiments, we have evaluated the performances of the landmark detection and the position tracking of the system.

Complementary data are necessary to bind the positioning error growth of Pedestrian Dead Reckoning (PDR). In this paper, absolute position updates are made possible with the online detection of different types of points of interest (POIs) located on the map. The POIs are selected depending on specific motion patterns which are associated to absolute locations on the map. To create the POIs database, the correlation between pedestrian motion and different map locations is first studied and the outcome is a typology of POIs. A K-NN (K nearest neighbors) algorithm is used to train different motion modes, which are further exploited for the detection of POIs in order to update the PDR algorithm with position data. Experimental assessment of the POI-based PDR calibration is conducted in both outdoor and indoor spaces with a focus on the transition between both environments. 90% of the time, motion is correctly classified and the PDR position is corrected with an accuracy that depends on POIs features (width of corridor/door, staircase size…). Therefore, the approach is found to be promising for enhancing PDR positioning using only map data.

Navigation applications and location-based services are currently becoming standard features in smart phones with built-in GPS receivers. However, a ubiquitous navigation solution which locates a mobile user anytime anywhere is still not available, especially in Global Navigation Satellite System (GNSS) degraded and denied environments. Different motion sensors and angular sensors have been adopted for augmenting the positioning solutions for such environments. An electromyography (EMG) sensor, which measures electrical potentials generated by muscle contractions from human body, is employed in this paper to detect the muscle activities during human locomotion and captures the human walking dynamics for motion recognition and step detection in a Pedestrian Dead Reckoning (PDR) solution. The work presented in this paper is a consecutive step of our pilot studies in developing a novel and robust PDR solution using wearable EMG sensors. The PDR solution includes standing and walking identification, step detection, stride length estimation, and a position calculation with a heading angular sensor. A situation of standing still is identified from the EMG signals collected from a walking process, which has standing and walking dynamics, via a hidden Markov model classifier fed by sample entropy features. Such pre-classified processing reduces the misdetection rate of step detection. After step detection, two stride length estimation methods are investigated for the PDR solution. Firstly, a linear stride length estimation method based on statistic models is investigated to improve the accuracy of the PDR solution. Secondly, five different walking motions are recognized by a motion recognition algorithm based on some particular EMG features, and a fixed stride length is then set for each walking motion to propagate the position. To validate the effectiveness and practicability of the methods mentioned above, some field tests were conducted by a few testers. The test results indicate that the performance of the proposed PDR solution is comparable to that of a commercial GPS receiver in outdoor test under an open-sky environment.

The fifth-generation (5G) network can be used jointly with a global navigation satellite system (GNSS) to help relieve the problem of having few visible satellites in urban environments and achieve highly accurate localization. The 5G base stations (BSs) can provide range and angle measurements at a much higher rate than GNSS. However, this benefit has not been fully employed by existing GNSS-5G hybrid methods. In addition, the near–far effect of 5G BS causes a large dynamic range of the measurement noise uncertainty. This challenge has not been well dealt with by the existing adaptive noise estimation methods, resulting in the decline of positioning accuracy and system convergence. This work proposes a multiple-rate adaptive Kalman filter (MRAKF) for GNSS-5G hybrid positioning with a hybrid sequential fusing scheme. It supports the fusion of GNSS and 5G measurements at different data rates to better employ the high-rate 5G measurements. A distance-dependent model is derived to quantitatively characterize the effect of BS-user equipment (UE) distance over the measurement noise covariance, leading to a proactive measurement uncertainty prediction algorithm to adaptively adjust the observation noise covariance matrix. Furthermore, a switchover strategy is proposed to switch its positioning mode to avoid the impact of variation of available line of sight (LOS) BSs. Both simulations and a real driving test were carried out. Results show that the proposed MRAKF method significantly improves the positioning accuracy compared with the other four adaptive noise estimation methods and the standard EKF. It requires the lowest computation complexity among all adaptive methods in this work.

Smartphone mode classification is essential to many applications, such as daily life monitoring, healthcare, and indoor positioning. In the latter, it was shown that knowledge of the smartphone location on pedestrians can improve the positioning accuracy. Most of the research conducted in this field is focused on pedestrian motion in a horizontal plane. In this research, we use supervised machine learning techniques to recognize and classify the smartphone mode (text, talk, pocket and swing) while accounting for the movement up and downstairs. We distinguish between the going up and the down motion, each with four different smartphone modes, making eight states in total. This classification is based on the use of an optimal set of sensors that varies according to battery life and the energy consumption of each sensor. The classifier was trained and tested on a dataset constructed from multiple user measurements (total of 94 min) to achieve robustness. This provided an accuracy of more than 90% in the cross validation method and 91.5% if the texting mode is excluded. When considering only stairs motion, regardless of the direction, the accuracy improves to 97%. These results may assist many algorithms, mainly in pedestrian dead reckoning, in improving a variety of challenges such as speed and step length estimation and cumulative error reduction.

Pedestrian dead reckoning (PDR) uses built-in sensors in smartphones to track user positions, offering both versatility and portability. However, diversities among individuals and their behavior patterns decrease positioning accuracy. In this study, a two-stage personalized pedestrian dead reckoning based on neural networks (P2Net) is proposed. The first stage employs a human activity and smartphone location recognition (HSR) module, integrating a convolutional neural network with focal loss (FLCNN) to recognize nine modes, which constrains the subsequent optimized PDR procedure. In the second stage, a hybrid feature temporal attention network (HFTAN) is constructed to achieve generalized step length estimation across individual diversities. Temporal features via temporal convolutional network (TCN) and physical features are combined to generate hybrid features with time-series modeling capability and interpretability, which are then fed into a bi-directional long short-term memory (BiLSTM) model with a feature attention mechanism to estimate pedestrian step length. Experimental results demonstrate that P2Net achieves a class average accuracy of 92.75% for nine modes, and the total traveled distance error (TTDE) under various states is within 10%, outperforming other mentioned positioning methods.

The demand for pedestrian navigation has increased along with the rapid progress in mobile and wearable devices. This study develops an accurate and usable Step Length Estimation (SLE) method for a Pedestrian Dead Reckoning (PDR) system with features including a wide range of step lengths, a self-contained system, and real-time computing, based on the multi-sensor fusion and Fuzzy Logic (FL) algorithms. The wide-range SLE developed in this study was achieved by using a knowledge-based method to model the walking patterns of the user. The input variables of the FL are step strength and frequency, and the output is the estimated step length. Moreover, a waist-mounted sensor module has been developed using low-cost inertial sensors. Since low-cost sensors suffer from various errors, a calibration procedure has been utilized to improve accuracy. The proposed PDR scheme in this study demonstrates its ability to be implemented on waist-mounted devices in real time and is suitable for the indoor and outdoor environments considered in this study without the need for map information or any pre-installed infrastructure. The experiment results show that the maximum distance error was within 1.2% of 116.51 m in an indoor environment and was 1.78% of 385.2 m in an outdoor environment.

Purpose Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait. Design/methodology/approach The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds. Findings Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning. Originality/value A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

The topic of indoor positioning and indoor navigation by using observations from smartphone sensors is very challenging as the determined trajectories can be subject to significant deviations compared to the route travelled in reality. Especially the calculation of the direction of movement is the critical part of pedestrian positioning approaches such as Pedestrian Dead Reckoning (“PDR”). Due to distinct systematic effects in filtered trajectories, it can be assumed that there are systematic deviations present in the observations from smartphone sensors. This article has two aims: one is to enable the estimation of partial redundancies for each observation as well as for observation groups. Partial redundancies are a measure for the reliability indicating how well systematic deviations can be detected in single observations used in PDR. The second aim is to analyze the behavior of partial redundancy by modifying the stochastic and functional model of the Kalman filter. The equations relating the observations to the orientation are condition equations, which do not exhibit the typical structure of the Gauss-Markov model (“GMM”), wherein the observations are linear and can be formulated as functions of the states. To calculate and analyze the partial redundancy of the observations from smartphone-sensors used in PDR, the system equation and the measurement equation of a Kalman filter as well as the redundancy matrix need to be derived in the Gauss-Helmert model (“GHM”). These derivations are introduced in this article and lead to a novel Kalman filter structure based on condition equations, enabling reliability assessment of each observation.

Pedestrian Dead Reckoning (PDR) is a good alternative positioning method in Global Navigation Satellite Systems (GNSS)-challenged environments. It has the advantage in terms of infrastructure-independent, while the disadvantage is that, the estimated errors grow rapidly with time. The Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong transmission power, which contributes to better urban propagation and building penetration than GNSS signals. So, in this paper, an enhanced PDR algorithm aided with DTMB signals is proposed. In the study, a complete software-defined radio receiver is developed to estimate the Doppler speed and range information from DTMB signals. To reduce the accumulated errors of the PDR, the Extended Kalman Filter (EKF) algorithm is carried out to fuse the information of Doppler speed and ranging from the DTMB signals, as well as the information of walking speed and heading from the PDR. The results of the field tests showed that the 95% positioning error of the proposed algorithm is less than 3.94 m, while the PDR only is less than 9.19 m, which suggests the effectiveness of the proposed method to provide continuous positioning by fusing DTMB signals with PDR.