Pedestrian Positioning System Research Articles

Seamless Indoor–Outdoor Foot-Mounted Inertial Pedestrian Positioning System Enhanced by Smartphone PPP/3-D Map/Barometer

Seamless Indoor–Outdoor Foot-Mounted Inertial Pedestrian Positioning System Enhanced by Smartphone PPP/3-D Map/Barometer

Pedestrian Sensing and Positioning System Using 2D-LiDAR Based on Artificial Neural Networks Toward 6G

Pedestrian Sensing and Positioning System Using 2D-LiDAR Based on Artificial Neural Networks Toward 6G

Hybrid pedestrian positioning system using wearable inertial sensors and ultrasonic ranging

Pedestrian positioning system (PPS) using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare, emergency rescue, soldier positioning, etc. The performance of traditional PPS is limited by the cumulative error of inertial sensors, complex motion modes of pedestrians, and the low robustness of the multi-sensor collaboration structure. This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging (H-PPS). A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node, and enhanced by a novel ellipsoid constraint model. In addition, a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes, which effectively reduces the cumulative error originating from inertial sensors. Finally, a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator, and multi-level constraints are extracted to further improve the performance of the overall system. Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.

Pedestrian Positioning Using an Enhanced Ensemble Transform Kalman Filter.

Due to the unavailability of GPS indoors, various indoor pedestrian positioning approaches have been designed to estimate the position of the user leveraging sensory data measured from inertial measurement units (IMUs) and wireless signal receivers, such as pedestrian dead reckoning (PDR) and received signal strength (RSS) fingerprinting. This study is similar to the previous study in that it estimates the user position by fusing noisy positional information obtained from the PDR and RSS fingerprinting using the Bayes filter in the indoor pedestrian positioning system. However, this study differs from the previous study in that it uses an enhanced state estimation approach based on the ensemble transform Kalman filter (ETKF), called QETKF, as the Bayes filer for the indoor pedestrian positioning instead of the SKPF proposed in the previous study. The QETKF estimates the updated user position by fusing the predicted position by the PDR and the positional measurement estimated by the RSS fingerprinting scheme using the ensemble transformation, whereas the SKPF calculates the updated user position by fusing them using both the unscented transformation (UT) of UKF and the weighting method of PF. In the field of Earth science, the ETKF has been widely used to estimate the state of the atmospheric and ocean models. However, the ETKF algorithm does not consider the model error in the state prediction model; that is, it assumes a perfect model without any model errors. Hence, the error covariance estimated by the ETKF can be systematically underestimated, thereby yielding inaccurate state estimation results due to underweighted observations. The QETKF proposed in this paper is an efficient approach to implementing the ETKF applied to the indoor pedestrian localization system that should consider the model error. Unlike the ETKF, the QETKF can avoid the systematic underestimation of the error covariance by considering the model error in the state prediction model. The main goal of this study is to investigate the feasibility of the pedestrian position estimation for the QETKF in the indoor localization system that uses the PDR and RSS fingerprinting. Pedestrian positioning experiments performed using the indoor localization system implemented on the smartphone in a campus building show that the QETKF can offer more accurate positioning results than the ETKF and other ensemble-based Kalman filters (EBKFs). This indicates that the QETKF has great potential in performing better position estimation with more accurately estimated error covariances for the indoor pedestrian localization system.

MMGraphSLAM: Autonomous indoor positioning based on millimeter Wave GraphSLAM

Currently, it is still challenging to design an indoor positioning system with high precision and ubiquitousness. Recently, more attentions have been paid to the application of millimeter wave (mmWave) Radar because of its high sensing accuracy, autonomy and ubiquitousness. This work presents an autonomous indoor pedestrian positioning system based on mmWave GraphSLAM (MMGraphSLAM) without additional beacon infrastructure. In MMGraphSLAM, trajectories based on inertial sensor are optimized by mmWave Radar and GraphSLAM. We use PointNet Auto-Encoder neural network to enhance the point cloud data from mmWave Radar. Moreover, MMGraphSLAM fuses the point cloud and intensity-range information to improve the accuracy of loop closure detection. Then feature information is allocated with different weights to guarantee the reliability of back-end in MMGraphSLAM. A set of comprehensive evaluations in multiple experiments illustrate that our proposed system can better recover pedestrian’s walking trajectories and reach a sub-meter positioning accuracy without additional signal infrastructure.

A tri-IMUs pedestrian positioning system based on single-lower-limb kinematic constraints

Single-sensor zero-velocity-update (ZUPT)-aided pedestrian inertial navigation system (PINS) is a conventional method. However, the single-sensor systematic error still causes imprecise estimation of step length and high drift of orientation. In this study, three magnetometers and inertial measurement units are mounted on single-leg toe, shank, and thigh, respectively. Gaits are divided into two phases: stance and swing. Based on the biomechanical characteristics of a human lower limb, a velocity-position estimation algorithm is proposed. The positions of the three sensors are fused by dynamic geometric relationships, which are formulated through the natural lower-limb model and attitude estimation. A velocity-difference constraint is proposed to suppress the divergence of velocity, and Coriolis-based velocity correction is designed to observe velocities of the toe, shank, and thigh at the stance phase. A MAHONY-linear-Kalman framework separately estimates attitudes, velocities, and positions to reduce calculations. The proposed method is compared with foot-mounted, shank-mounted, and thigh-mounted ZUPT-aided PINS as well as a dual-sensor foot-to-foot algorithm through experiments. In the flat terrain experiment, compared with the errors of the Shank-mounted INS, the Thigh-mounted INS, the Foot-mounted INS, the Foot-to-Foot Constraint Method, the root mean squared error (RMSE) of the proposed method can be reduced by 40.34%, 71.85%, 29.53%, and 7.08%, respectively. In the slope experiment, compared with the other four methods, the RMSE of the proposed method can be reduced by 80%, 86.74%, 67.54%, and 61.86%, respectively. In the natural terrain experiment, compared with the other four methods, the RMSE of the proposed method can be reduced by 81.63%, 96.27%, 70%, and 60.87% respectively. The results show that the proposed method greatly suppresses the positioning and orientation errors in different scenes.

Consumer-Grade Inertial Measurement Units Enhanced Indoor Magnetic Field Matching Positioning Scheme

Magnetic field matching (MFM) positioning is one of the mainstream methods of consumer indoor positioning, which has attracted great attention from academia and industry. However, efficient methods for constructing magnetic field maps and matching positioning methods with high stability still need to be developed. This study proposes an indoor MFM positioning scheme enhanced by consumer-grade inertial measurement units that can efficiently generate a magnetic field grid map and achieve robust matching positioning, without the need to actively calibrate the magnetometer bias. When generating the magnetic field map, the proposed method employs a pedestrian positioning and orientation system to efficiently collect data by releasing individual behavioral constraints and reducing the number of control points required. Moreover, a magnetometer bias autocalibration method, aided by the precise attitude, is proposed to simplify the data collection process. During real-time positioning, the position and attitude generated by pedestrian dead reckoning (PDR) are used to generate the differential magnetic field strength in the sensor frame; this achieves matching positioning that is independent of the magnetometer bias. Furthermore, an estimation method for the MFM position noise, based on the magnetic field gradient, is proposed to improve the accuracy of MFM/PDR integrated positioning. Several experiments were conducted to verify the feasibility and performance of the proposed scheme. Only slight differences were found between the magnetic field maps using three different smartphones, showing that the proposed scheme can efficiently generate a high-precision magnetic map. The positioning results of multiple tests conducted using eight different smartphones revealed that the proposed scheme achieves continuous and robust meter-level positioning accuracy.

Distributed Collaborative Pedestrian Inertial SLAM With Unknown Initial Relative Poses

Collaborative indoor positioning techniques for pedestrians have been extensively researched in the past years, particularly concerning the range-based collaborative indoor positioning system. However, range-based indoor collaboration methods suffer from nonline-of-sight (NLOS) and electromagnetic signal loss in indoor environments. Meanwhile, these methods require prior knowledge of the initial relative pose of the pedestrian, which is difficult to obtain in such an environment. To overcome the unreliable mutual observation and the lack of initial relative poses information in collaborative position systems, this article proposes a distributed collaborative inertial simultaneous localization and mapping (DCOGI-SLAM) framework for collaborative pedestrian positioning systems in unknown indoor environments without prior information. A coarse alignment method of relative poses based on encounter events is proposed to obtain the initial relative poses of pedestrians. A mutual position observation is constructed based on the map constructed through the occupancy grid-based inertial SLAM (OGI-SLAM) method to provide a stable innovation for collaborative correction of positioning errors, insulating the system from the NLOS and the loss of ranging signals. Moreover, a distributed collaborative occupancy grid-based inertial simultaneous localization and mapping (DCOGI-SLAM) framework is proposed to enable each pedestrian positioning system in a formation to operate independently. In a two-person collaborative experiment over a space of approximately 2500 m2, the proposed system can obtain comparable accuracy to single OGI-SLAM with known initial relative position information, when the initial relative position information is unknown. The average positioning error of the proposed method is 1.48 m.

Pedestrian Trajectory Estimation Based on Foot-Mounted Inertial Navigation System for Multistory Buildings in Postprocessing Mode

Acquiring accurate and reliable pedestrian trajectories is essential for providing indoor location-based services. Although a foot-mounted inertial navigation system (Foot-INS) can acquire pedestrian trajectories in multistory buildings, it will inevitably encounter heading divergence because the constraint information is not always valid. Therefore, we proposed an accurate and convenient postprocessing indoor pedestrian positioning system (IPPS) to acquire pedestrian trajectories in multistory buildings such as shopping malls. Based on the hypotheses that the start and end points of the pedestrian trajectories on a single floor were closed, and the horizontal position of the closing point on each floor was identical. Therefore, in the single floor of multistory buildings, we use the closing point to control the trajectory drift error caused by the Foot-INS, and use a smoothing algorithm to reasonably distribute the drift error to the entire trajectory. Heading divergence is unavoidable with the Foot-INS, result in the pedestrian trajectories acquired on different floors were rotationally offset. Because pedestrian trajectories can epitomize the building orientation and provide an opportunity to align those trajectories on multistory buildings, an algorithm was proposed to match the trajectories acquired on different floors. A hybrid simulation experiment was conducted using an accurate reference object to evaluate the positioning performance of the proposed IPPS. The effectiveness of acquiring pedestrian trajectories was also confirmed by various experimental tests in a large shopping mall. The study findings suggest that the proposed IPPS is self-contained, low cost, and has the potential for large-scale applications.

PEDESTRIAN INERTIAL NAVIGATION ALGORITHM BASED ON SCENE RECOGNITION

Abstract. As indoor location-based services become increasingly essential to people's daily life, it is necessary to build a stable and accurate indoor pedestrian positioning system. The foot-mounted inertial navigation system can provide a short-term robust solution but suffer from error accumulation over time. To alleviate this issue, this paper proposes a pedestrian inertial navigation algorithm based on scene recognition to reduce the heading drift. Based on the hypothesis that corridors in buildings are generally narrow and straight and pedestrians have a high probability to walk in straight lines in corridors, we use a scene recognition model to assist foot-mounted INS. When the scene recognition model determines that the pedestrian is walking in a corridor, a straight-line constraint will be implemented to reduce the heading drift and improve the observability of the vertical gyroscope bias. Experiments show that the algorithm can effectively improve the navigation performance and the observability of vertical gyroscope bias when the sensor biases are significant.

The Improved 3D Pedestrian Positioning System Based on Foot-Mounted Inertial Sensor

The foot-mounted navigation system based on Micro Inertial Measurement Unit (MIMU) can estimate the position of pedestrians. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial sensor. However, the low accuracy and high noise of the MIMU will greatly affect the measurement accuracy of zero velocity detector, which will lead to poor 3D pedestrian positioning accuracy. An improved generalized likelihood ratio test (GLRT) method is proposed by establishing adaptive thresholds at different gait frequency. The core is to dynamically adjust the detection threshold according to different motion states, so as to determine the constraint relation for detection. In addition, the drift of sensors and the accumulated error of position integration affect the accuracy of attitude and position. Therefore, an improved extended Kalman particle filter (EKPF) is proposed. The improved EKPF constructs the position constraint model in altitude direction by introducing barometer information. The new particle filter distribution function is constructed by constrained multi-source observation information, which makes the posterior probability distribution closer to the real distribution. In order to verify the feasibility and effectiveness of this method, the inertial pedestrian navigation system is established. The experimental results demonstrate that the improved GLRT method performs well in detecting the zero velocity point of the foot, and improved EKPF method reduces the positioning error by 69.25% and 53.57% compared with the EKF and PF, respectively. Therefore, the proposed method can improved the 3D pedestrian positioning accuracy.

HPIPS: A High-Precision Indoor Pedestrian Positioning System Fusing WiFi-RTT, MEMS, and Map Information.

In order to solve the problem of pedestrian positioning in the indoor environment, this paper proposes a high-precision indoor pedestrian positioning system (HPIPS) based on smart phones. First of all, in view of the non-line-of-sight and multipath problems faced by the radio-signal-based indoor positioning technology, a method of using deep convolutional neural networks to learn the nonlinear mapping relationship between indoor spatial position and Wi-Fi RTT (round-trip time) ranging information is proposed. When constructing the training dataset, a fingerprint grayscale image construction method combined with specific AP (Access Point) positions was designed, and the representative physical space features were extracted by multi-layer convolution for pedestrian position prediction. The proposed positioning model has higher positioning accuracy than traditional fingerprint-matching positioning algorithms. Then, aiming at the problem of large fluctuations and poor continuity of fingerprint positioning results, a particle filter algorithm with an adaptive update of state parameters is proposed. The algorithm effectively integrates microelectromechanical systems (MEMS) sensor information in the smart phone and the structured spatial environment information, improves the freedom and positioning accuracy of pedestrian positioning, and achieves sub-meter-level stable absolute pedestrian positioning. Finally, in a test environment of about 800 m2, through a large number of experiments, compared with the millimeter-level precision optical dynamic calibration system, 94.2% of the positioning error is better than 1 m, and the average positioning error is 0.41 m. The results show that the system can provide high-precision and high-reliability location services and has great application and promotion value.

Improved Position Accuracy of Foot-Mounted Inertial Sensor by Discrete Corrections from Vision-Based Fiducial Marker Tracking.

In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. The main contribution of this work comes from the observation that different walking types (e.g., forward walking, sideways walking, backward walking) lead to different levels of position and heading error. Our approach takes this into account when accumulating the error, thereby leading to more-accurate estimations. Through experimentation, we show the variation in error accumulation and the improvement in accuracy alter when and how often to activate the camera tracking system, leading to better balance between accuracy and power consumption overall. The IMU and vision-based systems are loosely coupled using an extended Kalman filter (EKF) to ensure accurate and unobstructed positioning computation. The motion model of the EKF is derived from the foot-mounted IMU data and the measurement model from the vision system. Existing indoor positioning systems for training exercises require extensive active infrastructure installation, which is not viable for exercises taking place in a remote area. With the use of passive infrastructure (i.e., fiducial markers), the positioning system can accurately track user position over a longer duration of time and can be easily integrated into the environment. We evaluated our system on an indoor trajectory of 250 m. Results show that even with discrete corrections, near a meter level of accuracy can be achieved. Our proposed system attains the positioning accuracy of 0.55 m for a forward walk, 1.05 m for a backward walk, and 1.68 m for a sideways walk with a 90% confidence level.

Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter

The Foot-mounted Inertial Pedestrian-Positioning System (FIPPS) based on the Micro-Inertial Measurement Unit (MIMU) is a good choice for the forest fire fighters when the Global Navigation Satellite System is unavailable. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial navigation. However, the performance of ZUPT is highly affected by the low accuracy and high noise of the MIMU. The accuracy of conventional ZUPT for attitude alignment is reduced by the zero offset of acceleration and the drift of a gyroscope during the standing phase. An initial alignment algorithm based on Adaptive Gradient Descent Algorithm (AGDA) is proposed. In the stepping phase, the extended Kalman filter (EKF) is often used to correct attitude and position in track estimation. However, the measurement noise of the EKF is influenced by the high-frequency acceleration and angular velocity. Thus, the accuracy of the attitude and position will decrease. A double-constrained extended Kalman filtering (DEKF) is proposed. An adaptive parameter positively correlated with the acceleration and angular velocity is set, and the measurement noise in the DEKF is adaptively adjusted. The performance of the proposed method is verified by implementing the pedestrian test trajectory using MPU-9150 MIMU manufactured by InvenSense. The results show that the attitude error of the AGDA is 33.82% less than that of the conventional GDA. The attitude error of DEKF is 21.70% less than that of the conventional EKF. The experimental results verify the effectiveness and applicability of the proposed method.

Research on the Heading Calibration for Foot-Mounted Inertial Pedestrian-Positioning System Based on Accelerometer Attitude

Foot-mounted inertial pedestrian positioning (FIPP) plays an important role for facilitating pedestrian activities. It is suitable for indoor environment applications where global navigation satellite systems are unavailable such as during firefighting and military actions. However, the positioning error of FIPP can increase rapidly due to the measurement noise of the sensors. Zero Velocity Update (ZUPT) is an error correction method proposed to solve this accumulative error. However, the heading misalignment angle, which results in a continuous increase in the positioning error, cannot be estimated by ZUPT. In order to solve this problem, the improved ZUPT based on the Improved Attitude Algorithm (IAA) according to accelerometer measurements is proposed in this paper. When a pedestrian is in the stance phase, the horizontal attitude is estimated by using accelerometer measurements. According to the relationship between the heading misalignment angle and horizontal attitude, the heading misalignment angle is obtained by a series of mathematical derivations. By taking the velocity error and the attitude misalignment angle as observations, the heading misalignment angle and positioning error can be estimated and compensated for through the Kalman filter. Finally, we use MTI-G710 sensor manufactured by XSENS for the actual test and the experiment results show that the proposed method is effectively correct.

Three-dimensional indoor location estimation using single inertial navigation system with linear regression

This paper presents a 3D pedestrian position estimation algorithm using a waist-mounted inertial measurement unit (IMU) sensor. When using the IMU for position estimation, the measurement accuracy is degraded by the influence of the gyroscope’s drift error. This study proposes reducing the accumulated drift error by using the quaternion method to obtain an accurate Euler angle and combining this with step estimation to achieve 2D location estimation. For an indoor pedestrian positioning system, a pedestrian’s height accuracy is related to the reliability of the entire positioning system. In order to enhance 3D location estimation, an estimation algorithm for realizing the indoor height of pedestrians is proposed. This algorithm, which is based on the acceleration value of the accelerometer without using any external equipment, is combined with an algorithm which is additionally proposed herein. Moreover, to obtain corresponding decision points to distinguish between the behaviors of upstairs and downstairs movement, and to solve the problem of different decision points in different experiments, a linear regression algorithm is adopted, which improves the generalization ability of the algorithm for distinguishing between upstairs and downstairs movement. The effectiveness and validity of the proposed algorithm in 2D and 3D space is demonstrated by practical experiments.

Research on the Forward and Reverse Calculation Based on the Adaptive Zero-Velocity Interval Adjustment for the Foot-Mounted Inertial Pedestrian-Positioning System.

Pedestrian-positioning technology based on the foot-mounted micro inertial measurement unit (MIMU) plays an important role in the field of indoor navigation and has received extensive attention in recent years. However, the positioning accuracy of the inertial-based pedestrian-positioning method is rapidly reduced because of the relatively low measurement accuracy of the measurement sensor. The zero-velocity update (ZUPT) is an error correction method which was proposed to solve the cumulative error because, on a regular basis, the foot is stationary during the ordinary gait; this is intended to reduce the position error growth of the system. However, the traditional ZUPT has poor performance because the time of foot touchdown is short when the pedestrians move faster, which decreases the positioning accuracy. Considering these problems, a forward and reverse calculation method based on the adaptive zero-velocity interval adjustment for the foot-mounted MIMU location method is proposed in this paper. To solve the inaccuracy of the zero-velocity interval detector during fast pedestrian movement where the contact time of the foot on the ground is short, an adaptive zero-velocity interval detection algorithm based on fuzzy logic reasoning is presented in this paper. In addition, to improve the effectiveness of the ZUPT algorithm, forward and reverse multiple solutions are presented. Finally, with the basic principles and derivation process of this method, the MTi-G710 produced by the XSENS company is used to complete the test. The experimental results verify the correctness and applicability of the proposed method.

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model.

Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value.

Performance Enhancement of Indoor Pedestrian Positioning with Two-Order Bayesian Estimation Based on EKF and PF

To improve the accuracy of indoor pedestrian positioning, an indoor pedestrian positioning system with two-order Bayesian estimation based on Extended Kalman Filter (EKF) and Particle Filter (PF) is proposed in this paper. The presented system combines a foot-mounted inertial sensor, a Wi-Fi propagation model and building structure to make good use of these information resources. There are two updates in this system in order to limit the accumulative errors of inertial sensors. In the first update, the inertial navigation system (INS) is the main system in the calculation of pedestrian positioning, and Zero-velocity update (ZUPT) is introduced as the reference to correct the accumulative errors of INS based on EKF. To further limit the accumulative errors of inertial sensors, the estimated results obtained from the first update, including horizontal position information, are introduced as the observations based on PF in the second update; Pedestrian Dead Reckoning (PDR) is the main system in the calculation of pedestrian positioning, and the weight of particles is determined by the Wi-Fi propagation model, building structure information and output of the first update. The results show that the accuracy of positioning is effectively increased.

Advanced Pedestrian Positioning System to Smartphones and Smartwatches.

In recent years, there has been an increasing interest in the development of pedestrian navigation systems for satellite-denied scenarios. The popularization of smartphones and smartwatches is an interesting opportunity for reducing the infrastructure cost of the positioning systems. Nowadays, smartphones include inertial sensors that can be used in pedestrian dead-reckoning (PDR) algorithms for the estimation of the user’s position. Both smartphones and smartwatches include WiFi capabilities allowing the computation of the received signal strength (RSS). We develop a new method for the combination of RSS measurements from two different receivers using a Gaussian mixture model. We also analyze the implication of using a WiFi network designed for communication purposes in an indoor positioning system when the designer cannot control the network configuration. In this work, we design a hybrid positioning system that combines inertial measurements, from low-cost inertial sensors embedded in a smartphone, with RSS measurements through an extended Kalman filter. The system has been validated in a real scenario, and results show that our system improves the positioning accuracy of the PDR system thanks to the use of two WiFi receivers. The designed system obtains an accuracy up to 1.4 in a scenario of 6000 .