A multi-hypothesis particle filtering approach for pedestrian dead reckoning

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.

This paper proposes two schemes for indoor positioning by fusing Bluetooth beacons and a pedestrian dead reckoning (PDR) technique to provide meter-level positioning without additional infrastructure. As to the PDR approach, a more effective multi-threshold step detection algorithm is used to improve the positioning accuracy. According to pedestrians’ different walking patterns such as walking or running, this paper makes a comparative analysis of multiple step length calculation models to determine a linear computation model and the relevant parameters. In consideration of the deviation between the real heading and the value of the orientation sensor, a heading estimation method with real-time compensation is proposed, which is based on a Kalman filter with map geometry information. The corrected heading can inhibit the positioning error accumulation and improve the positioning accuracy of PDR. Moreover, this paper has implemented two positioning approaches integrated with Bluetooth and PDR. One is the PDR-based positioning method based on map matching and position correction through Bluetooth. There will not be too much calculation work or too high maintenance costs using this method. The other method is a fusion calculation method based on the pedestrians’ moving status (direct movement or making a turn) to determine adaptively the noise parameters in an Extended Kalman Filter (EKF) system. This method has worked very well in the elimination of various phenomena, including the “go and back” phenomenon caused by the instability of the Bluetooth-based positioning system and the “cross-wall” phenomenon due to the accumulative errors caused by the PDR algorithm. Experiments performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building in the China University of Mining and Technology (CUMT) campus showed that the proposed scheme can reliably achieve a 2-meter precision.

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.

Pedestrian Dead Reckoning algorithms are commonly used to assist pedestrian navigation with handheld sensors. The estimation of the walking direction remains an important source of positioning error of mobile phone users since this direction may be different from the device's pointing direction. A better understanding of human walking gait has enabled to produce new algorithms to mitigate the impact of the way the device is held in hand. WAISS algorithm is one of them. It is based on the study of horizontal hand accelerations and their modeling using Gaussian Mixture Models (GMM). However, ongoing search for universal modeling of handheld device carrying mode defeats the varying nature of human gait. This paper investigates the impact of individual gait characteristics and their modeling to improve the estimation of the walking direction. Different models are learned for curved and straight lines and varying GMM are proposed to account for inter-individual gait variations. This results in a reduced walking direction error with a 8.1° mean error to the 90th percentile computed for 3 subjects over a 1.5 km indoor/outdoor walk.

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.

Indoor localization using the magnetic fingerprint collected by the embedded sensors of smartphone has been under constant improvement with the widespread popularity of the smartphone. Most of the current systems rely on the Particle Filter (PF) or the Kalman Filter (KF) to combine the Pedestrian Dead Reckoning (PDR) with the magnetic or WiFi fingerprint for improving their accuracy. In this paper, a system using the PDR and the magnetic fingerprint is proposed for indoor localization, which is based on the combination of the Particle Filter (PF) and the Extend Kalman Filter (EKF). The system includes the Pedestrian Dead Reckoning (PDR) module and the magnetic fingerprint matching module. In particular, the hybrid indoor positioning algorithm which combines the Particle Filter (PF) and the Extend Kalman Filter (EKF) is proposed in the magnetic fingerprint matching module for the fusion of the results of the Pedestrian Dead Reckoning module and the magnetic fingerprint. This hybrid indoor positioning algorithm is the key component which can reduce the computation of the Particle Filter effectively and solve the inherent blindness and particles degeneration problem. The obtained results in the real scenarios show that our fusion system achieves better results than the widely adopted system in which the Particle Filter (PF) or the Kalman Filter (KF) is used. The evaluation shows the system achieves a localization accuracy about 1-2m on average in a large building.

As the global navigation satellite system (GNSS) faces the problems of signals blocked by various objects and absorbed by many materials in indoor environments, many indoor positioning methods based on smartphones' inertial measurement unit (IMU) become increasingly applicable. Pedestrian dead reckoning (PDR) as one of the most universal methods, several algorithms with high accuracy have been proposed based on it. However, many algorithms are only applicable to scenes where pedestrians are in the “going forward” state. When the pedestrian's motions include complex patterns such as sidestepping and walking backward, PDR will not work normally as the estimated result is far from the real trajectory. In this paper, we design a deep learning model assisted PDR algorithm to recognize the pedestrian's complex motion patterns (going forward, walking backward, left sidestepping, right sidestepping) from the smartphones' IMU. We firstly employ one combined model of trained convolution neural network (CNN) and long short-term memory (LSTM) neural network to determine the correction angle for different motion patterns. Then, we estimate the pedestrian's motion direction by adding the correction angle to the attitude estimated by the inertial navigation system (INS) mechanization. Finally, the position can be computed by the estimated stride length and the corrected motion direction. Compared with different algorithms, the experimental results have proved that our algorithm can effectively process data in complex motion patterns.

Commonly used Global Navigation Satellite Systems (GNSS) are inappropriate as Location Based Services (LBS) in indoor environment. Therefore research teams are developing different systems, which can be used as a suitable alternative. One of options is to use Inertial Navigation System (INS) which consists of inertial sensors and mathematic procedures. This concept has been known for a long time, but with arrival of Microelectro Mechanical System (MEMS) INS found wide use. Smartphones with inertial sensors, such as accelerometers and gyroscopes, allow us to use them as input devices for Pedestrian Dead Reckoning (PDR). In this paper we present PDR by using smartphone sensors. They can be classified as low-cost Inertial Measurement Unit (IMU), and have been compared with more precise and expensive Xsens IMU. Accuracy of inertial sensors has increased in the past few years, but they still cannot alone provide proper accuracy because of many negative effects, such as heading drift due to gyroscope bias. Particle Filter (PF) has been successfully used with map constraints to increase the accuracy of proposed location system. Presented results show that low-cost smartphone IMU combined with PF can be applicable as proper navigation system.

In recent years, the demand for location based service has promoted the rapid development of pedestrian navigation and positioning technology. Among them, Pedestrian Dead Reckoning (PDR) based on inertial sensor can greatly improve the robustness of indoor and outdoor pedestrian navigation system due to its environment-friendly. This paper designs a robust PDR algorithm based on smart phone. This paper constructs multi-level constraints based on INS algorithm and establishing observation equation needed by EKF using the mutually complementary relationship among sensors, such as gyroscope accelerometer and magnetometer, and human dynamic model. The cumulative error of the system is less than 2% of the traveling distance when using different intelligent terminals, holding on hand both in indoor and outdoor test.

Particle filter and Viterbi tracking algorithm are two representative sensor fusion methods to estimate a location of a mobile device by using various signals such as Wi-Fi, Bluetooth, magnetic fields and sensors like 3-axis accelerometer and gyroscope in indoor environments. This paper measures and compares the performance of the two sensor fusion methods on two paths in the first floor of KI building, KAIST. One path is a random walk and another is a rectangle path circumventing the area. On the rectangle path, the two methods showed a similar performance to each other, approximately 2m average error distance. On the other hand, on the random walk path, the Viterbi tracking algorithm significantly outperformed the particle filter in accuracy. This indicates that the choice of sensor fusion method influences a little to positioning accuracy when pedestrian dead reckoning (PDR) is working properly. However, when the PDR is not properly working, the choice of sensor fusion method greatly influences to positioning accuracy. In our test, the Viterbi tracking algorithm revealed to be much more resilient to PDR errors than particle filters.

The use of wearable foot-based inertial measurement units (IMUs) incorporated in a navigation system can address the problem of single-person location tracking in situations and environments where GPS signals may be unavailable or inconsistent; e.g., in buildings. This Pedestrian Dead Reckoning (PDR) approach, which enables standalone personal tracking, is an idea that has been attempted many times with varying degrees of success. There are two main approaches to solve this difficult problem: the first is to use inertial measurement units (IMUs) in a filter to apply stride-length, heading correction, and zero-velocity updates to a Kalman filter to get a position solution, while the other is to use Machine Learning techniques to better identify and classify a person’s movement to achieve an accurate position. This paper continues on the path of the former method by investigating the feasibility of PDR using a pair of low cost IMUs along with a pair of relative position and attitude magnetic sensors connected to an individual’s feet. Laverne, et al., [1] have shown that incorporating a ranging measurement between two IMU-based foot sensors significantly increases the accuracy of the pedestrian dead reckoning (PDR) solution. This work will be utilizing a solution more desirable for wearable PDR applications by incorporating smaller, low cost, commercial grade IMUs. To compensate for this degradation in quality, the ranging sensor is replaced with two small magnetic sensors that provide a 6 degree-of-freedom relative pose estimate between the sensors. In addition to a ranging measurement, this paper will introduce the use of a relative rotation measurement update to assist with drift reduction.

This paper provides a hybrid approach between Wi-Fi and pedestrian dead reckoning (PDR) for the iPhones in indoor environments, and addresses the following two problems. First, since Apple Inc. no longer provides public information about currently-connected Wi-Fi (e.g., service set identifier, received signal strength and channel), Wi-Fi based pedestrian tracking apps that run on the iPhones are more restricted compared to other ones on the Android platforms. Second, even the PDR approach provides such a great way for self-localization, it suffers from accumulated errors of inertial sensors embedded in the smartphones. We propose a conversion function from a Wi-Fi status value to a proximity for the localization purpose. Then, a mobile iPhone collects mobility information from inertial measurements unit (IMU), inputs to the PDR, and combines with Wi-Fi proximity in order to perform accurate self-localization and tracking. Moreover, we improve the PDR by reducing drifting effects caused by the IMU biases. Experiment results show that the proposed scheme is effective and has low complexity, while bringing the benefits from smartphone IMU.

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.

Automatic lecture recording is an appealing alternative approach to manually recording lectures in the process of online course making as it can to a large extent save labor cost. The key of the automatic recording system is lecturer tracking, and the existing automatic tracking methods tend to lose the target in the case of lecturer’s rapid movement. This article proposes a lecturer tracking system based on MobileNet-SSD face detection and Pedestrian Dead Reckoning (PDR) technology to solve this problem. First, the particle filter algorithm is used to fuse the PDR information with the rotation angle information of the Pan-Tilt camera, which can improve the accuracy of detection under the tracking process. In addition, to improve face detection performance on the edge side, we utilize the OpenVINO toolkit to optimize the inference speed of the Convolutional Neural Networks (CNNs) before deploying the model. Further, when the lecturer is beyond the camera’s field of view, the PDR auxiliary module is enabled to capture the object automatically. We built the entire lecture recording system from scratch and performed the experiments in the real lectures. The experimental results show that our system outperforms the systems without a PDR module in terms of the accuracy and robustness.

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.