A WiFi Assisted Pedestrian Heading Estimation Method Using Gyroscope

Personal location discovery and navigation within buildings has become an important research topic in the last years. One method to determine one's current position based on mobile-devices is to compare the set of available WiFi access points (APs), i.e. the fingerprint of a given space, to a previously collected database. In this context, this paper addresses the inherent problem of such systems that this fingerprint database needs to be established beforehand. Thus, situations can occur where a building is only partially represented in the database and localization can only be provided in a subset of the spaces of the building. This problem occurs especially in crowd-sourcing (organic) approaches where users consecutively contribute location-binds. In these situations an additional system is needed to provide localization. We present a first study on the fusion of pedestrian dead reckoning (PDR) from inertial sensors with position estimates from a WiFi localization system. We outline a possible design of particle filter and analyze its behavior on experimental data. We conclude that the outlined method can help to improve WiFi localization and is especially useful within crowd-sourcing environments.

Currently, pedestrian dead reckoning (PDR) is widely used in indoor positioning. Its principle is to recursively update the location of pedestrians by using the step count, step length and heading. To estimate the pedestrian heading, a common method in PDR is to utilize magnetometer measurements to estimate the heading of a smartphone. However, magnetometer is extremely vulnerable to magnetic interferences in indoor environments, resulting that magnetometer measurements are unreliable for heading estimation. Moreover, there is an offset angle between the device heading and the pedestrian heading, and the device heading may continuously change while the pedestrian heading keeps fixed. Therefore, the pedestrian heading cannot be simply replaced by the device heading. In this paper, we propose a step-wise method to accurately estimate the heading of a pedestrian who carries a smartphone at different positions. To be specific, the proposed method fuses the signals from the accelerometer, gyroscope and magnetometer of the smartphone to transform the device reference frame to the Earth reference frame, and then utilizes the horizontal angular velocities to establish an optimization problem, the analytical solution of which is obtained. Extensive simulations are carried out and confirm that the proposed method is more robust and more accurate than the existing algorithms.

Pedestrian dead reckoning (PDR) is a self-contained positioning technology and has been a significant research topic in recent years. Pedestrian-stride-length estimation is the core part of the PDR system and directly affects the performance of the PDR. The current stride-length-estimation method is difficult to adapt to changes in pedestrian walking speed, which leads to a rapid increase in the error of the PDR. In this paper, a new deep-learning model based on long short-term memory (LSTM) and Transformer, LT-StrideNet, is proposed to estimate pedestrian-stride length. Next, a shank-mounted PDR framework is built based on the proposed stride-length-estimation method. In the PDR framework, the detection of pedestrian stride is achieved by peak detection with a dynamic threshold. An extended Kalman filter (EKF) model is adopted to fuse the gyroscope, accelerometer, and magnetometer. The experimental results show that the proposed stride-length-estimation method can effectively adapt to changes in pedestrian walking speed, and our PDR framework has excellent positioning performance.

In the cloud age, it is quite easy to collect sensory data from smartphones. With these sensory data, it is desired to provide various kinds of applications to serve the user. In this research, we aim at developing an indoor navigation system on smartphone using solely smartphone sensory data. There are many researches on indoor localization and navigation in the literature. Nevertheless, environmental sensors and/or wearable sensors are usually needed. This can be costly and inconvenient. In this paper, we propose a smartphone indoor localization system using only accelerometer and gyroscope data from the smartphone. The Pedestrian Dead Reckoning (PDR) approach is used to build this system. The PDR approach is simple and efficient though seems traditional. The major weakness of the PDR is that the estimation error would accumulate over time. Thus we propose to add so-called calibration marks which look like short arrows and are placed on both the floor plan and the ground. To use the system, the user first finds a calibration mark on the ground, stands on it and faces the right direction. He/she then moves the android icon (representing the user) on top of the calibration mark on the floor plan on the smartphone. When the user starts to move, the android icon also moves on the floor plan following the real-time estimation of step length and moving direction change for each step from accelerometer and gyroscope data. This is a prototype of an indoor navigation system that can become fully functional after an optimal path planning module is included. Experimental results of estimated walking trace tests show high accuracy. The system is promising and useful as long as a floor plan and calibration marks are built in advance.

To improve the effect of college English teaching, combines multi-source data (MSD) fusion technology to build a college English blended teaching system. Moreover, through the research on the functions of smarter classrooms and the analysis and exploration of blended teaching, this paper designs a college English blended teaching system based on MSD fusion. In addition, this paper analyzes the bandwidth-tunable optical routing module of the interference-coupled series double-ring involved in multi-data fusion. Finally, this paper studies the working principle and structural design of the module, and constructs a college English blended teaching system structure based on MSD fusion. From the evaluation results, it can be seen that the college English blended teaching system based on MSD fusion proposed in this paper can effectively improve the quality of English teaching.

Wearable consumer devices have evolved into essential gateways for everyday health insights and location-based services. However, achieving clinical-grade physiological monitoring and seamless positioning on wrist-worn platforms remains challenging due to motion artifacts and signal degradation in complex environments. This paper proposes a robust, lightweight multi-modal system that integrates biosensors (photoplethysmography, PPG; electrocardiography, ECG) and motion sensors (inertial measurement unit, IMU) to achieve continuous health monitoring and seamless indoor-outdoor navigation. To mitigate the impact of user movement on consumer-grade sensors, we introduce a subspace projection-based artifact removal method using Hankel-SVD, significantly enhancing heart rate estimation robustness. Furthermore, we propose a factor graph-based framework that tightly fuses global navigation satellite system (GNSS) and Pedestrian Dead Reckoning (PDR) to ensure accurate trajectory tracking across environment transitions. Experimental results in real-world walking scenarios demonstrate that the proposed system maintains an average heart rate estimation error within 2.3 bpm (with >93% of samples <5 bpm) and blood pressure estimation error within 3–6 mmHg. In complex urban settings, the integrated localization method reduces average positioning errors by 34.1% and 64.3% compared to standalone GNSS and PDR, respectively. Characterized by low computational cost and high stability, this solution provides a viable paradigm for next-generation intelligent health management and emergency response in consumer wearables.

At present, indoor location-based service has become a hot topic. The traditional approaches that rely on professional equipment and expertise to collect indoor maps has limited the rapid development of indoor location-based services. Adopting low-cost crowdsourcing methods to construct and maintain indoor maps with various sensors integrated in smart phones by common users is a development trend of constructing indoor map. The existing crowdsourcing-based indoor topology construction methods mostly use the pedometer based PDR (Pedestrian Dead Reckoning) to infer user trajectories, which suffers from the problem of accumulated estimation errors. To reduce the error of pedestrian dead reckoning and improve the accuracy of the indoor topology construction, we propose a novel indoor topology construction algorithm based on the user closed trajectory fusion. The algorithm requires the user to start and end a trajectory loop at the same location when collecting data. Based on this trajectory loop, we can perform the forward and backward dead reckoning starting from the starting and the ending point of the path, respectively. For instance, after completing the forward path inference, we can swap the starting point with the ending point, and then perform the reverse path inference. Last, we aggregate the coordinates obtained from the forward and reverse path inferences with the different weight. The weight is defined to be proportional to the confidence of the PDR, i.e., inversely proportional to the walking time from the starting location of path inference. Compared to the traditional unidirectional trajectory inference method, our proposed algorithm can greatly reduce the accumulated error of dead reckoning by aggregating the bidirectional trajectory inference. To improve the accuracy of heading estimation of each step, we Ieverage a radial Gaussian function to filter the compass noise, and adopt the multi-strategy fusion mechanism based on the difference of direction estimation using the gyroscope and compass. The paper further introduces the external contour of the building to calibrate the direction of the indoor topological path, and improves the accuracy of the indoor topological path construction. The experimental results shows that our proposed crowdsourcing-based indoor topology construction algorithm using the bidirectional trajectory fusion scheme outperforms the traditional indoor topology construction algorithm that uses only the forward PDR and can obtain an average map accuracy of 0.40-meter.

In this paper, we proposed Advanced Heuristic Drift Elimination (AHDE) which can remove azimuth drift error in indoor environments. In Pedestrian Dead Reckoning (PDR) system, azimuth error is one of the main factors that cause estimated position error. In order to reduce azimuth error, several methods are used. Heuristic Drift Elimination (HDE) algorithm proposed by Johann Borenstein shows great strength in indoor environments. HDE assumes that generally walls and corridors are straight and either parallel or orthogonal to each other in man-made building. They called the typical directions of walls and corridors as the dominant directions. HDE is corrected if the computed azimuth angle matches the closest dominant direction. HDE also has limitation when the pedestrian walks in various directions because HDE can cause a new azimuth error by matching the closed dominant direction. To overcome these limitations, we propose AHDE which is based on INS-EKF-ZUPT (IEZ) by using foot-mounted IMU. The algorithm consists with the following two steps. First, it determines whether a pedestrian is walking straight forward or not. If a pedestrian is not walking straight forward, the algorithm estimates the biases of accelerometers and gyroscopes by Zero velocity UPdaTe (ZUPT) method. However if the pedestrian is walking straight forward, the algorithm determines whether the pedestrian is walking along the dominant direction or not. When it is determined that pedestrian is walking along the dominant direction, the algorithm corrects the computed azimuth angle to the closest dominant direction. When it is determined that the pedestrian is not walking along the dominant direction but walking straight with no change in azimuth, AHDE applies a correction to the gyro output which contains the bias error. Experimental results show that the accuracy of AHDE is improved compared to HDE and the algorithm is a powerful method which can reduce the azimuth error in complex motion.

Indoor positioning is currently a research hotspot. In recent years, Pedestrian Dead Reckoning (PDR) has been widely used in indoor positioning. However, the positioning error caused by heading drifts will accumulate as the walking distance increases, so some methods need to be used to correct the heading angle. Heuristic Drift Elimination (HDE) is an effective heading correction algorithm, which only uses the information of a building’s dominant directions to reduce the heading error, but it does not apply to the non-dominant direction condition. In this paper, we propose a heading drift suppressing method for the limitation of HDE. Firstly, the method constructs membership functions to judge the pedestrian’s motion according to the result of comprehensive evaluation. Then, it further determines by a threshold condition whether the pedestrian walks along the dominant directions, and a heading error measurement is introduced for heading correction. Finally, we verify by experiments that the proposed method can correct heading angles properly for different conditions, which indicates an adaptability to the environment.

Since 1991, the Research and Development (R&D) group at Lockheed Martin Canada (LM Canada) has been developing and demonstrating the application of Multi-Source Data Fusion (MSDF) techniques for target tracking and identification within the Naval Command and Control (C2) for the HALIFAX Class frigates. The current C2 as well as the sensor suite of the HALIFAX Class were designed in the early 80s and based around a proprietary hardware architecture. The sensor data is pre-processed and provided to the C2 in real time. Considering the late 70s and 80s design of the sensor interfaces, where a data fusion within the C2 was not a commonality, not all of the information beneficial for a data fusion system is provided to the C2. After a sequence of simulation and modelling efforts for an MSDF capability within the HALIFAX Class C2, this project is now at a point where real data captured from a ship trial on the HALIFAX Class is being injected into the MSDF. This is an ongoing activity and a number of iterations are foreseen before the MSDF becomes part of HALIFAX Class C2. This paper provides a summary of lessons learned in this exercise.

In this paper, the sensor is applied to the collection of rock parameter data. Aiming at the classification and evaluation of stability (i.e. rock quality), an attribute recognition model for the classification and evaluation of surrounding rock quality of underground engineering is established. Using multi-source data fusion and orthogonal numerical simulation test methods, the effects of rock mechanics parameters on the horizontal convergence of the tunnel, the settlement of the vault and the plastic zone coefficient are studied. Six factors (elastic modulus, Poisson’s ratio, internal friction angle, tensile strength, cohesion and density) and three levels of orthogonal experimental solutions were selected. The method of defining similar weight by using similar number to determine the weight of evaluation index, so as to calculate the comprehensive attribute measure, and apply confidence criteria to identify the stability of rock samples. Through the analysis and evaluation of rock mass quality classification of underground engineering, the application of the model and the evaluation method of rock mass quality classification are explained. The test results match the orthogonal test results; Considering the stability of tunnel envelope, the horizontal convergence, vault settlement and plastic zone coefficient after excavation should be comprehensively considered.

The zero-velocity update (ZUPT)-aided extended Kalman filter (EKF) is commonly used in the traditional inertial navigation system (INS)-based foot-mounted pedestrian dead reckoning (PDR) system, which can effectively suppress the error growth of the inertial-based pedestrian navigation systems. However, in the realistic test, the system still often suffers from drift, which is commonly caused by two reasons: failed detection of the stationary phase in the dynamic pedestrian gait and heading drift, which is a poorly observable variable of the ZUPT method. In this paper, firstly, in order to improve the initial heading alignment accuracy, a novel method to calibrate the PDR system’s initial absolute heading is proposed which is based on the geometric method. By using a calibration line rather than only using the heading of the starting point, the method can calibrate the initial heading of the PDR system more accurately. Secondly, for the problem of failed detection of the stationary phase in the dynamic pedestrian gait, a novel stationary phase detection method is proposed, which is based on foot motion periodicity rather than the threshold comparison principle in the traditional method. In an experiment, we found that the zero-speed state points always occur around the minimum value of the stationary detector in each gait cycle. By taking the minimum value in each gait cycle as the zero-speed state point, it can effectively reduce the failed detection of the zero-speed interval. At last, in order to reduce the heading drifts during walking over time, a new motion constraint method is exploited based on the range constraint principle. During pedestrian walking, the distance between the foot position estimates of the current moment and the previous stationary period is within the maximum stride length. Once the distance is greater than the maximum stride length, the constraint method is used to confine the current estimated foot position to the sphere of the maximum stride length relative to the previous stationary foot position. Finally, the effectiveness of all proposed methods is verified by the experiments.

This paper aims to design a method of multi-source data fusion in multi-target collaborative navigation. First, the respective features of GPS/INS/RS/MO data in the navigation process are clarified. Then a multi-source data fusion method is designed including GPS/INS data fusion with adaptive Kalman filter, RS/MO data fusion with ranging table matching of observation targets, and joint adjustment with fused GPS/INS and RS/MO data. Finally, a simulation experiment is carried out to verify the improvement in positioning efficiency and precision. The results show that collaborative navigation based on multi-source data fusion can increase the stability and accuracy of the navigation service.

Many studies utilize the signal strength of short-range radio systems (such as WiFi, ultrasound and infrared) to build a radio map for indoor localization, by deploying a large number of beacon nodes within a building. The drawback of such an infrastructure-based approach is that the deployment and calibration of the system are costly and labor-intensive. Some prior studies proposed the use of Pedestrian Dead Reckoning (PDR) for indoor localization, which does not require the deployment of beacon nodes. In a PDR system, a small number of sensors are put on the pedestrian. These sensors (such as a G-sensor and gyroscope) are used to estimate the distance and direction that a user travels. The effectiveness of a PDR system lies in its success in accurately estimating the user's moving distance and direction. In this work, we propose a novel waist-mounted based PDR that can measure the user's step lengths with a high accuracy. We utilize vertical acceleration of the body to calculate the user's change in height during walking. Based on the Pythagorean Theorem, we can then estimate each step length using this data. Furthermore, we design a map matching algorithm to calibrate the direction errors from the gyro using building floor plans. The results of our experiment show that we can achieve about 98.26% accuracy in estimating the user's walking distance, with an overall location error of about 0.48 m.

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.