Smart Fusion of Multi-sensor Ubiquitous Signals of Mobile Device for Localization in GNSS-Denied Scenarios (original) (raw)
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New GNSS precise positioning by mobilephones
2019
In Android Release N ("Nougat"), Google introduced APIs giving access to GNSS raw measurements from android smartphones. After this announcement, users will be able to log GNSS raw measurements such as GPS satellite information (C/No, azimuth, elevation if a particular satellite has been used in the PVT), NMEA sentences and PVT solution with the proper time stamp. This can open many possibilities for the single-frequency GNSS receivers, also called low-cost GNSS receiver, integrated in smartphones. An opportunity to do better and complete research with GNSS raw measurements from the singlefrequency GNSS receivers can improve the accuracies for smart mobile devices. Tens meters accuracy could be enough for most of daily navigation purposes; however, it could be better to increase it for some particular cases: usages needs higher accuracy (decimeter of accuracy) like navigation for impaired people. In this aspect, mainly, the research investigates assessment of kinematic and short session static accuracies with single frequency low-cost receivers. Additionally, shares what kind of difficulties are faced, how good the results are evaluated, when the single-frequency GNSS receiver raw measurements are processed and analyzed with well know software packages for geodetic postprocessing. To explain in detail, in the research, accuracy assessment of relative static point positioning is practiced for U-blox LEA-4T. U-blox is a single-frequency GNSS receiver that measures the pseudoranges with the C/A code only. For the research, U-blox data quality is investigated for short sessions. In this aspect, the research contains the processing and analysis of static positioning for short time periods with U-blox. Besides, RTKLIB has been tested for processing the U-blox data in short static sessions. Then, Samsung Galaxy S8 is studied as low-cost single frequency receiver with kinematic, single point positioning, DGPS and static point positioning approach. In order to log desired data in android smartphone, OruxMap, GNSS Logger, Geo++ RINEX Logger and RinexOn applications are tested. Estimated point positions in real-time by Samsung Galaxy S8 chipset from API Level 23 and estimated point positions by RTKLIB post-processing from API Level 24 are analyzed in detail.
International Association of Geodesy Symposia, 2020
Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. As part of the joint IAG/FIG Working Groups 4.1.1 and 5.5 on Multi-sensor Systems, a benchmarking measurement campaign was conducted at The Ohio State University. Initial experiments have demonstrated that Cooperative Localization (CL) is extremely useful for positioning and navigation of platforms navigating in swarms or networks. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were equipped with combinations of GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), Raspberry Pi units, cameras, Light Detection and Ranging (LiDAR) and inertial sensors for CL. Pedestrians wore a specially designed helmet equipped with some of these sensors. An overview of the experimental configurations, test scenarios, characteristics and sensor specifications is given. It has been demonstrated that all involved se...
International Journal of Innovative Research in Computer Science and Technology, 2024
Accurate localization is crucial for numerous applications, spanning from navigation systems to indoor positioning and asset tracking. However, achieving precise localization remains challenging, especially in environments where traditional positioning technologies face limitations. To address this challenge, this paper proposes a novel approach: the fusion of multiple positioning technologies. By integrating data from GPS, Wi-Fi, Bluetooth, RFID, and other sensors, our framework aims to enhance localization accuracy, robustness, and adaptability across diverse environments. We present a comprehensive fusion algorithm that combines geometric, probabilistic, and machine learning techniques, while incorporating context-awareness mechanisms for adaptive localization. Through simulations and real-world experiments, we demonstrate the effectiveness of our fusion framework in improving localization accuracy and resilience to environmental factors. This research contributes to advancing the state-of-the-art in localization technologies and opens avenues for innovative applications in transportation, healthcare, retail, and beyond.
Mobile Positioning Techniques and Systems: A Comprehensive Review
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The recent developments in mobile positioning technologies and the increasing demands of ubiquitous computing have significantly contributed to sophisticated positioning applications and services. Position information represents a core element in the human-centred activities, assisting in visualising complex environments effectively and providing a representational coordinate for localisation, tracking, and navigation purposes. The emergence of smartphones has accelerated the development of cutting-edge positioning-based systems since they are contained to have more processing, memory, and battery power. Similarly, mobile devices are now equipped with new sensory capabilities, wireless communications, and localisation technologies. This has quadrupled towards new advances on positioning techniques, enhancing the existing ones and brought more value to positioning-based systems. Research studies in positioning techniques have progressed in different directions, and no work has catego...
DINGPOS: High sensitivity GNSS platform for deep indoor scenarios
2010 International Conference on Indoor Positioning and Indoor Navigation, 2010
Deep indoor scenarios are one of the most challenging areas of application for satellite navigation (GNSS) in personal navigation devices. Especially severe signal attenuation, as well as heavy multipath is constraining the use of GNSS for deep indoor applications. The project DINGPOS is focusing on the development of a platform for pedestrian users which can acquire and track GNSS signals also in most adverse indoor signal conditions. The main idea of the concept is the extension of coherent signal integration time of the GNSS receiver to the domain of several seconds, which increases the correlation gain significantly. To facilitate this goal, a very long and very precise signal replica is needed. Therefore the system must reproduce the user motion, the navigation message data bits and the satellite constellation precisely. Hence the system uses a sensor suite of several state of the art indoor positioning sensors and innovative fusion algorithms. Integrating element of the system is a software receiver using ultra-tightly coupling realized by vector tracking. The presented work was performed under the ESA funded contract DINGPOS, ESTEC Ctr. No. 20834.
A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS
Sensors, 2012
Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user's motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data.
IEEE Access, 2021
The global navigation satellite system (GNSS) is widely used in smartphone positioning, but its performance can be degraded in urban environments because of signal reflections or blockages. To address these GNSS outages, pedestrian dead reckoning (PDR) is commonly used due to its significant improvements in both the stability and continuity of positioning, which are dependent on three key factors: continuous absolute position, heading and step information. Signals of opportunity are commonly used in positioning, whereas the installation of Bluetooth low energy (BLE) sensors on lampposts can provide an opportunity for positioning and heading estimation in urban canyons. In this article, a system integrating the GNSS, PDR, and BLE techniques is implemented in smartphones to provide a real-time positioning solution for pedestrians, which includes a new position correction method based on BLE heading, a reliable heading estimation integrating BLE and inertial sensors, an unconstrained step detection method with high accuracy, and an extended Kalman filter (EKF) to integrate multiple sensors and techniques. In several field experiments, with improvements in availability and robustness, the heading accuracy of the proposed fusion approach could reach approximately 3 degrees; the positioning accuracy achieved between 2.7 m and 4.2 m, compared with a 30 m error from the GNSS alone. Simultaneously, this system could achieve a high positioning accuracy of 2.4 m with unconstrained smartphones in a mixed environment. The proposed system has been demonstrated to perform well in urban canyons.
Indoor Positioning using Sensor-fusion in Android Devices
This project examines the level of accuracy that can be achieved in precision positioning by using built-in sensors in an Android smartphone. The project is focused in estimating the position of the phone inside a building where the GPS signal is bad or unavailable. The approach is sensor-fusion: by using data from the device's different sensors, such as accelerometer, gyroscope and wireless adapter, the position is determined. The results show that the technique is promising for future handheld indoor navigation systems that can be used in malls, museums, large office buildings, hospitals, etc.
Many Ways Lead to the Goal—Possibilities of Autonomous and Infrastructure-Based Indoor Positioning
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There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel dataset is used to evaluate common approaches for autonomous and infrastructure-based positioning methods. The autonomous variant is the most cost-effective realization; however, realizations using the real test data demonstrate that the use of only autonomous solutions cannot always provide a robust solution. Therefore, correction through the use of infrastructure-based position estimation based on smartphone technology is discussed. This approach invokes the minimum cost when using existing infrastructure, whereby Pedestrian Dead Reckoning (PDR) forms the basis of the autonomous position estimation. Realizations with Particle Filters (PF) and a topological approach are presented and d...