scholarly journals Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6292
Author(s):  
Vincenzo Di Pietra ◽  
Paolo Dabove ◽  
Marco Piras
Keyword(s):  
Data Fusion ◽  
Low Cost ◽  
Ground Truth ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Ultra Wideband ◽  
Portable Devices ◽  
Positioning System ◽  
Fusion Algorithm ◽  
Satellite Systems

The growth of location-based services (LBS) has increased rapidly in last years, mainly due to the possibility to exploit low-cost sensors installed in portable devices, such as smartphones and tablets. This work aims to show a low-cost multi-sensor platform developed by the authors in which an ultra-wideband (UWB) indoor positioning system is added to a classical global navigation satellite systems–inertial navigation system (GNSS-INS) integration, in order to acquire different synchronized data for further data fusion analysis in order to exploit seamless positioning. The data fusion is based on an extended Kalman filter (EKF) and on a geo-fencing approach which allows the navigation solution to be provided continuously. In particular, the proposed algorithm aims to solve a navigation task of a pedestrian user moving from an outdoor space to an indoor environment. The methodology and the system setup is presented with more details in the paper. The data acquired and the real-time positioning estimation are analysed in depth and compared with ground truth measurements. Particular attention is given to the UWB positioning system and its behaviour with respect to the environment. The proposed data fusion algorithm provides an overall horizontal and 3D accuracy of 35 cm and 45 cm, respectively, obtained considering 5 different measurement campaigns.

scholarly journals A low-cost IMU/GPS position accuracy experimental study using extended kalman filter data fusion in real environments

2021 ◽  
Vol 297 ◽  
pp. 01040
Author(s):  
Aziz El Fatimi ◽  
Adnane Addaim ◽  
Zouhair Guennoun
Keyword(s):  
Experimental Study ◽  
Data Fusion ◽  
Low Cost ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Circular Path ◽  
Positioning System ◽  
Fusion Algorithm ◽  
Position Accuracy ◽  
The Stability

In a three-dimensional environment, the navigation of a vehicle in airspace, terrestrial space, or maritime space presents complex aspects concerning the determination of its position, its orientation, and the stability of the processing of the asynchronous data coming from the various sensors during navigation. In this context, this paper presents an experimental analysis of the position accuracy estimated by a low-cost inertial measurement unit coupled, by the extended Kalman data fusion algorithm, with a system of absolute measurements of a positioning system received from a GPS which designates the global positioning system. The different scenarios of the experimental study carried out during this work concerned three tests in a real environment, such as the navigation in a course inside the city of Rabat/Morocco with a moderate speed, a section on the highway at a speed of 120 Km/h and a circular path around a roundabout. The experimental results proved that the low-cost sensors studied are a good candidate for civil navigation applications.

scholarly journals UAVS ENHANCED NAVIGATION IN OUTDOOR GNSS DENIED ENVIRONMENT USING UWB AND MONOCULAR CAMERA SYSTEMS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 665-672 ◽  
Author(s):  
S. Zahran ◽  
A. Masiero ◽  
M. M. Mostafa ◽  
A. M. Moussa ◽  
A. Vettore ◽  
...  
Keyword(s):  
Navigation System ◽  
Low Cost ◽  
Global Navigation Satellite Systems ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Satellite Systems ◽  
Monocular Camera ◽  
Camera Systems ◽  
Global Navigation Satellite ◽  
The Cost

<p><strong>Abstract.</strong> The demand for small Unmanned Aerial Vehicles (UAVs) is massively increasing these days, due to the wide variety of applications utilizing such vehicles to perform tasks that may be dangerous or just to save time, effort, or cost. Small UAVs navigation system mainly depends on the integration between Global Navigation Satellite Systems (GNSS) and Inertial Measurement Unit (INS) to estimate the Positions, Velocities, and Attitudes (PVT) of the vehicle. Without GNSS such UAVs cannot navigate for long periods of time depending on INS alone, as the low-cost INS typically exhibits massive accumulation of errors during GNSS absence. Given the importance of ensuring full operability of the UAVs even during GNSS signals unavailability, other sensors must be used to bound the INS errors and enhance the navigation system performance. This paper proposes an enhanced UAV navigation system based on integration between monocular camera, Ultra-Wideband (UWB) system, and INS. In addition to using variable EKF weighting scheme. The paper also investigates this integration in the case of low density of UWB anchors, to reduce the cost required for such UWB system infrastructure. A GoPro Camera and UWB rover were attached to the belly of a quadcopter, an on the shelf commercial drone (3DR Solo), during the experimental flight. The velocity of the vehicle is estimated with Optical Flow (OF) from camera successive images, while the range measurements between the UWB rover and the stationary UWB anchors, which were distributed on the field, were used to estimate UAV position.</p>

scholarly journals Characterization of an Ultrasonic Local Positioning System for 3D Measurements

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2794 ◽  
Author(s):  
Khaoula Mannay ◽  
Jesús Ureña ◽  
Álvaro Hernández ◽  
Mohsen Machhout ◽  
Taoufik Aguili
Keyword(s):  
Low Cost ◽  
Likelihood Estimation ◽  
Indoor Positioning ◽  
Global Navigation Satellite Systems ◽  
Positioning System ◽  
Fusion Algorithm ◽  
Coverage Area ◽  
Positioning Systems ◽  
Indoor Location ◽  
Local Positioning System

Indoor location and positioning systems (ILPS) are used to locate and track people, as well as mobile and/or connected targets, such as robots or smartphones, not only inside buildings with a lack of global navigation satellite systems (GNSS) signals but also in constrained outdoor situations with reduced coverage. Indoor positioning applications and their interest are growing in certain environments, such as commercial centers, airports, hospitals or factories. Several sensory technologies have already been applied to indoor positioning systems, where ultrasounds are a common solution due to its low cost and simplicity. This work proposes a 3D ultrasonic local positioning system (ULPS), based on a set of three asynchronous ultrasonic beacon units, capable of transmitting coded signals independently, and on a 3D mobile receiver prototype. The proposal is based on the aforementioned beacon unit, which consists of five ultrasonic transmitters oriented towards the same coverage area and has already been proven in 2D positioning by applying hyperbolic trilateration. Since there are three beacon units available, the final position is obtained by merging the partial results from each unit, implementing a minimum likelihood estimation (MLE) fusion algorithm. The approach has been characterized, and experimentally verified, trying to maximize the coverage zone, at least for typical sizes in most common public rooms and halls. The proposal has achieved a positioning accuracy below decimeters for 90% of the cases in the zone where the three ultrasonic beacon units are available, whereas these accuracies can degrade above decimeters according to whether the coverage from one or more beacon units is missing. The experimental workspace covers a large volume, where tests have been carried out at points placed in two different horizontal planes.

scholarly journals Estimation of GPS L2 Signal Observables Using Multilayer Perceptron Artificial Neural Networks for Positional Accuracy Improvement

2020 ◽  
Vol 24 (1) ◽  
pp. 97-103
Author(s):  
Cassio Vinícius Carletti Negri ◽  
Paulo Cesar Lima Segantine
Keyword(s):  
Multilayer Perceptron ◽  
Low Cost ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Dual Frequency ◽  
Positional Accuracy ◽  
Geospatial Information ◽  
Satellite Systems ◽  
Artificial Neural

In recent decades, due to the increasing mobility of people and goods, the rapid growth of users of mobile devices with location-based services has increased the need for geospatial information. In this context, positioning using data collected by the Global Navigation Satellite Systems (multi-GNSS) has gained more importance in the field of geomatics. The quality of the solutions is related, among other factors, to the receiver’s type used in the work. To improve the positioning with low-cost devices and to avoid additional user expenses, this work aims to propose the implementation of an Artificial Neural Network (ANN) to estimate the GPS L2 carrier observables. For this, a network model was selected through the cross-validation (CV) technique, the observations were estimated, and the accuracy of the solutions was analyzed. The CV technique demonstrated that a Multilayer Perceptron with four intermediate layers and one with one intermediate layer are the most appropriate configurations for this problem. The dual-frequency RINEX processing (with artificial data) revealed significant improvements. For some tests, it was possible to comply with the rural property georeferencing regulations of the Brazilian National Institute of Colonization and Agrarian Reform (INCRA). The results indicate, therefore, that the methodological proposal of the present investigation is very promising for approximating the quality of positioning reachable using a dual-frequency receiver.

scholarly journals A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7230
Author(s):  
Santosh Subedi ◽  
Jae-Young Pyun
Keyword(s):  
Indoor Localization ◽  
Indoor Positioning ◽  
Performance Comparison ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Smart Devices ◽  
Positioning System ◽  
Localization Accuracy ◽  
Satellite Systems ◽  
Indoor Positioning System

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

Fusion of Ship Perceptual Information for Electronic Navigational Chart and Radar Images based on Deep Learning

2019 ◽  
Vol 73 (1) ◽  
pp. 192-211
Author(s):  
Muzhuang Guo ◽  
Chen Guo ◽  
Chuang Zhang ◽  
Daheng Zhang ◽  
Zongjiang Gao
Keyword(s):  
Deep Learning ◽  
Data Fusion ◽  
Reference Points ◽  
Machine Learning Algorithms ◽  
Global Navigation Satellite Systems ◽  
Fusion Algorithm ◽  
Satellite Systems ◽  
Radar Images ◽  
Edge Matching ◽  
Marine Radar

Superimposing Electronic Navigational Chart (ENC) data on marine radar images can enrich information for navigation. However, direct image superposition is affected by the performance of various instruments such as Global Navigation Satellite Systems (GNSS) and compasses and may undermine the effectiveness of the resulting information. We propose a data fusion algorithm based on deep learning to extract robust features from radar images. By deep learning in this context we mean employing a class of machine learning algorithms, including artificial neural networks, that use multiple layers to progressively extract higher level features from raw input. We first exploit the ability of deep learning to perform target detection for the identification of marine radar targets. Then, image processing is performed on the identified targets to determine reference points for consistent data fusion of ENC and marine radar information. Finally, a more intelligent fusion algorithm is built to merge the marine radar and electronic chart data according to the determined reference points. The proposed fusion is verified through simulations using ENC data and marine radar images from real ships in narrow waters over a continuous period. The results suggest a suitable performance for edge matching of the shoreline and real-time applicability. The fused image can provide comprehensive information to support navigation, thus enhancing important aspects such as safety.

scholarly journals Robust TOA-Based UAS Navigation under Model Mismatch in GNSS-Denied Harsh Environments

2020 ◽  
Vol 12 (18) ◽  
pp. 2928
Author(s):  
Jan Mortier ◽  
Gaël Pagès ◽  
Jordi Vilà-Valls
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Systems ◽  
Real Life ◽  
Transportation Systems ◽  
Urban Environments ◽  
Global Navigation Satellite Systems ◽  
Ultra Wideband ◽  
Time Of Arrival ◽  
Navigation Systems ◽  
Satellite Systems

Global Navigation Satellite Systems (GNSS) is the technology of choice for outdoor positioning purposes but has many limitations when used in safety-critical applications such Intelligent Transportation Systems (ITS) and Unmanned Autonomous Systems (UAS). Namely, its performance clearly degrades in harsh propagation conditions and is not reliable due to possible attacks or interference. Moreover, GNSS signals may not be available in the so-called GNSS-denied environments, such as deep urban canyons or indoors, and standard GNSS architectures do not provide the precision needed in ITS. Among the different alternatives, cellular signals (LTE/5G) may provide coverage in constrained urban environments and Ultra-Wideband (UWB) ranging is a promising solution to achieve high positioning accuracy. The key points impacting any time-of-arrival (TOA)-based navigation system are (i) the transmitters’ geometry, (ii) a perfectly known transmitters’ position, and (iii) the environment. In this contribution, we analyze the performance loss of alternative TOA-based navigation systems in real-life applications where we may have both transmitters’ position mismatch, harsh propagation environments, and GNSS-denied conditions. In addition, we propose new robust filtering methods able to cope with both effects up to a certain extent. Illustrative results in realistic scenarios are provided to support the discussion and show the performance improvement brought by the new methodologies with respect to the state-of-the-art.

scholarly journals Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

Constructing Floor Plan through Smoke Using Ultra Wideband Radar

2021 ◽  
Vol 5 (4) ◽  
pp. 1-29
Author(s):  
Weiyan Chen ◽  
Fusang Zhang ◽  
Tao Gu ◽  
Kexing Zhou ◽  
Zixuan Huo ◽  
...  
Keyword(s):  
Spatial Information ◽  
Low Cost ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Ultra Wideband ◽  
Indoor Environments ◽  
Floor Plan ◽  
Construction Methods ◽  
Median Error ◽  
Indoor Scenarios

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.

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