GPS Localization Research Articles

Globally solving the fractional squared least squares model for GPS localization

Globally solving the fractional squared least squares model for GPS localization

Predicting Signal Reception Information from GNSS Satellites in Indoor Environments without Site Survey: Towards Opportunistic Indoor Positioning Based on GNSS Fingerprinting

While traditional GPS localization is impractical in certain indoor environments, GPS satellites offer a valuable source of indoor contextual information, particularly around window-side areas where signals are more accessible. This study introduces a new method that predicts GPS signal reception information in a target indoor environment without the need for actual data collection in the target environment. By predicting the strengths of GPS signals that are expected to be measured at various indoor positions, we can construct a GPS signal reception map of a target environment, which functions similarly to a Wi-Fi fingerprinting map surrounding window-side areas, offering a new dimension in indoor positioning systems. For instance, it aids in the correction of accumulated errors in pedestrian dead reckoning (PDR) systems. Our proposed method leverages easily accessible inputs, including satellite location data, indoor floorplan image of the target floor, and 3D mapping of the surrounding buildings, to predict signal reception at each position in the target environment. We developed a specialized neural network, named multi-scale branch fusion network (MSBF-Net), designed to process and integrate data of varying scales, such as the floorplan image of the target environment and 3D map of the surrounding area obtained from Google Earth, with a specific focus on understanding the line-of-sight (LOS) and multi-path effects caused by internal obstacles and surrounding buildings. This advanced capability enables the network to effectively interpret complex signal interactions within urban environments, enhancing its predictive accuracy for GPS signal reception. The effectiveness of our method was rigorously evaluated using real-world environments. In addition, we employed our method to implement opportunistic GPS fingerprint-based indoor positioning, where position estimates are provided when a strong signal is observed from at least one satellite. Surprisingly, the positioning method achieved a positioning error of only 2.8 meters when a smartphone is close to window-side areas even though the method does not rely on labeled training data collected in a target environment and signal infrastructures installed in the target environment.

Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach.

Accurate and responsive epidemiological simulations of epidemic outbreaks inform decision-making to mitigate the impact of pandemics. These simulations must be grounded in quantities derived from measurements, among which the parameters associated with contacts between individuals are notoriously difficult to estimate. Digital contact tracing data, such as those provided by Bluetooth beaconing or GPS colocating, can provide more precise measures of contact than traditional methods based on direct observation or self-reporting. Both measurement modalities have shortcomings and are prone to false positives or negatives, as unmeasured environmental influences bias the data. We aim to compare GPS colocated versus Bluetooth beacon-derived proximity contact data for their impacts on transmission models' results under community and types of diseases. We examined the contact patterns derived from 3 data sets collected in 2016, with participants comprising students and staff from the University of Saskatchewan in Canada. Each of these 3 data sets used both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data (Avicenna Research) app to collect sensor data about every 5 minutes over a month. We compared the structure of contact networks inferred from proximity contact data collected with the modalities of GPS colocating and Bluetooth beaconing. We assessed the impact of sensing modalities on the simulation results of transmission models informed by proximate contacts derived from sensing data. Specifically, we compared the incidence number, attack rate, and individual infection risks across simulation results of agent-based susceptible-exposed-infectious-removed transmission models of 4 different contagious diseases. We have demonstrated their differences with violin plots, 2-tailed t tests, and Kullback-Leibler divergence. Both network structure analyses show visually salient differences in proximity contact data collected between GPS colocating and Bluetooth beaconing, regardless of the underlying population. Significant differences were found for the estimated attack rate based on distance threshold, measurement modality, and simulated disease. This finding demonstrates that the sensor modality used to trace contact can have a significant impact on the expected propagation of a disease through a population. The violin plots of attack rate and Kullback-Leibler divergence of individual infection risks demonstrated discernible differences for different sensing modalities, regardless of the underlying population and diseases. The results of the t tests on attack rate between different sensing modalities were mostly significant (P<.001). We show that the contact networks generated from these 2 measurement modalities are different and generate significantly different attack rates across multiple data sets and pathogens. While both modalities offer higher-resolution portraits of contact behavior than is possible with most traditional contact measures, the differential impact of measurement modality on the simulation outcome cannot be ignored and must be addressed in studies only using a single measure of contact in the future.

MOBILE ATTENDANCE SYSTEM USING GPS AND FACIAL AUTHENTICATION

Student attendance in any educational institutions is a critical content of the administration of school and the major task of the course lecturer who maintain attendance. The conventional method of monitoring students' attendance in the classroom typically involves requiring students to physically record their attendance. Initially, this process was carried out manually using pen and paper. However, as the student population grows, this approach becomes increasingly burdensome, time-consuming, and susceptible to mistakes. To avoid these challenges, this paper proposes a Mobile attendance system using GPS locator and facial authentication algorithm to monitor students’ attendance using android mobile devices. The mobile attendance app is based on Client-Server Architecture where the server is hosted in the cloud and clients are with mobile devices with in-built GPS Locator. To secure the integrity of the attendance, the GPS locator helps in locating the precise geographical position of the students present, a mock detector is also integrated into the app to detect fake location of the students in the classroom meeting and the facial image used by the student at runtime support startup camera.

Измерение распределения дозы гамма-излучения с помощью дрона дискретным методом измерения

Several dose distribution maps have been created using a drone-mounted gamma radiation detector. Based on the results and the experiences gained in the experiments, the shortcomings of the system as well as the possibility for further development have also emerged. The primary objective of the development was to create a more compact, easily portable, and deployable system, but one which, in contrast with the previous ones, is more sensitive. Despite background radiation (typically 0.01 Sv/h in the testing areas), a difference of +0.009 Sv/h has reliably been detected in the experiments made so far. In this present development, this value was successfully reduced to +0.005 - +0.007 S/h. The improvement in sensitivity was achieved primarily by increasing the measurement time per point, which was realized using special flight control software. This sensitivity increases either allows higher scanning altitude (approximately +1-2m) or, in case of an identical flight device at an identical altitude, a larger survey area with one take-off. Naturally, scanning height or scanning speed can increase significantly if the activity of the searched source is high. In our experiments, we used a natural uranium mineral (Autunit) with activity far below that of artificially produced isotopes. In this series of experiments, we also covered the identification of several sources, which models the possibility of mapping active sources scattered around the site of a possible accident. The main advantage of the system developed and introduced by us over the survey procedures used in practice is that it is easy to mobilize, a large area can be surveyed at low cost without putting an operator at risk in the field. The purpose of the system is to detect the presence of the source and to localize it to such an extent that the localization can subsequently be easily specified by manual or other ground procedures. Since we do not strive for centimeter positioning accuracy, standard GPS localization is sufficient for measurements. During the measurements, the geographical coordinates are interpreted in accordance with the WGS’84 system. Additionally, the system displays the circles of latitude and longitude coordinates in the figures presented.

Deep Neural Network for Visual Localization of Autonomous Car in ITS Campus Environment

Intelligent Car (I-Car) ITS is an autonomous car prototype where one of the main localization methods is obtained through reading GPS data. However the accuracy of GPS readings is influenced by the availability of the information from GPS satellites, in which it often depends on the conditions of the place at that time, such as weather or atmospheric conditions, signal blockage, and density of a land. In this paper we propose the solution to overcome the unavailability of GPS localization information based on the omnidirectional camera visual data through environmental recognition around the ITS campus using Deep Neural Network. The process of recognition is to take GPS coordinate data to be used as an output reference point when the omnidirectional camera takes images of the surrounding environment. Visual localization trials were carried out in the ITS environment with a total of 200 GPS coordinates, where each GPS coordinate represents one class so that there are 200 classes for classification. Each coordinate/class has 96 training images. This condition is achieved for a vehicle speed of 20 km/h, with an image acquisition speed of 30 fps from the omnidirectional camera. By using AlexNet architecture, the result of visual localization accuracy is 49-54%. The test results were obtained by using a learning rate parameter of 0.00001, data augmentation, and the Drop Out technique to prevent overfitting and improve accuracy stability.

ViF-GTAD: A new automotive dataset with ground truth for ADAS/AD development, testing, and validation

A new dataset for automated driving, which is the subject matter of this paper, identifies and addresses a gap in existing similar perception datasets. While most state-of-the-art perception datasets primarily focus on the provision of various onboard sensor measurements along with the semantic information under various driving conditions, the provided information is often insufficient since the object list and position data provided include unknown and time-varying errors. The current paper and the associated dataset describe the first publicly available perception measurement data that include not only the onboard sensor information from the camera, Lidar, and radar with semantically classified objects but also the high-precision ground-truth position measurements enabled by the accurate RTK-assisted GPS localization systems available on both the ego vehicle and the dynamic target objects. This paper provides insight on the capturing of the data, explicitly explaining the metadata structure and the content, as well as the potential application examples where it has been, and can potentially be, applied and implemented in relation to automated driving and environmental perception systems development, testing, and validation.

Seasonal Movement Patterns of Urban Domestic Cats Living on the Edge in an African City.

Domestic cats (Felis catus) are amongst the most destructive invasive vertebrates globally, depredating billions of native animals annually. The size and seasonal variation of their geographical "footprint" is key to understanding their effects on wildlife, particularly if they live near conservation areas. Here we report the first GPS-tracking studies of free-roaming owned cats in the city of Cape Town, South Africa. A total of 23 cats was tracked (14 cats in summer, 9 in winter) using miniature (22 g) GPS locators in 2010-2011. In summer, all cats living on the urban-edge (UE: n = 7) made extensive use of protected areas, while only one of seven urban (U) cats (>150 m from the edge) did so. In winter two of four UE and two of five U cats entered protected areas. Home ranges (95% kernel density estimates) were significantly larger in summer (3.00 ± 1.23 ha) than winter (0.87 ± 0.25 ha) and cats ventured further from their homes in summer (maximum 849 m) than in winter (max 298 m). The predation risk posed by caracal (Caracal caracal) may limit the time cats spend in protected areas, but our results suggest that cat buffers around conservation areas should be at least ~600 m wide to reduce impacts to native fauna.

Design of a Lightweight Autonomous Robotic Phenotyping System for Turfgrass

High-throughput field inspection and phenotyping are among the most promising applications for agricultural robotics. We present an autonomous, lightweight robotic system for turfgrass phenotyping applications. The robot is built on two integrated bikebot platform with high-clearance and low soil compaction. To improve the motion control accuracy, an invariant extended Kalman filter is built with fusion of GPS and inertial measurement unit for outdoor localization and navigation. Multiple cameras are used for multi-modal imaging collections. Preliminary experiment results show that the localization accuracy is at the centimeter level and the path-following error is less than 0.5 m most time for various reference trajectories in the feld. The robot design and autonomous navigation demonstrates the feasibility for turfgrass inspection application.

A Case Study of Human Milk Banking with Focus on the Role of IoT Sensor Technology

Human milk is the biological norm for newborn nutrition, with breast milk from the mother being recognized as the best source of nutrition for infant health. When the mother’s milk is unavailable, donor human milk is the best alternative for infants with low birthweights. Growing recognition of the benefits of donor human milk has led to increasing global interest in monitoring and controlling human milk’s quality to fulfil the need for donor human milk. In response to this need, the REAMIT project proposed to adapt and apply existing innovative technology to continuously monitor and record human milk quality and signal potential milk quality issues. IoT sensors and big data technology have been used to monitor conditions that may increase spoilage (such as temperature and humidity) in the transportation stage. The sensors were installed in the insulated bags used to transport the milk from the donor’s home or hospital to the human milk bank and vice versa. The temperature and humidity were collected every 30 min, whilst the GPS locator sent data every 2 min. The data are collected in the cloud using GPRS/CAT-M1 technology. An algorithm was designed to send alerts when the milk temperature is above the prespecified threshold specified by the organisation, i.e., above −20 °C. The experience showed evidence that IoT sensors can efficiently be used to monitor and maintain quality in supply chains of high-quality human milk. This rare product needs a high level of quality control, which is possible with the support of smart technologies. The IoT technology used can help the human milk supply chain in five different aspects, namely by reducing waste, assuring quality, improving availability, reducing cost and improving sustainability. This system could be extended to various supply chains of rare and precious commodities, including further medical supplies such as human blood and organs, to completely avoid waste and ensure total quality in supply chains.

Gamma Radiation Dose Measurement Using an Energy-Selective Method with the Help of a Drone

Several dose distribution maps were obtained using a gamma radiation detector mounted to a drone. Based on the results and experience of the experiments, the shortcomings of the system and the possibilities for further development were identified. The primary goal of the development was to create a more compact, easy-to-carry, and easy-to-install system with increased sensitivity, which was achieved by several different methods and their combinations. During the discrete measurement procedure, the aim was to decrease the detection threshold, +0.005 to +0.007 μS/h measured above the background radiation. The increase in sensitivity was based on the characteristic energy spectrum of radiative materials. We took advantage of the fact that the radiating samples do not evenly increase the amount of gamma radiation over the entire energy spectrum. During the processing of the measurement data, we performed a comparison with the background radiation in the vicinity of the energy peaks characteristic of the sample and its decay products. This provides a better signal-to-noise ratio, thus enabling a more sensitive detection procedure. An important feature of our method is that in the traditional intensity curve displayed as a function of flight time only noise is visible, therefore one cannot directly conclude the presence of the sample. However, our method is clearly able to identify the location of the searched source at a height of 8 m with a continuous flight speed of 2 m/s using a 500 μS/h activity sample (as measured at a distance of 0.1 m from the sample). The increase in sensitivity allows either a higher scanning height (approximately +1 to 2 m) or, in the case of the same aircraft at the same altitude, a larger area from one take-off. Of course, the scan height or scan speed can increase significantly if the activity of the source being sought is high. In our experiments, we used a natural uranium mineral (Autunite) with activity far below that of artificially produced isotopes. In the series of our experiments, we also covered the detection of several sources, which modeled the possibility of mapping scattered active sources. The main advantages of the system developed and presented by us over the survey procedures used in practice is that a large area can be mobilized easily, without the risk of a human operator in the field, and the survey of a large area can be carried out at a low cost. The purpose of the system is to detect the presence of the source and to localize it to such an extent that the localization can then be easily refined by manual or other ground procedures. As we do not aim for positioning accuracy by centimeter, standard GPS localization is sufficient for the measurements. During the measurements, the geographical coordinates are interpreted in the GWS'84 system. The coordinates of the latitude and longitude circles are also shown in this system in the figures presented.

Mapping Agricultural Soil in Greenhouse Using an Autonomous Low-Cost Robot and Precise Monitoring

Our work is focused on developing an autonomous robot to monitor greenhouses and large fields. This system is designed to operate autonomously to extract useful information from the plants based on precise GPS localization. The proposed robot is based on an RGB camera for plant detection and a multispectral camera for extracting the different special bands for processing, and an embedded architecture integrating a Nvidia Jetson Nano, which allows us to perform the required processing. Our system uses a multi-sensor fusion to manage two parts of the algorithm. Therefore, the proposed algorithm was partitioned on the CPU-GPU embedded architecture. This allows us to process each image in 1.94 s in a sequential implementation on the embedded architecture. The approach followed in our implementation is based on a Hardware/Software Co-Design study to propose an optimal implementation. The experiments were conducted on a tomato farm, and the system showed that we can process different images in real time. The parallel implementation allows to process each image in 36 ms allowing us to satisfy the real-time constraints based on 5 images/s. On a laptop, we have a total processing time of 604 ms for the sequential implementation and 9 ms for the parallel processing. In this context, we obtained an acceleration factor of 66 for the laptop and 54 for the embedded architecture. The energy consumption evaluation showed that the prototyped system consumes a power between 4 W and 8 W. For this raison, in our case, we opted a low-cost embedded architecture based on Nvidia Jetson Nano.

Deep semantic segmentation for the quantification of grape foliar diseases in the vineyard.

Plant disease evaluation is crucial to pathogen management and plant breeding. Human field scouting has been widely used to monitor disease progress and provide qualitative and quantitative evaluation, which is costly, laborious, subjective, and often imprecise. To improve disease evaluation accuracy, throughput, and objectiveness, an image-based approach with a deep learning-based analysis pipeline was developed to calculate infection severity of grape foliar diseases. The image-based approach used a ground imaging system for field data acquisition, consisting of a custom stereo camera with strobe light for consistent illumination and real time kinematic (RTK) GPS for accurate localization. The deep learning-based pipeline used the hierarchical multiscale attention semantic segmentation (HMASS) model for disease infection segmentation, color filtering for grapevine canopy segmentation, and depth and location information for effective region masking. The resultant infection, canopy, and effective region masks were used to calculate the severity rate of disease infections in an image sequence collected in a given unit (e.g., grapevine panel). Fungicide trials for grape downy mildew (DM) and powdery mildew (PM) were used as case studies to evaluate the developed approach and pipeline. Experimental results showed that the HMASS model achieved acceptable to good segmentation accuracy of DM (mIoU > 0.84) and PM (mIoU > 0.74) infections in testing images, demonstrating the model capability for symptomatic disease segmentation. With the consistent image quality and multimodal metadata provided by the imaging system, the color filter and overlapping region removal could accurately and reliably segment grapevine canopies and identify repeatedly imaged regions between consecutive image frames, leading to critical information for infection severity calculation. Image-derived severity rates were highly correlated (r > 0.95) with human-assessed values, and had comparable statistical power in differentiating fungicide treatment efficacy in both case studies. Therefore, the developed approach and pipeline can be used as an effective and efficient tool to quantify the severity of foliar disease infections, enabling objective, high-throughput disease evaluation for fungicide trial evaluation, genetic mapping, and breeding programs.

Adaptive Kalman Filter for Free GPS Localization with Fuzzy Intersection Method

Adaptive Kalman Filter for Free GPS Localization with Fuzzy Intersection Method

The Contribution of Environmental and Cultural Aspects of Pastoralism in the Provision of Ecosystem Services: The Case of the Silesian Beskid Mts (Southern Poland)

Grazing has proven to be a successful management tool in terms of the maintenance and restoration of biodiversity. Sheep and pastoralism can provide a various set of ecosystem services, i.e., benefits for humans from the environment. The Silesian Beskids, the westernmost part of the Carpathian Mountains, is an area with a long tradition of pastoralism. Since the 13th century, sheep have grazed clearings located in forests and in large areas of grasslands in mountains. In this study, we tried to identify all possible aspects of provisional, regulating and cultural services provided by sheep and the pasturage tradition bearing in mind the specificity of this region. The biodiversity maintenance and habitat creation due to grazing and the cultural values of pastoralism was illustrated using a literature review of data concerning cultural heritage, tourism and education and using our own phytosociological field studies performed in nine vegetation types within grazing sites. In order to examine the impact of grazing, an observation of sheep’s daily grazing activity during the vegetation season using tractive GPS locators was conducted. The results showed that grazing has a positive impact on the maintenance of vegetation including EU-protected habitats. In addition, the pastoral activity introduced to the Beskids by the Vlachs contributed to the development of a specific highlander culture that continues to this day. Not all potential ecosystem services associated with the use of sheep are realized in the study area. Amongst the provisional services, sheep bones are not used while dairy is the most popular followed by meat. In turn, the cultural heritage of pastoralism is one of the most important drivers for the touristic attraction of Silesian Beskid.

An Indoor Smart Parking Algorithm Based on Fingerprinting

In the last few years, researchers from many research fields are investigating the problem affecting all the drivers in big and populated cities: the parking problem. In outdoor environments, the problem can be solved by relying on vehicular ad hoc networks, which guarantee communication among vehicles populating the network. When it comes to indoor settings, the problem gets harder, since drivers cannot count on classic GPS localization. In this work, a smart parking solution for a specific indoor setting is provided, exploiting the fingerprint approach for indoor localization. The considered scenario is a multi-level car park inside an airport building. The algorithm provides a vehicle allocation inside the car park in quadratic time over the number of parking slots, by also considering the driver’s preferences on the terminal to be reached.

WeCo-SLAM: Wearable Cooperative SLAM System for Real-Time Indoor Localization Under Challenging Conditions

Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. In this paper, a real-time tracking system for continuously locating emergency response agents in challenging conditions is presented. A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintaining the localization. The system achieved Absolute Trajectory Error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different level of details.

3D Distance Filter for the Autonomous Navigation of UAVs in Agricultural Scenarios

In precision agriculture, remote sensing is an essential phase in assessing crop status and variability when considering both the spatial and the temporal dimensions. To this aim, the use of unmanned aerial vehicles (UAVs) is growing in popularity, allowing for the autonomous performance of a variety of in-field tasks which are not limited to scouting or monitoring. To enable autonomous navigation, however, a crucial capability lies in accurately locating the vehicle within the surrounding environment. This task becomes challenging in agricultural scenarios where the crops and/or the adopted trellis systems can negatively affect GPS signal reception and localisation reliability. A viable solution to this problem can be the exploitation of high-accuracy 3D maps, which provide important data regarding crop morphology, as an additional input of the UAVs’ localisation system. However, the management of such big data may be difficult in real-time applications. In this paper, an innovative 3D sensor fusion approach is proposed, which combines the data provided by onboard proprioceptive (i.e., GPS and IMU) and exteroceptive (i.e., ultrasound) sensors with the information provided by a georeferenced 3D low-complexity map. In particular, the parallel-cuts ellipsoid method is used to merge the data from the distance sensors and the 3D map. Then, the improved estimation of the UAV location is fused with the data provided by the GPS and IMU sensors, using a Kalman-based filtering scheme. The simulation results prove the efficacy of the proposed navigation approach when applied to a quadrotor that autonomously navigates between vine rows.

Building on Existing Technology to Enhance Remote Assessments of Brain Health

Tablets, smartphones, linked devices have features such as high-fidelity microphones, accelerometers, GPS locators, and gyroscopes can be used to capture brain health-related data. Collection of data remotely is especially important given the vulnerability of older adults to COVID and the need to protect from such exposure. As part of an American Heart Association/Gates Venture Strategically Funded Network, a number of remote assessments are being deployed to capture information related to brain health in a subset of the Bogalusa Heart Study cohort (mean age 51.4, SD 5.3). The Linus Health Platform includes applications that measure cognitive abilities, and collect digital voice features and phone sensor data that can be derived into surrogate measures of cognitive function and mood. A readily available suite of games (Lumosity) is also being used to assess cognitive health. These devices and applications offer a largely unexplored opportunity for acquiring and assessing data related to brain health.

Simulation of GPS localisation based on ray tracing

Abstract. In recent years, many simulation tools emerged to model the communication of connected vehicles. Thereby, the focus was put on channel modelling, applications or protocols while the localisation due to satellite navigation systems was treated as perfect. The effect of inaccurate positioning, however, was neglected so far. This paper presents an approach to extend an existing simulation framework for radio networks to estimate the localisation accuracy by navigation systems like GPS, GLONASS or Galileo. Therefore the error due multipath components is calculated by ray optical path loss predictions (ray tracing) considering 3D building data together with a well-established model for the ionospheric error.