Assisted GPS Research Articles

Combining Image Processing Techniques and Mobile Sensor Information for Marker-less Augmented Reality Based Reconstruction

Marker-less Augmented Reality(AR) based recon- struction using mobile devices, is a near impossible task. When considering vision based tracking approaches, it is due to the lack of processing power in mobile devices and when considering mobile sensor based tracking approaches, it is due to the lack of accuracy in mobile Global Positioning System(GPS). In order to address this problem this research presents a novel approach which combines image processing techniques and mobile sensor information which can be used to perform precise position localization in order to perform augmented reality based reconstruction using mobile devices. The core of this proposed methodology is tightly bound with the image processing technique which is used to identify the object scale in a given image, which is taken from the user’s mobile device. Use of mobile sensor information was to classify the most optimal locations for a given particular user location. This proposed methodology has been evaluated against the results obtained using 10cm accurate Real-Time Kinematic(RTK) device and against the results obtained using only the Assisted Global Positioning System(A-GPS) chips in mobile devices. Though this proposed methodology require more processing time than A-GPS chips, the accuracy level of this proposed methodology outperforms that of A-GPS chips and the results of the experiments carried out further convince that this proposed methodology facilitates improving the accuracy of position local- ization for augmented reality based reconstruction using mobile devices under certain limitations.

Multiresolution spatial generalized linear mixed model for integrating multi-fidelity spatial count data without common identifiers between data sources

A motivating example for this paper is a large human location information system that collects two types of information on mobile device locations: 1) large amounts of low-accuracy cell tower triangulation (CTT) calculated location data and 2) small amounts of high-accuracy assisted global positioning system (AGPS) pinpointed location data. Integrating the CTT and AGPS data and extracting more complete and accurate location information is important to achieve better estimation of the true spatial density. However, the problem is challenging because there is no direct link between the CTT and APGS data. In this paper, we propose a multiresolution spatial generalized linear mixed model to integrate low-accuracy and high-accuracy spatial count data given no direct link between two data sources. The relationship between the high-accuracy data and low-accuracy data is estimated at a low-resolution level, where the relationship between the two types can be better captured, and then the estimated relationship is propagated to the high-resolution level. Using the high-accuracy data, the location information of the low-accuracy data is flexibly adjusted via spatial random effects that are modeled using a Gaussian process. The proposed method is validated using simulated and real data examples.

Assisted cold start method for GPS receiver with artificial neural network-based satellite orbit prediction

Traditional GPS receivers usually take a long time to obtain their first position solution in the cold start mode. This paper presents a method for assisting the cold start of GPS receivers by autonomous satellite orbit prediction. In this method, the time-consuming broadcast ephemeris collection process in a traditional cold start is replaced by orbit prediction to obtain the satellite position information required for positioning. Thus, the time to first fix of the receiver can be reduced significantly. In addition, since the traditional orbit prediction algorithms based solely on physics-based models have limited accuracy, a hybrid model composed of dynamic models and artificial neural network models for orbit prediction is proposed to improve the accuracy of the orbit prediction. Taking GPS PRN1 as an example, the simulation results show that, in 75% of cases, the satellite position error after 7 days’ prediction with the dynamic models is less than 162.84 m, but this value can be reduced to 46.06 m with the proposed hybrid model; the improvement rate achieved is approximately 71.71%. Some positioning experiments with simulated satellite signals were conducted to assess the first-fix accuracy of a GPS receiver with the proposed satellite orbit prediction function. The results of this study offer insights for the design of advanced assisted GPS receivers.

Driverless Car for Next Generation Commuters - Key Factors and Future Issues

In today’s world, as the technology is increasing day by day, road accidents are becoming more and more prevalent. Driver error is the most common cause of traffic accidents, and with cell phones, in-car entertainment systems, more traffic and more complicated road systems, it isn't likely to go away. To reduce the human error, there arises a possibility if with use of technology this error can be reduced and driving can be safer. Driverless cars are one of the major topics where transportation and innovation are considered to be perfectly combined for the safety on roads. Every automotive player is trying its best to make it practically possible in whichever it could. Companies developing and/or testing driverless cars include Audi, BMW, Ford, Google, General Motors, Tesla, Volkswagen and Volvo. One of the major ongoing projects is “Waymo” by Google. In this project we designed a prototype of driverless car with basic features using Arduino uno. We have used gmaps to create a map where we used to give the source and destination to get the route then we download the route in Keyhole Markup Language (KML) file format then we go to convertcsv.com site to extract latitude and longitude from the KML file that we just downloaded from the gmaps. We copy the latitude and longitude to our Arduino uno code where we keep the latitudes and longitudes in form of an array. Then we have built an app which uses the mobile Assisted Global Positioning System (AGPS) sensor to sense the current real time position in terms of latitudes and longitudes and send them to things peak cloud and simultaneously displaying them on a real time map. Arduino uno then receives the latest data sent from the mobile to the cloud and to do that Arduino uno uses a Wi-Fi module (esp 8266) to connect to the internet. Then the thingspeak cloud send the data receive from the mobile to the Arduino uno and then using that data we calculate the degree and distance required to move the car according to the latitudes and longitudes stored in the array which we got from the gmap route.

A Bayesian Nonparametric Mixture Measurement Error Model With Application to Spatial Density Estimation Using Mobile Positioning Data With Multi-Accuracy and Multi-Coverage

The development of mobile network technologies has made it possible to collect location data of mobile devices through various positioning technologies. The location data can be used to estimate the spatial density of mobile devices, which in turn can be used by mobile service providers to plan for network capacity improvements. The two most prevalent positioning technologies are the assisted global positioning system (AGPS) and cell tower triangulation (CTT) methods. AGPS data provide more accurate location information than CTT data but can cover only a fraction of mobile devices, while CTT data can cover all mobile devices. Motivated by this problem, we propose a Bayesian nonparametric mixture measurement error model to estimate the spatial density function by integrating both noise-free data (i.e., AGPS data) and data contaminated with measurement errors (i.e., CTT data). The proposed model estimates the true latent locations from contaminated data, and the estimated latent locations, combined with noise-free data, are used to infer the model parameters. We model the true density function using a Dirichlet process (DP) mixture model with a bivariate beta distribution for the mixture kernel and a DP prior for the mixing distribution. The use of bivariate beta distributions for the mixture kernel allows the density function to have various shapes with a bounded support. Moreover, the use of a DP prior for the mixing distribution allows the number of mixture components to be determined automatically without being specified in advance. Therefore, the proposed model is very flexible. We demonstrate the effective performance of the proposed model via simulated and real-data examples.

Internet of Things for a Smart Transportation System

Today, school buses transport millions of students to and from schools. Therefore, safety of school students is still a hot topic and the most imperative issue. The evolution of wireless location-based services has created consumer requests for availability of global positioning systems (GPS) in urban and indoor environments. Nowadays, there is a requirement to deliver a system for the school bus, that monitors its location and speed. This work investigates two different ways to track the vehicle through ordinary GPS, Assisted-GPS (AGPS). A-GPS improves startup performance (time-to-first-fix (TTFF)), of a GPS satellite-based positioning system in the downtown of big cities depending on 4G/LTE cellular networks. The proposed system is basically built using Raspberry Pi (3) with 4G model shield and important telematics technologies like Representational state transfer (RESTful API) which is the vital key for the Internet of Things (IoT) field.

Performance analysis of GNSS units in manned helicopter operations

Global Navigation Satellite System (GNSS) receivers make use of the United States' Global Positioning System (GPS), Russia's Global Navigation Satellite System (GLONASS), Europe's Galileo, China's Beidou, and Japan's Quasi-Zenith Satellite System constellations to determine the geolocation of the receiver based on the location of satellites and measurements of the distance and travel time of the GNSS signals. GNSS signals are dependent on line of sight to satellites with reflections or obstructions resulting in errors in the estimated position [1]. GNSS systems, while having their origin in military applications [2], have been adopted for many civilian uses from recreational navigation [3] to automated vehicle location systems [4]. While the GPS Standard Positioning Service without any correction sources establishes a 30 m range error limit for civilian use, GPS receivers typically perform better than this standard with errors primarily from atmospheric interference [5]. This is further improved in mobile devices by assisted GPS, WiFi, and cellular positioning when available [6].

Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai

Traditionally, departments of transportation (DOTs) have dispatched probe vehicles with dedicated vehicles and drivers for monitoring traffic conditions. Emerging assisted GPS (AGPS) and accelerometer-equipped smartphones offer new sources of raw data that arise from voluntarily-traveling smartphone users provided that their modes of transportation can correctly be identified. By introducing additional raster map layers that indicate the availability of each mode, it is possible to enhance the accuracy of mode detection results. Even in its simplest form, an artificial neural network (ANN) excels at pattern recognition with a relatively short processing timeframe once it is properly trained, which is suitable for real-time mode identification purposes. Dubai is one of the major cities in the Middle East and offers unique environments, such as a high density of extremely high-rise buildings that may introduce multi-path errors with GPS signals. This paper develops real-time mode identification ANNs enhanced with proposed mode availability geographic information system (GIS) layers, firstly for a universal mode detection and, secondly for an auto mode detection for the particular intelligent transportation system (ITS) application of traffic monitoring, and compares the results with existing approaches. It is found that ANN-based real-time mode identification, enhanced by mode availability GIS layers, significantly outperforms the existing methods.

Mobile navigation services with augmented reality

In this study, we combine augmented reality (AR) with the technique of assisted global positioning system to construct a guiding system of AR and design the guiding graphs metaphorically, so that the system interface operation is used more intuitively. We further investigate the availability of the system, and present an empirical study to statistically show that the guiding system of AR significantly outperforms that of plane map in terms of the finishing time of mission and correctness. Finally, according to the results of questionnaire of the system availability, we induct six essential factors influencing the guiding system availability of AR, namely guiding service usability factor, user esthetics of design factor, guiding service technique factor, guiding service creativity factor, guiding service entertainment factor, and guiding service practicality factor. The results of the study could be extended to other related studies. In terms of task completion time and accuracy rate, AR navigation is obviously better than the two plane map navigation modes. The users can find the direction of the destination within 7 s on average, and the accuracy rate is as high as 97.73%. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Trip misreporting forecast using count data model in a GPS enhanced travel survey

As Global Positioning System (GPS) technology advances, it has been increasingly used to supplement traditional self-reported travel surveys due to its promising features in capturing travel data with better accuracy and reliability. Realizing the limitations of diary-based surveys, this paper presents a study that directly accounts for trip misreporting behavior in trip generation models. Travel data were obtained from prompted-recall assisted GPS survey along with a diary-based survey. Negative Binomial models for count data were developed to accommodate misreporting behavior by introducing interaction effects of the sample-indicator variable with various personal and household variables. The interaction effects indicate how the impacts of the socioeconomic and demographic variables on trip-making vary across the two samples. Assuming that the GPS sample represents the ground truth, the interaction effects actually capture the likelihood and the extent of trip misreporting by detailed personal and household characteristics. The model results reveal significant interaction effects of several personal and household variables, indicating misreporting behavior associated with these attributes. The addition of interaction coefficients to the main effect model represents the real impacts of the independent variables, after compensating for trip misreporting behavior, if any.

IMPLEMENTATION OF VISIBLE LIGHT COMMUNICATION BASED SYSTEM FOR INDOOR POSITIONING

Visible light communication (VLC), have gain much interest in past decade in indoor communication. In particular, it is use for illumination and communication simultaneously. White light emitting diodes which are used as a transmitter for VLC are potential candidates for localization. Conventionally global positioning system (GPS) has very poor performance in indoor environments. Assisted global positioning system (AGPS) has positioning accuracy in few meter range. WLEDs can be used in small cell area inside the room hence indoor positioning can be measured accurately in centimeter. In this paper a WLEDs based indoor position system is investigated, the position was accurately confined in 11cm with low cost and on the shelf components. Article DOI: https://dx.doi.org/10.20319/Mijst.2017.31.6780 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Angle Difference Method for Vehicle Navigation in Multilevel Road Networks With a Three-Dimensional Transport GIS Database

Multilevel road networks such as grade-separated interchanges and elevated roads have been increasingly used to solve traffic congestion in large cities. When navigating a vehicle in a multilevel road network, identifying the location of the vehicle in different road levels is of equal importance to identifying its planar location, particularly for overlapping and parallel roads. Although they can be represented and visualized in the existing navigation system, at present, it is difficult to guide a vehicle through such a multilevel road network because the existing vehicle positioning system uses consumer-grade GPS, and the transport geographic information system (GIS-T) database is mainly 2-D-based. The location of a vehicle on diffrent road levels in multilevel road networks is often overlooked. This paper examines the deficiency of existing approaches in supporting vehicle navigation in multilevel road networks with consumer-grade GPS. It proposes to use an angle difference method that compares the vehicle pitch angle with the inclination angles of different road levels calculated from road elevations stored in the proposed GIS-T database to snap the vehicle to the appropriate road level when the vehicle is entering or exiting a multilevel road network. The angle difference method is implemented based on consumer-grade assisted GPS (A-GPS) and onboard vehicle pitch angle measurement with smartphone. Experiment results prove that the angle difference method have high accuracy in determining the road level when the vehicle is driving in a parallel multilevel road network.

Inference of network delays for SUPL 3.0-based assisted GNSS

The concept of assisted global positioning system (A-GPS) has successfully facilitated receiver operations in signal-challenged environments and enhanced GPS receiver performance in terms of time-t...

Network delay modeling for assisted GPS

Currently, conventional Global Positioning System (GPS) receivers work well in open-sky environments. However, location finding in weak-signal conditions, such as urban canyons and indoors, is challenging and has been the subject of extensive research. Assisted GPS (A-GPS) is one of the concepts that helps receivers acquire weak signals by receiving assistance data from wireless networks, such as orbital parameters, and coarse time and location references. A-GPS also improves time-to-first-fix (TTFF) through faster delivery of information that is recovered from navigation data broadcast by GPS satellites. Receiver technology developers rely on signal simulators for testing real-world scenarios. While A-GPS support has been integrated in many simulators, probabilistic models of delays occurring during assistance data delivery have not been studied properly. This paper provides a methodology of A-GPS network delay modeling that is applicable to various simulation environments. Particularly, a testbed is designed to collect delay data, model them statistically and integrate the model in a simulator. The testbed employs secure user plane location (SUPL) architecture in two modes, mobile station (MS) based (MS-based) and MS-assisted, where data channels are used to communicate A-GPS assistance data.Measurement campaigns are conducted and network delay models are derived for various representative distances between assistance servers and receivers, and for various networks that users connect to, such as LAN, WLAN, third-generation mobile telecommunication (3G), high-speed downlink packet access (HSDPA), and fourth-generation long term evolution (4G LTE), with the Transmission Control Protocol and the Internet Protocol (TCP/IP) connection between a server and a receiver.

Semi‐Supervised Learning in Inferring Mobile Device Locations

With the development of mobility technology, location information has become collectible by various positioning technologies. Different positioning technologies have their advantages and limitations. In particular, location accuracy varies among different technologies. In this paper, we propose semi‐supervised learning in inferring low‐accuracy location data density from high‐accuracy location data density. We focus on the enormous amount of low‐accuracy cell tower triangulation (CTT) calculated mobile device location data and the small amount of high‐accuracy assisted global positioning system (AGPS) pinpointed location data. The CTT and AGPS mobile device location data are collected for each cell tower that serves the devices, and then, the actual distribution is learned from both CTT and AGPS data by semi‐supervised learning, and the likelihood for low‐accuracy CTT location can be used as an accuracy indicator. The proposed method takes the advantage of the existing extensively collected location data and augments it by a machine‐learning algorithm. The method does not only complement the downside of one technology with the other technology but also considers the computing efficiency by extracting features that can segment the large data naturally, which is the essential in divide‐and‐conquer distributed big data processing. This big data approach improves the location accuracy statistically without the added complexity and cost of upgrading or replacing mobile networks or devices. Moreover, also, the proposed method focuses on the location density alignment, which avoids tracking individual user devices and preserves user privacy. Copyright © 2014 John Wiley & Sons, Ltd.

Fast Multi-Satellite ML Acquisition for A-GPS

Successful position fix in harsh environments such as indoors and dense urban canyons is a strongly required capability for an assisted global positioning system (A-GPS) receiver. In recently developed cellular networks, receiving fine time assistance and maintaining high-frequency accuracy using downlink measurements are not possible for A-GPS receivers, since node-Bs are asynchronous and are not equipped with a source for precise time and frequency. In this paper, we propose a correlator-based fast multi-satellite maximum likelihood (MSML) algorithm, for A-GPS receivers in asynchronous networks, that achieves fast acquisition utilizing fast computation techniques. From numerous Monte Carlo simulations, it is demonstrated that the proposed fast MSML algorithm, when compared with conventional correlator-based acquisition techniques used in standalone GPS and A-GPS receivers, provides higher detection sensitivity for weak signals in the presence of other strong signals by removing strong inter-satellite interference (ISI).

Measurement and Modeling of Network Delays for MS-Based A-GPS Assistance Delivery

While conventional global positioning system (GPS) receivers have gained popularity for applications in open-sky environments, their performance degrades in challenging conditions, such as urban canyons and indoors. The assisted GPS (A-GPS) concept enhanced receiver capability to operate in weak signal conditions using terrestrial links to communicate helpful information such as satellite orbital parameters, coarse estimates of time and location, and so forth. The A-GPS mode has been standardized for many wireless networks and is integrated in high-end simulators to support development and testing efforts. As the terrestrial-assistance data channel becomes an important link for the receiver, the statistical modeling of information delivery delays should be addressed to simulate more adequate operation scenarios. This paper addresses this aspect by presenting a delay measurement and modeling approach for Mobile Station Based assistance data. Even though the approach can be applied to other measurement setups, this paper is presented for standardized secure user plane location architecture that exploits data channels of wireless networks to deliver assistance data. Measurement data are collected and modeled for various scenarios of relative deployment of assistance servers and receivers. Delays are modeled for LAN, WLAN, third-generation mobile telecommunication, high-speed downlink packet access, and fourth-generation long-term evolution networks, with the transmission control protocol and the Internet protocol connection between a server and receiver.

Real-Time Transportation Mode Detection Using Smartphones and Artificial Neural Networks: Performance Comparisons Between Smartphones and Conventional Global Positioning System Sensors

Traditionally, traffic monitoring requires data from traffic cameras, loop detectors, or probe vehicles that are usually operated by dedicated employees. In efforts to reduce the capital and operational costs associated with traffic monitoring, departments of transportation have explored the feasibility of using global positioning system (GPS) data loggers on their probe vehicles that are postprocessed for analyzing the traffic patterns on desired routes. Furthermore, most cell phones are equipped with embedded assisted-GPS (AGPS) chips, and if the mode of transportation the phone is in can be anonymously identified, the phones can be treated as if they are probe vehicles that are voluntarily hovering throughout the city, at minimal additional costs. Emerging cell phones known as “smartphones” are equipped with additional sensors including an accelerometer and magnetometer. The accelerometer can directly measure the acceleration values, as opposed to having acceleration values derived from speed values in conventional GPS sensors. The magnetometer can measure mode-specific electromagnetic levels. Smartphones are subscribed with roadside Internet data plans that can provide an essential platform for real-time traffic monitoring. In this article, neural network-based artificial intelligence is used to identify the mode of transportation by detecting the patterns of distinct physical profile of each mode that consists of speed, acceleration, number of satellites in view, and electromagnetic levels. Results show that newly available values in smartphones improve the mode detection rates when compared with using conventional GPS data loggers. When smartphones are in known orientations, they can provide three-dimensional (3-D) acceleration values that can further improve mode detection accuracies.

Dichotomous search of coarse time error in collective detection for GPS signal acquisition

Coarse time error (CTE) is an additional systematic absolute-timing-related bias that must be compensated in an assisted GPS (A-GPS)-based snapshot receiver where the complex baseband signal available for processing is finite and short. Resolving for CTE instead of waiting for the time of week string in the satellite's navigation message allows A-GPS receivers to achieve faster time to first fix in cold start and warm start conditions. This paper highlights the problem of CTE that is conventionally resolved using coarse time positioning (CTP)--a least-squares-based algorithm. Following this, the same problem is shown to occur when collective detection (CD)--a direct position estimation algorithm--is applied in place of CTP. Previous literature on CD has not discussed nor presented any resolution to the CTE problem. Directly augmenting CTE to be resolved together with the user's position and common clock bias in CD requires huge computational resources, which is impractical to be implemented using current generation consumer-grade computers. Therefore, the main contribution of this research is to propose and implement a dichotomous search jointly with CD to resolve for the CTE and position-common-clock-bias vector, respectively, at a relatively low computational burden. Empirical results using live satellite signals show that the proposed method is capable of effectively eliminating the positioning error biases caused by CTE. It is shown that the proposed compensation method can achieve equivalent performance to the conventional CTP method without losing the benefits of CD.

When assistance becomes dependence

Location based services are a vital component of the mobile ecosystem. Among all the location technologies used behind the scenes, A-GPS (Assisted-GPS) is considered to be the most accurate. Unlike standalone GPS systems, A-GPS uses network support to speed nup position fix. However, it can be a dangerous strategy due to varying cell conditions which may impair performance, sometimes potentially neglecting the expected benefits of the original design. We present the characterization of the accuracy, location acquisition speed, energy cost, and network dependency of the state of the art A-GPS receivers shipped in popular mobile devices. Our analysis is based on active measurements, an exhaustive on-device analysis, and cellular traffic traces processing. The results reveals a number of inefficiencies as a result of the strong dependence on the cellular network to obtain assisting data, implementation, and integration problems.