Novel Encryption Method of GPS Information in Image File Using Format-Preserving Encryption

Mobile Geographic Information Systems (GIS) is an outcome of integrating GIS, wireless communication, embeds and mobile computing technologies. Based on .NET Compact Framework and GPRS a Mobile GIS with own patent is developed. Services in the system include: downloading map by General Packet Radio Service (GPRS), defining geographical coordinate system of maps, Global Position System (GPS) connection and information dynamically showing on map, sharing users' GPS information through SMS (Short Message Service) to establishes communication between system users, vector feature map and related property data editing and shortest route planning. The system is now used in field data collection and simple navigation. This paper introduces the implementation of some basic GIS services in mobile environment. And some implemented services are presented and studied in detail.

In this paper we propose a full-view car navigator system which registers full-view spherical images with a digital route map via the GPS (global positioning system) information. A full-view image is captured by a spherical image sensor; the vibration of spherical camera is compensated by the information from an inertial sensor; the rectified spherical image is finally registered to digital route map via GPS information. Since an image sequence is directly registered no 3D model building is required. Since a full-view image is used the urban scene with high buildings can be wholly recorded and there are no limits on view direction. It results in a friendly human interface in our car navigator system.

In this paper, we develop an alternative navigation system for Unmanned Aerial Vehicle (UAV) in Global Positioning Systems (GPS)-denied environment. We use two image inputs, one is acquired with an on-board camera placed on the UAV (which is the large-area image) and the other is from satellite images (which is small known image) with GPS information. We use a convolutional neural network (CNN) architecture based on Oxford’s Visual Geometry Group network (VGG-16) and utilize normalized variant mutual information between these two images to obtain position of the UAV. Satellite images are labelled and given to the UAV. When GPS information is lost, our algorithm starts to function and images from UAV camera are searched whether satellite image is seen by cameras on UAV image or not. If the UAV is in that area, our algorithm finds the GPS information from satellite image data.

Global Positioning System (GPS) spoofing is a common mean to cheat an Unmanned Aerial Vehicle (UAV) which is guided by the INS/GPS integrated navigation system. Detecting the spurious GPS signal is the precondition of anti-spoofing for UAVs. A detection system of GPS spoofing is designed based on dynamics identification. The centroid motion model and attitude motion model were established. The measurement information from inertial measurement units (IMUs) and positioning information from GPS both are visualized responses to the dynamic laws of UAVs. These two types of measurement information were separately used to estimate the dynamics parameters of UAVs online using two Extended Kalman Filters (EKFs). The detection of spurious GPS signals was realized effectively based on the monitoring of relative estimation errors. Numerical simulations demonstrated that the dynamics parameters could be estimated online by the combined filters. And the precision and accuracy of filter #1, which was based on IMU information, were both higher than those of filter #2, which was based on GPS information. When the spurious GPS signal was just zero-order continuous with the true signal at the penetration point, a pulse relative error would occur at the penetration point; when the spurious GPS signal was first-order continuous with the true signal at the penetration point, a step relative error would occur. Furthermore, relative systematic errors matched with the period of spurious signals.

Car navigation systems (CNS) have become an interesting research and industrial topic due to their wide deployment. However, updating and distribution of the road map database is a complex and high cost process. In this paper, we propose a real-time map generation and updating algorithm, which fulfills or corrects the unknown road area of the conventional road map database by using global positioning system (GPS) information and wireless communication technologies. To generate the road map of unknown roads, the proposed algorithm uses road images, which can be obtained when a driver travels unknown roads. When a driver travels unknown roads, which do not appear on the map of the car navigation system, the car navigation system extracts GPS and image information of the unknown roads. Then, the car navigation system generates the road map for the unknown roads by extracting location and road information, and updates the road map database in the car. After updating the local road map database, the car navigation system updates the master road map database, which is located at the network and is distributed to other car navigation systems. Thus, the road map database provider can update the road map database of other car navigation systems, which share the same master road map database. The proposed algorithm provides an efficient updating process of the road map database with a simplified process and reduced time for unknown road areas.

Intelligent transportation systems have gained importance as the number of people and means of transportation has increased. Almost all of the transportation systems are GPS (Global Positioning System) based. Alternative route guidance systems are needed in areas where GPS signals are not sufficient or not fully reached, in areas surrounded by tunnels or high buildings. In this work, a position estimation system using the speed acceleration height and compass information together with the latest position information of the mobile device has been developed and integrated into the navigation system even if the GPS information is interrupted. The improved self-navigation system can be used both in the city and also in large areas where the final position is known. It is also aimed to develop a system that will help them to predict the positions of military vehicles both on land and in the sea if the satellites are blunted during wartime.

This paper presents a novel approach to address the challenge of self-localization of flying vehicles. It utilizes visual cues provided by the map imagery fed to a map-recognition convolution neural-network (CNN). This approach is invaluable during the navigation of flying vehicles in scenarios where the Global Positioning System (GPS) signal is unavailable. The proposed approach leverages the power of convolutional neural networks (CNNs) to imitate the visual perception and navigation abilities of homing pigeons, enabling the vehicle to navigate using solely real- time visual data with limited or no GPS information. Two pre-trained CNN’s (SqueezeNet and GoogLeNet) are selected and re-trained with Google Maps imagery, enabling them to efficiently learn and generalize from the diverse visual attributes present in the map. Extensive experimentation and evaluation have demonstrated the efficacy and resilience of the vision-based GPS-free navigation system. The resulting system predicts position accurately achieving an accuracy of 89.9% and 96.4% for SqueezeNet and GoogLeNet, respectively, for images with a resolution of (one km x one km) and reaching an accuracy of 94.7 for GoogLeNet for images with a resolution of (374 m x 374 m). Results underscore the potential of this approach for overcoming the challenge of GPS unavailability in aerial navigation.

In the research reported in this paper, we propose to overcome the unavailability of Global Positioning System (GPS) using combined information obtained from a scanning LADAR rangefinder on an Unmanned Ground Vehicle (UGV) and a LADAR mounted on an Unmanned Aerial Vehicle (UAV) that flies over the terrain being traversed. The approach to estimate and update the position of the UGV involves registering range data from the two LADARs using a combination of a feature-based registration method and a modified version of the well-known Iterative Closest Point (ICP) algorithm. Registration of range data thus guarantees an estimate of the vehicle's position even when only one of the vehicles has GPS information. Additionally, such registration over time (i.e., from sample to sample), enables position information to be maintained even when both vehicles can no longer maintain GPS contact. The approach has been validated by conducting systematic experiments on complex real-world data.

During prehistoric times, the Colorado River occasionally meandered into and filled the Salton Sea Basin, creating several huge inland lakes, variously called Lake LeConte and Lake Cahuilla. Previous researchers have identified high stands of these ancient lakes using standard survey methods. The objective of this investigation was to further delineate the prehistoric shorelines using satellite imagery, global positioning system (GPS) and geographic information system (GIS) technologies. Using one-meter digital orthophotographs, points were selected in the laboratory and were located in the field using a GPS. Point data were integrated with a digital elevation model (DEM) and elevation contours were plotted on Landsat-TM images, generating a range of prehistoric shorelines. Contours were then correlated with archaeological site data, geomorphic features, and other factors to reconstruct Early American settlement patterns for Lake Cahuilla. The combined GIS coverages of ancient Lake Cahuilla and cultural resources may be used together as a model for cultural resource constraints, identifying areas of high cultural resource sensitivity for evaluation of potential impacts as a result of implementation of Salton Sea restoration project alternatives.

The main focus of this project addresses some of the key problems in the postal industry which are adhoc pickups, efficient and quick delivery, increase in vehicle maintenance, overtime and fuel cost. Companies involved in postal industry are spending millions of extra dollars to fulfill customer’s need. There is a need of some reliable solution which can help improving the existing processes which are still using old technologies. This project uses advanced and reliable state of the art technologies like Global Positioning System (GPS) and Geographical Information System (GIS) to improve the existing process of Pickup and Delivery (P&D) of parcels which are the two fundamental services provided by the postal industry. Today, there is a huge competition and the companies involved in postal industry are allocating a lot of budget to provide better, improved, reliable and satisfactory service to the customers. This project presents an integrated approach to combine GPS and GIS to help improving the existing pickup/delivery process. A GPS enabled mobile device capable of wireless connectivity can be mounted in the courier’s vehicle. The existing shipment and tracking application can be modified to communicate to GPS and transmit GPS data to the server wirelessly. GIS functionality can be embedded in the server side to calculate the shortest route between the courier’s current position and the customer address. Similarly, digital maps can be used for visual display of the couriers and their online activities.

Rustling is an age-old practice that was widespread in Italy until the first half of the 20th century. Today, incidents of cattle rustling are again being reported. However, the problem is not only found in Italy. It is also becoming a plague for ranchers in the US and is still rampant in East Africa. In Italy, the cattle rustling phenomena have usually been limited through the direct control of the herdsmen. Global positioning system (GPS) and geographic information system (GIS) combined technologies are increasingly applied for tracking and monitoring livestock with greater spatial and temporal resolution. However, so far, no case studies of the use of GPS technology to combat cattle rustling have been reported in the literature. The aim of this research was to develop a GPS/GSM (global system for mobile communication) collar, using commercial hardware and implementing a specific software [ARVAshepherd 1.0; ARVAtec Srl, Rescaldina (MI), Italy] to track animals’ movements outside their grazing area and to signal when animals are straying outside virtual perimeters. A phase I study was conducted from January to June 2011 to build the GPS/GSM collar and to assess its performances in terms of GPS accuracy and precision, while a phase II study was conducted in July 2011 to test the GPS collar under real-life operating conditions. The static GPS positioning error achieved a circular error probable (50%) and horizontal 95% accuracy of 1.462 m and 4.501 m, respectively. This is comparable with values obtained by other authors in static tests of a commercial GPS collar for grazing studies. In field tests, the system was able to identify the incorrect position of the cattle and the warning messages were sent promptly to the farmer, continuing until the animals had been repositioned inside the fence, thus highlighting the potential of the GPS/GSM collar as an anti-theft system.

Automated positioning dual-axis solar tracking system with precision elevation and azimuth angle control

Personal digital assistant (PDA) applications to improve the effectiveness and reliability in geotechnical investigations have been developed. Because of PDA's legerity and mobility, it is an excellent choice for use with a simple global position system (GPS) in geotechnical field work, such as site reconnaissance, subsurface investigation, surface investigation, safety evaluation, testing, and instrumentation. Using PDAs in geotechnical engineering can save traditional field paper forms, avoid repeated record input, reduce the risk of wrong data interpretation, and shorten the time and space “barrier” between the office and the field. After the field data are collected on the PDA, they can be transferred to a desktop personal computer (PC) for further processing and reporting. The desktop PC is designated to handle more complicated analyses and visual presentations. Specialized functions of applications to operate PDA and GPS, as well as for the desktop PC, were developed according to the characteristics of the geotechnical tasks. The concept and framework were applied for three geotechnical assistant systems: drilling investigation assistant system, surface geologic investigation assistant system, and slope safety evaluation assistant system. In addition, an example illustrates the application of GPS, PDA, and geographic information systems for debris flow reconnaissance. Using PDAs improves the effectiveness and reliability of geotechnical investigations and is promising for further development.

In vehicular ad hoc networks (VANets), a precise localization system is a crucial factor for several critical safety applications. The global positioning system (GPS) is commonly used to determine the vehicles’ position estimation. However, it has unwanted errors yet that can be worse in some areas, such as urban street canyons and indoor parking lots, making it inaccurate for most critical safety applications. In this work, we present a new position estimation method called cooperative vehicle localization improvement using distance information (CoVaLID), which improves GPS positions of nearby vehicles and minimize their errors through an extended Kalman filter to execute Data Fusion using GPS and distance information. Our solution also uses distance information to assess the position accuracy related to three different aspects: the number of vehicles, vehicle trajectory, and distance information error. For that purpose, we use a weighted average method to put more confidence in distance information given by neighbors closer to the target. We implement and evaluate the performance of CoVaLID using real-world data, as well as discuss the impact of different distance sensors in our proposed solution. Our results clearly show that CoVaLID is capable of reducing the GPS error by 63%, and 53% when compared to the state-of-the-art VANet location improve (VLOCI) algorithm.

In this paper, a novel approach for improving vehicular positioning is presented. This method is based on the cooperation of the vehicles by communicating their measured information about their position. This method consists of two steps. In the first step, we introduce our cooperative map matching method. This map matching method uses the V2V communication in a vehicular ad hoc network (VANET) to exchange global positioning system (GPS) information between vehicles. Having a precise road map, vehicles can apply the road constraints of other vehicles in their own map matching process and acquire a significant improvement in their positioning. After that, we have proposed the concept of a dynamic base station DGPS (DDGPS), which is used by vehicles in the second step to generate and broadcast the GPS pseudorange corrections that can be used by newly arrived vehicles to improve their positioning. The DDGPS is a decentralized cooperative method that aims to improve the GPS positioning by estimating and compensating the common error in GPS pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS method, the pseudorange corrections are estimated based on the receiver's belief on its positioning and its uncertainty and then broadcasted to other GPS receivers. The performance of the proposed algorithm has been verified with simulations in several realistic scenarios.