Relevant bibliographies by topics / GPS-Denied navigation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'GPS-Denied navigation'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "GPS-Denied navigation"

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Suzuki, Satoshi. "Integrated Navigation for Autonomous Drone in GPS and GPS-Denied Environments." Journal of Robotics and Mechatronics 30, no. 3 (June 20, 2018): 373–79. http://dx.doi.org/10.20965/jrm.2018.p0373.

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In this study, a novel robust navigation system for a drone in global positioning system (GPS) and GPS-denied environments is proposed. In general, the drone uses position and velocity information from GPS for guidance and control. However, GPS cannot be used in several environments; for example, GPS exhibits huge errors near buildings and trees, indoor environments. In such GPS-denied environments, a Laser Imaging Detection and Ranging (LIDAR) sensor-based navigation system has generally been used. However, the LIDAR sensor also has a weakness, and it cannot be used in an open outdoor environment where GPS can be used. Therefore, it is advantageous to develop an integrated navigation system that operates seamlessly in both GPS and GPS-denied environments. In this study, an integrated navigation system for the drone using GPS and LIDAR was developed. The design of the navigation system is based on the extended Kalman filter, and the effectiveness of the developed system is verified by numerical simulation and experiment.
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Lee, Jong Ki, Dorota A. Grejner-Brzezinska, and Charles Toth. "Network-based Collaborative Navigation in GPS-Denied Environment." Journal of Navigation 65, no. 3 (March 23, 2012): 445–57. http://dx.doi.org/10.1017/s0373463312000069.

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Global Positioning System (GPS) has been used as a primary source of navigation in land and airborne applications. However, challenging environments cause GPS signal blockage or degradation, and prevent reliable and seamless positioning and navigation using GPS only. Therefore, multi-sensor based navigation systems have been developed to overcome the limitations of GPS by adding some forms of augmentation. The next step towards assured robust navigation is to combine information from multiple ground-users, to further improve the chance of obtaining reliable navigation and positioning information. Collaborative (or cooperative) navigation can improve the individual navigation solution in terms of both accuracy and coverage, and may reduce the system's design cost, as equipping all users with high performance multi-sensor positioning systems is not cost effective. Generally, ‘Collaborative Navigation’ uses inter-nodal range measurements between platforms (users) to strengthen the navigation solution. In the collaborative navigation approach, the inter-nodal distance vectors from the known or more accurate positions to the unknown locations can be established. Therefore, the collaborative navigation technique has the advantage in that errors at the user's position can be compensated by other known (or more accurate) positions of other platforms, and may result in the improvement of the navigation solutions for the entire group of users. In this paper, three statistical network-based collaborative navigation algorithms, the Restricted Least-Squares Solution (RLESS), the Stochastic Constrained Least-Squares Solution (SCLESS) and the Best Linear Minimum Partial Bias Estimation (BLIMPBE) are proposed and compared to the Kalman filter. The proposed statistical collaborative navigation algorithms for network solution show better performance than the Kalman filter.
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Leishman, Robert C., Timothy W. McLain, and Randal W. Beard. "Relative Navigation Approach for Vision-Based Aerial GPS-Denied Navigation." Journal of Intelligent & Robotic Systems 74, no. 1-2 (October 9, 2013): 97–111. http://dx.doi.org/10.1007/s10846-013-9914-7.

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Bachrach, Abraham, Samuel Prentice, Ruijie He, and Nicholas Roy. "RANGE-Robust autonomous navigation in GPS-denied environments." Journal of Field Robotics 28, no. 5 (August 9, 2011): 644–66. http://dx.doi.org/10.1002/rob.20400.

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SUZUKI, Satoshi, Hongkyu MIN, Tetsuya WADA, and Kenzo NONAMI. "Integrated Navigation of Aerial Robot in GPS and GPS-denied Environment." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2016 (2016): 1P1–10a2. http://dx.doi.org/10.1299/jsmermd.2016.1p1-10a2.

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Suzuki, Satoshi, Hongkyu Min, Tetsuya Wada, and Kenzo Nonami. "Integrated navigation of aerial robot for GPS and GPS-denied environment." Journal of Physics: Conference Series 744 (September 2016): 012219. http://dx.doi.org/10.1088/1742-6596/744/1/012219.

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Soldado Serrano, Inmaculada, José García Doblado, Ignazio Federico Finazzi, and María Ángeles Martín Prats. "Simless GSM positioning for navigation in GPS-denied environments." Aircraft Engineering and Aerospace Technology 90, no. 7 (October 1, 2018): 1072–76. http://dx.doi.org/10.1108/aeat-01-2017-0029.

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Purpose Until now, air navigation systems have mainly relied on global navigation satellite systems (GNSS) on the worldwide spread global positioning system. However, the so-called GNSS-denied environments open a new research line which pursues the development of alternative technologies which will cover this gap in positioning systems’ services. Design/methodology/approach This paper presents the possibility of using positioning systems based on global system for mobile communications (GSM) signal. This approach developed in a standalone device will provide real-time information. The presented algorithm pursues a new methodology for providing useful information. Findings Among all the different technologies aimed at giving a navigation solution in the absence of any kind of GNSS in which this paper is based, it advocates for the use of the signals of opportunity, particularly the usage of GSM. Practical implications Technology is currently immersed in an era of continuous progress and expansion of navigation systems. These are evolving toward high performance systems, offering precise, efficient and safe air navigation. In addition, the growing demand for unmanned aerial vehicles increases the level of exigency on this activity even more. Therefore, in the context of the development of unmanned navigation technologies, the aim is to implement positioning systems that will allow high precision even in though environments. Originality/value Referencing the SIMless concept, a SIM-free system is described in this paper. The SIM-free system is supported by open data bases and permits the positioning based on the information sniffed from the signals broadcast by a set of several nearby base station of the GSM network. Hence, it provides same and in some cases even better accuracies than the already developed techniques, not being necessary to synchronize the link between the mobile terminal and the base station transceiver (BTS).
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Aftatah, Mohammed, Abdelkabir Lahrech, Abdelouahed Abounada, and Aziz Soulhi. "GPS/INS/Odometer Data Fusion for Land Vehicle Localization in GPS Denied Environment." Modern Applied Science 11, no. 1 (October 11, 2016): 62. http://dx.doi.org/10.5539/mas.v11n1p62.

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The main purpose of this paper is to present a fusion approach to bridge the period of Global Positioning System (GPS) outages using two proprioceptive sensors that are the Inertial Navigation System (INS) and the odometer in order to assure a continuous localization for land vehicle in urban areas where GPS signal blockage is very often. Odometer and GPS measures are exploited to correct inertial sensor errors. In fact, during GPS availability, INS is integrated with GPS to provide accurate localization solution; whereas during GPS outages, the odometer measurements are used to correct the INS error thereby improving the positioning accuracy and assuring the continuity of navigation solution. The problem of estimation of vehicle localization is realized by Kalman Filter (KF) that merges sensor measurements. The paper thus introduces results from simulation and real data.
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Ali, Abdelrahman, and Naser El-Sheimy. "Low-Cost MEMS-Based Pedestrian Navigation Technique for GPS-Denied Areas." Journal of Sensors 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/197090.

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The progress in the micro electro mechanical system (MEMS) sensors technology in size, cost, weight, and power consumption allows for new research opportunities in the navigation field. Today, most of smartphones, tablets, and other handheld devices are fully packed with the required sensors for any navigation system such as GPS, gyroscope, accelerometer, magnetometer, and pressure sensors. For seamless navigation, the sensors’ signal quality and the sensors availability are major challenges. Heading estimation is a fundamental challenge in the GPS-denied environments; therefore, targeting accurate attitude estimation is considered significant contribution to the overall navigation error. For that end, this research targets an improved pedestrian navigation by developing sensors fusion technique to exploit the gyroscope, magnetometer, and accelerometer data for device attitude estimation in the different environments based on quaternion mechanization. Results indicate that the improvement in the traveled distance and the heading estimations is capable of reducing the overall position error to be less than 15 m in the harsh environments.
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Perez-Grau, Francisco J., Ricardo Ragel, Fernando Caballero, Antidio Viguria, and Anibal Ollero. "An architecture for robust UAV navigation in GPS-denied areas." Journal of Field Robotics 35, no. 1 (October 11, 2017): 121–45. http://dx.doi.org/10.1002/rob.21757.

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Dissertations / Theses on the topic "GPS-Denied navigation"

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O'Shea, Patrick Joseph S. M. Massachusetts Institute of Technology. "Multiple hypothesis positioning algorithm for robust GPS-denied navigation." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122396.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 113-117).
In the past few decades, GPS has become the dominant source of precision navigation and is often required for many modern systems to operate. However, recent exposure of GPS vulnerabilities have called into question its overall resiliency and shown necessity for robust alternatives. Precision celestial navigation using Draper's Skymark technique can be used to replace GPS. However, these systems rely on prior position knowledge for system initialization. In GPS-denied scenarios, prior position knowledge may not be available or trustworthy. Similarly, other GPS-denied navigation techniques such as landmark navigation or vision-aided navigation can be difficult when there is limited prior position information. Therefore, the Multiple Hypothesis Positioning algorithm is developed in this thesis to provide robust positioning in GPS-denied navigation scenarios where little or no prior position knowledge is available.
The proposed robust positioning algorithm makes use of Multiple Hypothesis Tracking techniques to develop an object identification and observer positioning framework. The Multiple Hypothesis Positioning framework is developed broadly in this thesis to encompass multiple applications of the proposed algorithm. The Multiple Hypothesis Positioning framework is applied to two separate applications including a Lost-at-Sea positioning algorithm and a Lost-in-a-Forest positioning algorithm. The Lost-at-Sea application serves as an initialization process for Draper's Skymark technique in situations where no prior position knowledge is available. The Lost-in-a-Forest positioning algorithm uses pattern matching techniques to identify trees near an observer and compare these locally observed trees to a global map of all tree locations. The pattern matching techniques are combined with the Multiple Hypothesis Positioning framework to determine the observer's global position.
Both applications were tested in robust Monte Carlo simulations with positive results. Overall, the proposed Multiple Hypothesis Positioning algorithm and framework prove effective tools for robust positioning in GPS-denied navigation applications where prior position information is unavailable.
"The material included in this thesis was funded through internal research and development funds from the Charles Stark Draper Laboratories. This research is this thesis was supported by the Draper Education Office and the Draper Fellowship Program"--Page 5.
by Patrick Joseph O'Shea.
S.M.
S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics
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James, Sisa. "Localisation and navigation in GPS-denied environments using RFID tags." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/13281.

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Includes bibliographical references.
This dissertation addresses the autonomous localisation and navigation problem in the context of an underground mining environment. This kind of environment has little or no features as well as no access to GPS or stationary towers, which are usually used for navigation. In addition dust and debris may hinder optical methods for ranging. This study looks at the feasibility of using randomly distributed RFID tags to autonomously navigate in this environment. Clustering of observed tags are used for localisation, subsequently value iteration is used to navigate to a defined goal. Results are presented, concluding that it is feasible to localise and navigate using only RFID tags, in simulation. Localisation feasibility is also confirmed by experimental measurements.
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Wu, Allen David. "Vision-based navigation and mapping for flight in GPS-denied environments." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37281.

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Traditionally, the task of determining aircraft position and attitude for automatic control has been handled by the combination of an inertial measurement unit (IMU) with a Global Positioning System (GPS) receiver. In this configuration, accelerations and angular rates from the IMU can be integrated forward in time, and position updates from the GPS can be used to bound the errors that result from this integration. However, reliance on the reception of GPS signals places artificial constraints on aircraft such as small unmanned aerial vehicles (UAVs) that are otherwise physically capable of operation in indoor, cluttered, or adversarial environments. Therefore, this work investigates methods for incorporating a monocular vision sensor into a standard avionics suite. Vision sensors possess the potential to extract information about the surrounding environment and determine the locations of features or points of interest. Having mapped out landmarks in an unknown environment, subsequent observations by the vision sensor can in turn be used to resolve aircraft position and orientation while continuing to map out new features. An extended Kalman filter framework for performing the tasks of vision-based mapping and navigation is presented. Feature points are detected in each image using a Harris corner detector, and these feature measurements are corresponded from frame to frame using a statistical Z-test. When GPS is available, sequential observations of a single landmark point allow the point's location in inertial space to be estimated. When GPS is not available, landmarks that have been sufficiently triangulated can be used for estimating vehicle position and attitude. Simulation and real-time flight test results for vision-based mapping and navigation are presented to demonstrate feasibility in real-time applications. These methods are then integrated into a practical framework for flight in GPS-denied environments and verified through the autonomous flight of a UAV during a loss-of-GPS scenario. The methodology is also extended to the application of vehicles equipped with stereo vision systems. This framework enables aircraft capable of hovering in place to maintain a bounded pose estimate indefinitely without drift during a GPS outage.
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Mackie, James David. "Compact FMCW Radar for GPS-Denied Navigation and Sense and Avoid." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4388.

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Location information is vital for any type of aircraft and even more crucial for Unmanned Aerial Systems (UAS). GPS is a readily available solution but signals can easily be jammed or lost. In this thesis, radar is explored as a backup system for self-localization when GPS signals are not available. The method proposed requires that an area be pre mapped by collecting radar data with known latitude and longitude coordinates. New radar data is then collected and compared to previously stored values. Channel matrices are stored at each point and are used as the basis for location comparisons. Various methods of matrix comparison are used and both simulation as well as experimental results are shown. The main results of this thesis show that position can be determined using channel matrices if the sensor is within a certain radius of previously stored locations. This radius is on the order of a wavelength or less. Using correlation matrix comparisons the radius of localization is broadened. A novel method using multiple channel and multiple frequency data proves to be successful and determines the position of an octorotor UAS with a mean position error of less than three meters. The design of a low-cost, compact, and light-weight FMCW radar for two applications is also presented. The first application is a novel radar based positioning system that utilizes multiple channel and multiple frequency information to determine position. The second application is a UAS sense and avoid system using a monopulse configuration. Without connectors or antennas, the radar weighs 45.7 grams, is 7.5 cm x 5 cm x 3 cm in size, and costs less than $100 when built in quantities of 100 or more (excludes antennas and connectors). It is tested using delay lines and corner reflectors and accurately determines the distance to close range targets.
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Reid, Zachary A. "Leveraging 3D Models for SAR-based Navigation in GPS-denied Environments." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1540419210051179.

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Ellingson, Gary James. "Cooperative Navigation of Fixed-Wing Micro Air Vehicles in GPS-Denied Environments." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8706.

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Micro air vehicles have recently gained popularity due to their potential as autonomous systems. Their future impact, however, will depend in part on how well they can navigate in GPS-denied and GPS-degraded environments. In response to this need, this dissertation investigates a potential solution for GPS-denied operations called relative navigation. The method utilizes keyframe-to-keyframe odometry estimates and their covariances in a global back end that represents the global state as a pose graph. The back end is able to effectively represent nonlinear uncertainties and incorporate opportunistic global constraints. The GPS-denied research community has, for the most part, neglected to consider fixed-wing aircraft. This dissertation enables fixed-wing aircraft to utilize relative navigation by accounting for their sensing requirements. The development of an odometry-like, front-end, EKF-based estimator that utilizes only a monocular camera and an inertial measurement unit is presented. The filter uses the measurement model of the multi-state-constraint Kalman filter and regularly performs relative resets in coordination with keyframe declarations. In addition to the front-end development, a method is provided to account for front-end velocity bias in the back-end optimization. Finally a method is presented for enabling multiple vehicles to improve navigational accuracy by cooperatively sharing information. Modifications to the relative navigation architecture are presented that enable decentralized, cooperative operations amidst temporary communication dropouts. The proposed framework also includes the ability to incorporate inter-vehicle measurements and utilizes a new concept called the coordinated reset, which is necessary for optimizing the cooperative odometry and improving localization. Each contribution is demonstrated through simulation and/or hardware flight testing. Simulation and Monte-Carlo testing is used to show the expected quality of the results. Hardware flight-test results show the front-end estimator performance, several back-end optimization examples, and cooperative GPS-denied operations.
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Lewis, Benjamin Paul. "A Visual Return-to-Home System for GPS-Denied Flight." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6254.

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Unmanned aerial vehicle technology is rapidly maturing. In recent years, the sight of hobbyist aircraft has become more common. Corporations and governments are also interested in using drone aircraft for applications such as package delivery, surveillance and communications. These autonomous UAV technologies demand robust systems that perform under any circumstances. Many UAV applications rely on GPS to obtain information about their location and velocity. However, the GPS system has known vulnerabilities, including environmental signal degradation, terrestrial or solar weather, or malicious attacks such as GPS spoofing. These conditions occur with enough frequency to cause concern. Without a GPS signal, the state estimation in many autopilots quickly degrades. In the absence of a reliable backup navigation scheme, this loss of state will cause the aircraft to drift off course, and in many cases the aircraft will lose power or crash. While no single approach can solve all of the issues with GPS signal degradation, individual events can be addressed and solved. In this thesis, we present a system which will return an aircraft to its launch point upon the loss of GPS. This functionality is advantageous because it allows recovery of the UAV in circumstances which the lack of GPS information would make difficult. The system presented in this thesis accomplishes the return of the aircraft by means of onboard visual navigation, which removes the dependence of the aircraft on external sensors and systems. The system presented here uses an downward-facing onboard camera and computer to capture a string of overlapping images (keyframes) of the ground as the aircraft travels on its outbound journey. When a signal is received, the aircraft switches into return-to-home mode. The system uses the homography matrix and other vision processing techniques to produce information about the location of the current keyframe relative to the aircraft. This information is used to navigate the aircraft to the location of each saved keyframe in reverse order. As each keyframe is reached, the system programmatically loads the next target keyframe. By following the chain of keyframes in reverse, the system reaches the launch location. Contributions in this thesis include the return-to-home visual flight system for UAVs, which has been tested in simulation and with flight tests. Features of this system include methods for determining new keyframes and switching keyframes on the inbound flight, extracting data between images, and flight navigation based on this information. This system is a piece of the wider GPS-denied framework under development in the BYU MAGICC lab.
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Mayalu, Jr Alfred Kulua. "Beyond LiDAR for Unmanned Aerial Event-Based Localization in GPS Denied Environments." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104024.

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Finding lost persons, collecting information in disturbed communities, efficiently traversing urban areas after a blast or similar catastrophic events have motivated researchers to develop intelligent sensor frameworks to aid law enforcement, first responders, and military personnel with situational awareness. This dissertation consists of a two-part framework for providing situational awareness using both acoustic ground sensors and aerial sensing modalities. Ground sensors in the field of data-driven detection and classification approaches typically rely on computationally expensive inputs such as image or video-based methods [6, 91]. However, the information given by an acoustic signal offers several advantages, such as low computational needs and possible classification of occluded events including gunshots or explosions. Once an event is identified, responding to real-time events in urban areas is difficult using an Unmanned Aerial Vehicle (UAV) especially when GPS is unreliable due to coverage blackouts and/or GPS degradation [10]. Furthermore, if it is possible to deploy multiple in-situ static intelligent acoustic autonomous sensors that can identify anomalous sounds given context, then the sensors can communicate with an autonomous UAV that can navigate in a GPS-denied urban environment for investigation of the event; this could offer several advantages for time-critical and precise, localized response information necessary for life-saving decision-making. Thus, in order to implement a complete intelligent sensor framework, the need for both an intelligent static ground acoustic autonomous unattended sensors (AAUS) and improvements to GPS-degraded localization has become apparent for applications such as anomaly detection, public safety, as well as intelligence surveillance and reconnaissance (ISR) operations. Distributed AAUS networks could provide end-users with near real-time actionable information for large urban environments with limited resources. Complete ISR mission profiles require a UAV to fly in GPS challenging or denied environments such as natural or urban canyons, at least in a part of a mission. This dissertation addresses, 1) the development of intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification and 2) GPS impaired localization through a formal framework for trajectory-based flight navigation for unmanned aircraft systems (UAS) operating BVLOS in low-altitude urban airspace. Our AAUS sensor method utilizes monophonic sound event detection in which the sensor detects, records, and classifies each event utilizing supervised machine learning techniques [90]. We propose a simulated framework to enhance the performance of localization in GPS-denied environments. We do this by using a new representation of 3D geospatial data using planar features that efficiently capture the amount of information required for sensor-based GPS navigation in obstacle-rich environments. The results from this dissertation would impact both military and civilian areas of research with the ability to react to events and navigate in an urban environment.
Doctor of Philosophy
Emergency scenarios such as missing persons or catastrophic events in urban areas require first responders to gain situational awareness motivating researchers to investigate intelligent sensor frameworks that utilize drones for observation prompting questions such as: How can responders detect and classify acoustic anomalies using unattended sensors? and How do they remotely navigate in GPS-denied urban environments using drones to potentially investigate such an event? This dissertation addresses the first question through the development of intelligent WSN systems that can provide time-critical and precise, localized environmental information necessary for decision-making. At Virginia Tech, we have developed a static ground Acoustic Autonomous Unattended Sensor (AAUS) capable of machine learning for audio feature classification. The prior arts of intelligent AAUS and network architectures do not account for network failure, jamming capabilities, or remote scenarios in which cellular data wifi coverage are unavailable [78, 90]. Lacking a framework for such scenarios illuminates vulnerability in operational integrity for proposed solutions in homeland security applications. We address this through data ferrying, a communication method in which a mobile node, such as a drone, physically carries data as it moves through the environment to communicate with other sensor nodes on the ground. When examining the second question of navigation/investigation, concerns of safety arise in urban areas regarding drones due to GPS signal loss which is one of the first problems that can occur when a drone flies into a city (such as New York City). If this happens, potential crashes, injury and damage to property are imminent because the drone does not know where it is in space. In these GPS-denied situations traditional methods use point clouds (a set of data points in space (X,Y,Z) representing a 3D object [107]) constructed from laser radar scanners (often seen in a Microsoft Xbox Kinect sensor) to find itself. The main drawback from using methods such as these is the accumulation of error and computational complexity of large data-sets such as New York City. An advantage of cities is that they are largely flat; thus, if you can represent a building with a plane instead of 10,000 points, you can greatly reduce your data and improve algorithm performance. This dissertation addresses both the needs of an intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification as well as GPS-impaired localization through a formal framework for trajectory-based flight navigation for UAS operating BVLOS in low altitude urban and suburban environments.
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Olson, Jacob Moroni. "Collaborative UAV Planning, Mapping, and Exploration in GPS-Denied Environments." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8703.

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The use of multirotor UAVs to map GPS-degraded environments is useful for many purposes ranging from routine structural inspections to post-disaster exploration to search for survivors and evaluate structural integrity. Multirotor UAVs are able to reach many areas that humans and other robots cannot safely access. Because of their relatively short operational flight time compared to other robotic applications, using multiple UAVs to collaboratively map these environments can streamline the mapping process significantly. This research focuses on four primary areas regarding autonomous mapping and navigation with multiple UAVs in complex unknown or partially unknown GPS-denied environments: The first area is the high-level coverage path planning necessary to successfully map these environments with multiple agents. The second area is the lower-level reactive path planning that enables autonomous navigation through complex, unknown environments. Third, is the estimation framework that enables autonomous flight without the use of GPS or other global position sensors. Lastly, it focuses on the mapping framework to build a single dense 3D map of these environments with multiple agents flying simultaneously.
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Reis, Gregory M. "Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles in GPS-Denied Environments." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3736.

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Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective sampling requires a good understanding of vehicle localization relative to the sampled feature. Therefore, our work is motivated by the desire to enable intelligent data collection of complex dynamics and processes that occur in coastal ocean environments to further our understanding and prediction capabilities. The study originated from the need to localize and navigate aquatic robots in a GPS-denied environment and examine the role of the spatio-temporal dynamics of the ocean into the localization and navigation processes. The methods and techniques needed range from the data collection to the localization and navigation algorithms used on-board of the aquatic vehicles. The focus of this work is to develop algorithms for localization and navigation of AUVs in GPS-denied environments. We developed an Augmented terrain-based framework that incorporates physical science data, i.e., temperature, salinity, pH, etc., to enhance the topographic map that the vehicle uses to navigate. In this navigation scheme, the bathymetric data are combined with the physical science data to enrich the uniqueness of the underlying terrain map and increase the accuracy of underwater localization. Another technique developed in this work addresses the problem of tracking an underwater vehicle when the GPS signal suddenly becomes unavailable. The methods include the whitening of the data to reveal the true statistical distance between datapoints and also incorporates physical science data to enhance the topographic map. Simulations were performed at Lake Nighthorse, Colorado, USA, between April 25th and May 2nd 2018 and at Big Fisherman's Cove, Santa Catalina Island, California, USA, on July 13th and July 14th 2016. Different missions were executed on different environments (snow, rain and the presence of plumes). Results showed that these two methodologies for localization and tracking work for reference maps that had been recorded within a week and the accuracy on the average error in localization can be compared to the errors found when using GPS if the time in which the observations were taken are the same period of the day (morning, afternoon or night). The whitening of the data had positive results when compared to localizing without whitening.
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Book chapters on the topic "GPS-Denied navigation"

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Yang, Ka, Daji Qiao, and Wensheng Zhang. "Sensor-Aided Navigation in GPS-Denied Environments." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 345–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29222-4_25.

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Yakovlev, Konstantin, Vsevolod Khithov, Maxim Loginov, and Alexander Petrov. "Distributed Control and Navigation System for Quadrotor UAVs in GPS-Denied Environments." In Advances in Intelligent Systems and Computing, 49–56. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11310-4_5.

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Power, William, Martin Pavlovski, Daniel Saranovic, Ivan Stojkovic, and Zoran Obradovic. "Autonomous Navigation for Drone Swarms in GPS-Denied Environments Using Structured Learning." In IFIP Advances in Information and Communication Technology, 219–31. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49186-4_19.

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Prakash, Prafull, Chaitanya Murti, Saketha Nath, and Chiranjib Bhattacharyya. "Optimizing DNN Architectures for High Speed Autonomous Navigation in GPS Denied Environments on Edge Devices." In PRICAI 2019: Trends in Artificial Intelligence, 468–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29911-8_36.

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North, Eric, Jacques Georgy, Umar Iqbal, Mohammed Tarbochi, and Aboelmagd Noureldi. "Improved Inertial/Odometry/GPS Positioning of Wheeled Robots Even in GPS-Denied Environments." In Global Navigation Satellite Systems: Signal, Theory and Applications. InTech, 2012. http://dx.doi.org/10.5772/38824.

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Conference papers on the topic "GPS-Denied navigation"

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Richeson, Justin, and Darryll Pines. "GPS Denied Inertial Navigation Using Gravity Gradiometry." In AIAA Guidance, Navigation and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-6791.

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Soloviev, Andrey, and Chun Yang. "SAR/Inertial Integration for GPS-Denied Navigation." In 2016 International Technical Meeting of The Institute of Navigation. Institute of Navigation, 2016. http://dx.doi.org/10.33012/2016.13464.

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Leishman, Robert C., Timothy W. McLain, and Randal W. Beard. "Relative navigation approach for vision-based aerial GPS-denied navigation." In 2013 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2013. http://dx.doi.org/10.1109/icuas.2013.6564707.

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Flores-Abad, Angel, Noshin Habib, Diego Aponte-Roa, Albert Espinoza, and Josué Martinez-Martinez. "Unmanned Autonomous Aerial Navigation in GPS-Denied Environments." In The 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integration, And Alliances for A Sustainable Development” “Hemispheric Cooperation for Competitiveness and Prosperity on A Knowledge-Based Economy”. Latin American and Caribbean Consortium of Engineering Institutions, 2020. http://dx.doi.org/10.18687/laccei2020.1.1.349.

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Bachrach, Abraham, Anton de Winter, Ruijie He, Garrett Hemann, Samuel Prentice, and Nicholas Roy. "RANGE - robust autonomous navigation in GPS-denied environments." In 2010 IEEE International Conference on Robotics and Automation (ICRA 2010). IEEE, 2010. http://dx.doi.org/10.1109/robot.2010.5509990.

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Sharma, Rajnikant, and Clark Taylor. "Cooperative navigation of MAVs In GPS denied areas." In 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008). IEEE, 2008. http://dx.doi.org/10.1109/mfi.2008.4648041.

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Kleinert, Markus, and Sebastian Schleith. "Inertial aided monocular SLAM for GPS-denied navigation." In 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010). IEEE, 2010. http://dx.doi.org/10.1109/mfi.2010.5604453.

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Wang, Teng, Changyin Sun, and Guanqing Lao. "Aerial-DEM Geolocalization for GPS-Denied UAS Navigation." In 2018 Ninth International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2018. http://dx.doi.org/10.1109/icicip.2018.8606681.

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Balamurugan, G., J. Valarmathi, and V. P. S. Naidu. "Survey on UAV navigation in GPS denied environments." In 2016 International conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2016. http://dx.doi.org/10.1109/scopes.2016.7955787.

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Yang, Junho, Dushyant Rao, Soon-Jo Chung, and Seth Hutchinson. "Monocular Vision based Navigation in GPS-Denied Riverine Environments." In Infotech@Aerospace 2011. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-1403.

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Reports on the topic "GPS-Denied navigation"

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Claussen, Neil, Leonardo Le, Ryan Ashton, Kemberly Cespedes, Anirudh Patel, Langston Williams, Benjamin Miller, and Jason Searcy. Magnetic Navigation for GPS-Denied Airborne Applications. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1817974.

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