Master of Science (MS), Ohio University, 2018, Electrical Engineering & Computer Science (Engineering and Technology)

Small Unmanned Aircraft Systems (sUAS) are becoming very popular for solving a multitude of problems. As sUAS solutions are applied to more often, it is evident that multiple cooperative sUAS can be beneficial to certain tasks (surveillance, inspection, mapping). Unfortunately, operations involving multiple sUAS are inherently complex, requiring navigation solutions that are very accurate both in a relative and absolute sense for every member of the swarm. This thesis presents a method to use ultra-wideband (UWB) range radios to increase the relative position accuracy (and as a byproduct, absolute position accuracy) of the members of a swarm. A range radio system is also developed and analyzed, allowing simulations for testing this method. Finally, real flight data has been collected using multiple custom-built sUAS platforms and post-processed, allowing the filter to be analyzed using real world data.

Committee: Maarten Uijt de Haag (Advisor); Michael Braasch (Committee Member); Frank Van Graas (Committee Member); Geoffrey Dabelko (Committee Member) Subjects: Electrical Engineering

Master of Science, University of Toledo, 2016, Electrical Engineering

Navigation is one of the most crucial tasks of an autonomous unmanned aerial vehicle (UAV). The ability of a UAV to navigate and fly precisely determines its utility and performance. The current navigation systems rely heavily on the Global Positioning System (GPS) and are prone to error because of GPS signal outages. However, advancements in onboard processing power have enabled inertial navigation algorithms to perform well during short GPS outages. This thesis proposes intelligent algorithms to provide the navigation capability during long GPS outages and through GPS-denied environments using optical flow and inertial sensors. Traditional optical flow measurement uses block matching for motion vector calculation that makes the measurement task computationally expensive and slow. This thesis proposes the application of artificial bee colony based block matching for faster optical flow measurement. To make the fusion of optical flow data with inertial sensors efficient, a modified form of Extended Kalman Filter (EKF) is employed. The modifications make the filter less noisy by dynamically assigning weights to multiple sensors. A high accuracy of approximately 95 percent for non-GPS navigation during experiments is achieved.

Committee: Hong Wang (Committee Chair); Devabhaktuni Vijay (Committee Co-Chair); Niamat Mohammed (Committee Member); Javaid Ahmad (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (PhD), Ohio University, 2015, Electrical Engineering (Engineering and Technology)

As the potential use of autonomous unmanned aerial vehicles (UAVs) has become more prevalent in both the public and private sectors, the need for a reliable three-dimensional (3D) positioning, navigation, and mapping (PNM) capability will be required to enable operation of these platforms in challenging environments where the Global Positioning System (GPS) may not necessarily be available. Especially, when the platform's operational scenario involves motion through different environments like outdoor open-sky, outdoor under foliage, outdoor-urban and indoor, and includes transitions between these environments, there may not be one particular method to solve the PNM problem. In this dissertation we are not solving the PNM problem for every possible environment, but select a couple of dissimilar sensor technologies to design and implement an integrated navigation and mapping method that can support reliable operation in an outdoor and structured indoor environment. The integrated navigation and mapping design is based on a Global Positioning System (GPS) receiver, an Inertial Measurement Unit (IMU), a monocular digital camera, and three short to medium range laser scanners. To evaluate the developed algorithms a hexacopter was built, equipped with the above sensors, and both hand-carried and flown through the target environments. This dissertation will show that dm-level relative positioning accuracies can be achieved for operations traversing a building, and that when segments are included where GPS is available, the platform's trajectory and map will be globally anchored with m-level accuracy.

Committee: Maarten Uijt de Haag (Advisor); Frank van Graas (Committee Member); Wouter Pelgrum (Committee Member); Douglas Lawrence (Committee Member) Subjects: Electrical Engineering

Master of Science (MS), Ohio University, 2011, Electrical Engineering (Engineering and Technology)

As technology advances in the area of mobile vehicles, the need for precise reliable navigation increases with it. Whether the vehicle is an unmanned aerial vehicle (UAV), a manned aerial vehicle (MAV), or an intelligent ground vehicle (IGV), there is a constant need for precise navigation capabilities. This need spurred the invention and development of many navigation systems including the most useful system to date, the Global Positioning System (GPS). GPS is a powerful tool that can reliably give meter level accuracy on a world-wide scale. With this capability, GPS is the answer to a significant number of navigation problems, but it is not the answer to them all. Since GPS relies on exterior signals from orbiting satellites, tasks such as underground navigation and navigation in dense foliage can be difficult due to signal strength attenuation as it passes through these media. GPS is also very susceptible to multipath at the receiver. If the receiver is operating in a building or in a heavy urban environment, the multipath created can degrade the received signal to the point of losing its true capabilities. Lastly, GPS capabilities are ideal for military applications. However, any system that uses exterior signals for military applications must deal with the possibility of interference, jamming, or even an attack on the system in a wartime scenario. Although, the list of scenarios in which GPS is not a viable answer is small, it is important that those scenarios be addressed. One viable possibility is developing a new system that complements GPS by having functionality in scenarios in which GPS is a poor option or not an option at all. This thesis describes and discusses one such possibility that could complement GPS. The proposed system is a self contained system that would use multiple sensors and the environment around them for navigation. This method would integrate three-dimensional (3D) point cloud data, two-dimensional (2D) gray-level (intensity) data, 2D (open full item for complete abstract)

Committee: Maarten Uijt de Haag (Advisor); Frank van Graas (Committee Member); Wouter Pelgrum (Committee Member); Vardges Melkonian (Committee Member) Subjects: Electrical Engineering

Master of Science, The Ohio State University, 1965, Graduate School

Committee: Not Provided (Other) Subjects:

Doctor of Philosophy (PhD), Ohio University, 2022, Electrical Engineering & Computer Science (Engineering and Technology)

GNSS-aided inertial navigation systems (INS) used in civil aviation must undergo robust performance testing to ensure proper operation even in worst-case conditions. Gravity mis-modeling has the potential to cause significant impact to the position, velocity, and time (PVT) solution if gravity deflections are not properly accounted for within the navigation system. The root cause of the error lies in Einstein's equivalence principle and results in accelerometers not being able to distinguish between acceleration and the reaction to gravity. The accelerometer measurements are compensated by estimates of gravity from models or databases; however, the gravity estimates contain errors that appear as erroneous acceleration measurements that lead to position errors. First the impact of gravity mis-modeling on a GNSS-aided INS is examined. The gravity profiles for several RNP and RNP-AR approaches are presented and deflection of the vertical (DOV), the angle between the true and estimated gravity vectors, is examined. As shown in this work, every 1 arc-second of unaccounted DOV results in a 5 μg horizontal acceleration bias error. The impact of a large 80 arc second DOV gradient, equivalent to a 400 μg accelerometer bias, on a GNSS-aided INS while coasting without GNSS is studied next. For the case of a strategic IMU with an accel bias of 0.4 μg, it is demonstrated that during a 10-minute coasting period that the position error can grow up to 185 meters, which is approximately the same as a RNP 0.1 lane width. Next this dissertation proposes gravity error simulation architectures to be used by navigation system manufactures for testing the gravity compensation within the INS in support of TSO-authorization. Time-series system identification is used to identify auto-regressive moving average (ARMA) models that are used to generate time-series data representative of the gravity errors. A high-fidelity simulation architecture consisting of deterministic and stochasti (open full item for complete abstract)

Committee: Chris Bartone (Advisor); Michael Braasch (Committee Member); Julie Roche (Committee Member); Sabrina Ugazio (Committee Member); Daniel Phillips (Committee Member); Frank van Graas (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2022, Electrical and Computer Engineering

Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including Global Navigation Satellite System (GNSS)- denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage. The INS unit incorporates an Inertial Measurement Unit (IMU) to process the linear acceleration and angular velocity data to obtain orientation, position and velocity information using mechanization equations. In this work, we developed a novel deep learning-based methodology, using Convolutional Neural Networks (CNN) to reduce errors from MEMS IMU sensors. We developed a methodology of using CNN algorithms that can learn from the responses of a particular inertial sensor while subject to inherent noise errors and provide a near real-time error correction. We implemented a time-division method to divide the IMU output data into small step sizes. By using this method, we make the IMU outputs fit the input format of the CNN. We optimized the CNN algorithm for higher performance and lower complexity that would allow its implementation on ultra-low power hardware such as microcontrollers. We examined the performance of our CNN algorithm under various situations with IMUs of various performance grades, IMUs of the same type but different manufactured batch, and controlled, fixed and un-controlled vehicle motion paths.

Committee: Vamsy Chodavarapu (Committee Chair); Manish Kumar (Committee Member); Guru Subramanyam (Committee Member); Tarek Taha (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2020, Electrical and Computer Engineering

Inertial sensors became wearable with the advances in sensing and computing technologies in the last two decades. Captured motion data can be used to build a pedestrian inertial navigation system (INS); however, time-variant bias and noise characteristics of low-cost sensors cause severe errors in positioning. To overcome the quickly growing errors of so-called dead-reckoning (DR) solution, this research adopts a pedestrian INS based on a Kalman Filter (KF) with zero-velocity update (ZUPT) aid. Despite accurate traveled distance estimates, obtained trajectories diverge from actual paths because of the heading estimation errors. In the absence of external corrections (e.g., GPS, UWB), map information is commonly employed to eliminate position drift; therefore, INS solution is fed into a higher level map-matching filter for further corrections. Unlike common Particle Filter (PF) map-matching, map constraints are implicitly modeled by generating rasterized maps that function as a constant spatial prior in the designed filter, which makes the Bayesian estimation cycle non-recursive. Eventually, proposed map-matching algorithm does not require computationally expensive Monte Carlo simulation and wall crossing check steps of PF. Second major usage of the rasterized maps is to provide probabilities for a self-initialization method referred to as the Multiple Hypothesis Testing (MHT). Extracted scores update hypothesis probabilities in a dynamic manner and the hypothesis with the maximum probability gives the correct initial position and heading. Realistic pedestrian walks include room visits where map-matching is de-activated (as rasterized maps do not model the rooms) and consequently excessive positioning drifts occur. Another MHT approach exploiting the introduced maps further is designed to re-activate the map filter at strides that the pedestrian returns the hallways after room traversals. Subsequently, trajectories left behind inside the rooms are heuristically adjus (open full item for complete abstract)

Committee: Alper Yilmaz Prof (Advisor); Keith Redmill Prof (Committee Member); Charles Toth Prof (Committee Member); Janet Best Prof (Other) Subjects: Electrical Engineering; Engineering

Master of Science in Engineering, University of Akron, 2019, Mechanical Engineering

This thesis illustrates the application of the Extended Kalman filter for online estimation of attitude of a body. The accuracy of controlled attitude largely depends on the performance of the estimation algorithm. In this thesis, the extended Kalman filter (EKF) algorithm consisting of quaternion based state representation is used. The EKF algorithm utilizes gyroscope reading for priori estimation and measurements reading from the accelerometer and the magnetometer to correct the states. In simple terms, the extended Filter is used as the estimation tool by fusing the data from the gyroscope, the accelerometer and the magnetometer. A device that combines the gyroscope, accelerometer and magnetometer is called inertial measurement unit (IMU). The non- accurate scaling, sensor misalignment and non-zero biases of IMU devices are eliminated by proper calibration. The sensors utilized in the estimation have noise and biases which results in propagation of error in time. The noise and biases should be eliminated to get the accurate estimates. In this work, the EKF algorithm with some modification in state equation and in the Kalman filter gain is implemented for both the steady state and the body acceleration conditions. The estimation of the modified EKF is compared with the estimation technique used by VECTORNAV, a well-known commercial IMU. The modified EKF performed well compared to VECTORNAV in steady state condition. However, under body acceleration, the modified EKF did not perform as well as what VECTORNAV did. The attitude estimation technique discussed in this thesis is less expensive and easy compared to those used in missile and aircraft guidance. The algorithm discussed in this thesis can be well implemented in the navigation of robots and drones for home applications.

Committee: Celal Batur (Advisor); Ajay Mahajan (Committee Member); Siamak Farhad (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 1966, Graduate School

Committee: Not Provided (Other) Subjects: Education

Doctor of Philosophy, The Ohio State University, 2015, Aero/Astro Engineering

This dissertation provides a flight-testing framework for assessing the performance of fixed-wing, small-scale unmanned aerial systems (sUAS) by leveraging sub-system models of components unique to these vehicles. The development of the sub-system models, and their links to broader impacts on sUAS performance, is the key contribution of this work. The sub-system modeling and analysis focuses on the vehicle's propulsion, navigation and guidance, and airframe components. Quantification of the uncertainty in the vehicle's power available and control states is essential for assessing the validity of both the methods and results obtained from flight-tests. Therefore, detailed propulsion and navigation system analyses are presented to validate the flight testing methodology. Propulsion system analysis required the development of an analytic model of the propeller in order to predict the power available over a range of flight conditions. The model is based on the blade element momentum (BEM) method. Additional corrections are added to the basic model in order to capture the Reynolds-dependent scale effects unique to sUAS. The model was experimentally validated using a ground based testing apparatus. The BEM predictions and experimental analysis allow for a parameterized model relating the electrical power, measurable during flight, to the power available required for vehicle performance analysis. Navigation system details are presented with a specific focus on the sensors used for state estimation, and the resulting uncertainty in vehicle state. Uncertainty quantification is provided by detailed calibration techniques validated using quasi-static and hardware-in-the-loop (HIL) ground based testing. The HIL methods introduced use a soft real-time flight simulator to provide inertial quality data for assessing overall system performance. Using this tool, the uncertainty in vehicle state estimation based on a range of sensors, and vehicle operational environments is pre (open full item for complete abstract)

Committee: James W. Gregory (Advisor); Charles Toth (Committee Member); Cliff Whitfield (Committee Member); Jeffery P. Bons (Committee Member) Subjects: Aerospace Engineering

Doctor of Philosophy (PhD), Ohio University, 1999, Electrical Engineering & Computer Science (Engineering and Technology)

Low-cost Inertial Navigation Systems (INS) technology has evolved rapidly over the last decade with the development of less-expensive and higher-accuracy inertial measurement units (IMU). The development in the field of differential GPS (DGPS) has also matured over the past decade to provide reliable centimeter level accuracy in real-time. This dissertation provides a detailed study of the feasibility of using a low-cost IMU with accurate DGPS to achieve higher-accuracy, reliability, and continuity of the position solution. Detailed INS equations are provided as well as the hardware integration of a low-cost IMU and a centimeter-level DGPS system. The integrated system was dynamically tested in a van and a DC-3 research aircraft. In the absence of DGPS updates for a period of time of 10 seconds, IMU-derived positions diverged by 5-10 meters for the van tests and by tens of meters for flight tests. Noise on the IMU-derived position between successive, one-second DGPS position updates was observed to be on the order of a few millimeters for the van tests and one centimeters for the flight tests.

Committee: Frank van Graas (Advisor) Subjects:

Doctor of Philosophy (PhD), Ohio University, 1999, Electrical Engineering & Computer Science (Engineering and Technology)

This dissertation discusses integration architectures using digital terrain elevation data (DTED) with an inertial navigation system (INS), a global positioning system (GPS) and a radar altimeter. Two integration architectures are considered: DTED with INS, GPS and radar altimeter for aircraft vertical accuracy improvement during the final approach; and DTED with kinematic GPS (KGPS) and a radar altimeter for enhanced aircraft flight safety. Error models were generated and verified with flight-test data. High-fidelity simulation was used to investigate vertical accuracy improvement. Improvement was found to be 1.2 meters, a reduction of 28.6% in the vertical error. Flight testing was performed to assess the feasibility of enhanced flight safety. Reasons for enhanced flight safety are twofold: 1) the ad-hoc integration of terrain elevation data into the cockpit conceivably may create scenarios which lead to accidents because the cockpit display is quite realistic, and 2) reduction of controlled flight into terrain (CFIT). The radar altimeter is the principle sensor used to compare navigation outputs with publicly available DTED. Results show that it is feasible to define an operationally useful probability of agreement,- P a, among KGPS, DTED and the radar altimeter, by using a mean-square-difference test statistic. This probability of agreement can be used to warn the pilot if the terrain depiction does not agree with the navigation solution provided by KGPS, thus enhancing flight safety.

Committee: Frank van Graas (Advisor) Subjects:

Doctor of Philosophy (PhD), Ohio University, 2002, Electrical Engineering & Computer Science (Engineering and Technology)

A new approach, which enhances Inertial Navigation System (INS) accuracy by modernizing the INS algorithmic part rather than improving the inertial sensors, is investigated. This dissertation proposes block-processing development of the INS algorithmic part in the frequency domain to improve the INS accuracy performance. Considering current demands for inertial sensor miniaturization and cost decrease, and operation of the INS as a part of integrated Global Positioning System/INS systems, the block-processing approach is applied to improve short-term accuracy characteristics of the strapdown, low-grade INS. A genetic frequency-domain INS computation starts with the reconstruction of continuous-time signals from blocks of input discrete samples by implementing the Fourier Transform (FT) compensated for boundary discontinuities. Next, analytical transformation of FT spectrums of reconstructed input signals into spectrums of navigation outputs is carried out in the frequency domain. The genetic frequency-domain computation is utilized to develop frequency-domain attitude determination, coordinate transformation, integration, and INS calibration procedures. Test results presented verify that the frequency-domain approach shows a significant improvement over conventional, discrete-time methods in the ability to reconstruct the sculling and coning motion and subsequent reduction in sculling and commutation errors. The error reduction demonstrated is mostly critical when frequencies of oscillatory motion components are comparable to half the sampling frequency of input inertial measurements. An order of magnitude reduction in the calibration methodical error is demonstrated by implementing frequency-domain techniques for the calibration of the strapdown, low-grade INS. In addition, frequency-domain development of the INS calibration procedure improves observability of calibrated parameters and, as a result, compensates for the unbounded growth of degradation of inertial ac (open full item for complete abstract)

Committee: Frank van Graas (Advisor) Subjects:

Master of Science (MS), Ohio University, 1998, Electrical Engineering & Computer Science (Engineering and Technology)

Integration of differential global positioning system and an inertial navigation system for aircraft surface movement guidance

Committee: Frank van Graas (Advisor) Subjects:

Master of Science (MS), Ohio University, 1998, Electrical Engineering & Computer Science (Engineering and Technology)

An inertial measurement unit interface and processing system synchronized to global positioning system time

Committee: Frank van Graas (Advisor) Subjects:

Master of Science (MS), Ohio University, 2003, Electrical Engineering & Computer Science (Engineering and Technology)

Integrated Global Positioning System and inertial navigation system integrity monitor performance.

Committee: Frank van Graas (Advisor) Subjects:

Master of Science (MS), Ohio University, 2000, Electrical Engineering & Computer Science (Engineering and Technology)

An investigation of integrated global positioning system and inertial navigation system fault detection

Committee: Michael Braasch (Advisor) Subjects:

Master of Science (MS), Ohio University, 2000, Electrical Engineering & Computer Science (Engineering and Technology)

Integration of local area augumentation system and inertial navigation system for aircraft surface movement guidance

Committee: Frank van Graas (Advisor) Subjects:

Master of Computer Science, Miami University, 2008, Computer Science and Systems Analysis

A mobile robot must know its position in order to operate autonomously. The process of determining the robot's position from sensor data is termed robot localization. IMU and RF are a few of the many different types of sensors that can be used for navigation and localization purposes. When used independently, these sensors can achieve good accuracy when operating in certain conditions. By merging the results from multiple sensors, the accuracy over a wider range of conditions can be obtained. This work proposes a technique of merging heterogeneous information from inertial and RF sensors. Since sensors have errors associated with their readings, the robot's state will be represented using a probability distribution function (PDF). At each time step, this PDF will be updated based on the RF readings and then updated again based on the IMU readings. Better localization accuracy is obtained by using the RF and inertial sensors together.

Committee: Michael Zmuda PhD (Advisor); Jade Morton PhD (Committee Member); Valerie Cross PhD (Committee Member) Subjects: Artificial Intelligence; Computer Science; Robots