A long-baseline (LBL) underwater acoustic communication and localization system was developed for the Virginia Tech Underwater Glider (VTUG). Autonomous underwater vehicles, much like terrestrial and aerial robots require an effective positioning system, like GPS to perform a wide variety of guidance, navigation and control operations. Sea and freshwater attenuate electromagnetic waves (sea water is worse due to higher conductivity) within very few meters of striking the water surface. Since radio frequency communications are unavailable, many undersea systems use acoustic communications instead. Underwater acoustic communication is the technique of sending and receiving data below water. Underwater acoustic positioning is the technique of locating an underwater object. Among the various types of acoustic positioning systems, the LBL acoustic positioning method offers the highest accuracy for underwater vehicle navigation. A system consisting of three acoustic 'beacons which are placed on the surface of the water at known locations was developed. Using an acoustic modem to excite an acoustic transducer to send sound waves from an underwater glider, the range measurements to each of the beacons was calculated. These range measurements along with data from the attitude heading and reference system (AHRS) on board the glider were used to estimate the position of the underwater vehicle. Static and dynamic estimators were implemented. The system also allowed for underwater acoustic communication in the form of heartbeat messages from the glider, which were used to monitor the health of the vehicle. / Master of Science

This thesis paper presents a simultaneous 3D mapping and geolocation of road surface technique that combines local road surface mapping and global camera localization. The local road surface is generated by structure from motion (SFM) with multiple views and optimized by Bundle Adjustment (BA). A system is developed for the global reconstruction of 3D road surface. Using the system, the proposed technique globally reconstructs 3D road surface by estimating the global camera pose using the Adaptive Extended Kalman Filter (AEKF) and integrates it with local road surface reconstruction techniques. The proposed AEKF-based technique uses image shift as prior. And the camera pose was corrected with the sparse low-accuracy Global Positioning System (GPS) data and digital elevation map (DEM). The AEKF adaptively updates the covariance of uncertainties such that the estimation works well in environment with varying uncertainties. The image capturing system is designed with the camera frame rate being dynamically controlled by vehicle speed read from on-board diagnostics (OBD) for capturing continuous data and helping to remove the effects of moving vehicle shadow from the images with a Random Sample and Consensus (RANSAC) algorithm. The proposed technique is tested in both simulation and field experiment, and compared with similar previous work. The results show that the proposed technique achieves better accuracy than conventional Extended Kalman Filter (EKF) method and achieves smaller translation error than other similar other works. / Master of Science / This thesis paper presents a simultaneous three dimensional (3D) mapping and geolocation of road surface technique that combines local road surface mapping and global camera localization. The local road surface is reconstructed by image processing technique with optimization. And the designed system globally reconstructs 3D road surface by estimating the global camera poses using the proposed Adaptive Extended Kalman Filter (AEKF)-based method and integrates with local road surface reconstructing technique. The camera pose uses image shift as prior, and is corrected with the sparse low-accuracy Global Positioning System (GPS) data and digital elevation map (DEM). The final 3D road surface map with geolocation is generated by combining both local road surface mapping and global localization results. The proposed technique is tested in both simulation and field experiment, and compared with similar previous work. The results show that the proposed technique achieves better accuracy than conventional Extended Kalman Filter (EKF) method and achieves smaller translation error than other similar other works.

This thesis aims to give statistical methods applicating to video background subtraction. In the thesis, I will give out the problem introduction and analyze the problem with different statistical methods including histogram statistics, and Gaussian Mixture models methods. To study further, I will give out the time series analysis to make a more significant way: To build up the time series analysis way of video background subtraction with the Kalman filter and give out the predictions and evaluations.

As the mission field for autonomous vehicles expands into a larger variety of territories, the development of autonomous surface vehicles (ASVs) becomes increasingly important. ASVs have the potential to travel for long periods of time in areas that cannot be reached by aerial, ground, or underwater autonomous vehicles. ASVs are useful for a variety of missions, including bathymetric mapping, communication with other autonomous vehicles, military reconnaissance and surveillance, and environmental data collecting. Critical to an ASV's ability to maneuver without human intervention is its ability to detect obstacles, including the shoreline. Prior topological knowledge of the environment is not always available or, in dynamic environments, reliable. While many existing obstacle detection systems can only detect 3D obstacles at close range via a laser or radar signal, vision systems have the potential to detect obstacles both near and far, including "flat" obstacles such as the shoreline. The challenge lies in processing the images acquired by the vision system and extracting useful information. While this thesis does not address the issue of processing the images to locate the pixel positions of the obstacles, we assume that we have these processed images available. We present an algorithm that takes these processed images and, by incorporating the kinematic model of the ASV, maps the pixel locations of the obstacles into a global coordinate system. An Extended Kalman Filter is used to localize the ASV and the surrounding obstacles. / Master of Science

Yes / In this paper, a novel control algorithm is presented to enhance the performance of the tracking property for a class of nonlinear and dynamic stochastic systems subjected to non-Gaussian noises. Although the existing standard PI controller can be used to obtain the basic tracking of the systems, the desired tracking performance of the stochastic systems is difficult to achieve due to the random noises. To improve the tracking performance, an enhanced performance loop is constructed using the EKF-based state estimates without changing the existing closed loop with a PI controller. Meanwhile, the gain of the enhanced performance loop can be obtained based upon the entropy optimization of the tracking error. In addition, the stability of the closed loop system is analyzed in the mean-square sense. The simulation results are given to illustrate the effectiveness of the proposed control algorithm. / This work was supported in part by the PNNL Control of Complex Systems Initiative and in part by the National Natural Science Foundation of China under Grants 61621004,61573022 and 61333007.

High-accuracy localization is vital for many applications and is a fundamental prerequisite for enabling autonomous missions. Modern navigation systems often rely heavily on Global Navigation Satellite Systems (GNSS) for achieving high localization accuracy over extended periods of time, which has necessitated alternative localization methods that can be used in GNSS-disturbed environments. One popular alternative that has emerged is Collaborative Localization (CL), which is a method where agents of a swarm combine knowledge of their own state with relative measurements of other agents to achieve a localization accuracy that is better than what a single agent can achieve on its own. Performing this in a decentralized manner introduces the challenge of how to account for unknown inter-agent correlations, which typically leads to the need for using conservative fusion methods such as Covariance Intersection (CI) to preserve consistency. Many existing CL algorithms that utilize CI assume agents to have perception systems capable of identifying the relative position of other swarm members. These algorithms do therefore not work in systems where, e.g., agents are only capable of measuring range to each other. Other CI algorithms that support more generic measurement models can require large amounts of data to be exchanged when agents communicate, which could lead to issues in bandwidth-limited systems. This thesis develops a consistent decentralized collaborative localization algorithm based on CI that supports range-only measurements between agents and requires a communication effort that is constant in the number of agents in the swarm. The algorithm, referred to as the PSCI algorithm, was found to maintain satisfactory performance in various scenarios but exhibits slightly increased sensitivity to the measurement geometry compared to an already existing, more communication-heavy, CI-based algorithm. Moreover, the thesis highlights the impact of linearization errors in range-only CL systems and shows that performing CI-fusion before the range-observation measurement update, with a clever choice of CI cost function, can reduce linearization errors for the PSCI algorithm. A comparison between the PSCI algorithm and an already existing algorithm, referred to as the Cross-Covariance Approximation (CCA) algorithm, has further been conducted through a sensitivity analysis with respect to communication rate and the number of GNSS agents. The simulation results indicate that the PSCI algorithm exhibits diminishing improvement in Root Mean Square Error (RMSE) with increased communication rates, while the RMSE of the CCA algorithm reaches a local minimum, subsequently showing overconfidence with higher rates. Lastly, evaluation under a varying number of GNSS agents indicates that cooperative benefits for the PSCI filter are marginal when uncertainty levels are uniform across agents. However, the PSCI algorithm demonstrates superior performance improvements with an increased number of GNSS agents compared to the CCA algorithm, attributed to the overconfidence of the latter.

Lack of drivers knowledge about the abrupt changes in pavement friction and poor performance of the vehicle stability, traction and ABS controllers on the low friction surfaces are the most important factors affecting car crashes. Due to its direct relation to vehicle stability, accurate estimation of tire-road characteristics is of interest to all vehicle and tire companies. Many studies have been conducted in this field and researchers have used different tools and have proposed different algorithms. One such concept is the Intelligent Tire. The application of intelligent tire in tire-road characterization is investigated in this study. Three different test setups were used in this research to study the application of the intelligent tires to improve mobility; first, a wheeled ground robot was designed and built. A Fuzzy Logic algorithm was developed and validated using the robot for classifying different road surfaces such as asphalt, concrete, grass, and soil. The second test setup is a portable tire testing trailer, which is a quarter car test rig installed in a trailer and towed by a truck. The trailer was equipped with different sensors including an accelerometer attached to the center of the tire inner-liner. Using the trailer, acceleration data was collected under varying conditions and a Neural Network (NN) algorithm was developed and trained to estimate the contact patch length, effective tire rolling radius and tire normal load. The third test setup developed for this study was an instrumented Volkswagen Jetta. Different sensors were installed to measure vehicle dynamic response. Additionally, one front and one rear tire was instrumented with an accelerometer attached to their inner-liner. Two intelligent tire based algorithms, a tire pressure estimation algorithm and a road condition monitoring algorithm, were developed and trained using the experimental data from the instrumented VW Jetta. The two-step pressure monitoring algorithm uses the acceleration signal from the intelligent tire and the wheel angular velocity to monitor the tire pressure. Also, wet and dry surfaces are distinguished using the acceleration signal from the intelligent tire and the wheel angular velocity through the surface monitoring algorithm. Some of the model based tire-road friction estimation algorithms, which are widely used for tire-road friction estimation, were also introduced in this study and the performance of each algorithm was evaluated in high slip and low slip maneuvers. Finally a new friction estimation algorithm was developed, which is a combination of experiment based and vehicle dynamic based approaches and its performance was also investigated. / PHD / Lack of driver’s knowledge about the abrupt changes in pavement friction and poor performance of the vehicle stability, traction and ABS controllers on the low friction surfaces are the most important factors affecting car crashes. Due to its direct relation to vehicle stability, accurate estimation of tire-road characteristics is of interest to all vehicle and tire companies. Many studies have been conducted in this field and researchers have used different tools and have proposed different algorithms. One such concept is the Intelligent Tire. The application of intelligent tire in tire-road characterization is investigated in this study. Five main algorithms are developed in this study. First a fuzzy-logic terrain classification algorithm is developed for the small wheeled ground robot that classifies all different surfaces into four known categories; asphalt, concrete, sand and grass. A six-wheel grand robot was designed and built for this study and instrumented with intelligent tire, a tri-axial accelerometer embedded to the tire inner-liner, and other appropriate sensors. The input of the terrain classification algorithm are the intelligent tire signal, the slip ratio at the beginning of the motion and the wheel speed. The second algorithm is an intelligent tire based algorithm to estimate the tire normal load. A portable tire testing trailer, which is a quarter car test rig attached to the back of the trailer and towed by a truck was used for this part of the project. The trailer test setup was instrumented with different sensors and the tire normal load was controlled through a pneumatic force transducer and an air-spring system. A Neural Network algorithm was then trained that estimates the tire normal load using intelligent tire signal, the tire pressure and the wheel speed. The third and fourth algorithm are intelligent tire based algorithms to monitor the tire pressure and the road surface condition respectively. An instrumented vehicle, which was a Volkswagen Jetta 2003, was prepared and used for this part of the project. The inputs of these algorithms were the intelligent tire signal and the wheel speed and the outputs were the tire pressure condition and road surface condition (dry/ wet) respectively. The last algorithm is a new friction estimation algorithm, which is a combination of experiment based (intelligent tire) and vehicle dynamic based approaches. The algorithm is validated with the experimental data collected using the trailer test setup.

This thesis presents a novel self-powered infrastructural traffic monitoring approach that estimates traffic information by combining three detection techniques. The traffic information can be obtained from the presented approach includes vehicle counts, speed estimation and vehicle classification based on size. Two categories of sensors are used including IR Lidar and IR camera. With the two sensors, three detection techniques are used: Time of Flight (ToF) based, vision based and Laser spot flow based. Each technique outputs observations about vehicle location at different time step. By fusing the three observations in the framework of Kalman filter, vehicle location is estimated, based on which other concerned traffic information including vehicle counts, speed and class is obtained. In this process, high reliability is achieved by combing the strength of each techniques. To achieve self-powering, a dynamic power management strategy is developed to reduce system total energy cost and optimize power supply in traffic monitoring based on traffic pattern recognition. The power manager attempts to adjust the power supply by reconfiguring system setup according to its estimation about current traffic condition. A system prototype has been built and multiple field experiments and simulations were conducted to demonstrate traffic monitoring accuracy and power reduction efficacy. / Master of Science / This thesis presents a novel traffic monitoring system that does not require external power source. The traffic monitoring system is able to collect traffic variables including count, speed and vehicle types. The system uses two types of sensors and implements three different measuring techniques. By combining the results from the three techniques, higher accuracy and reliability is achieved. A power management component is also developed for the system to save energy usage. Based on current or predicted system power state, the power manager selectively deactivates or turns off certain part of the system to reduce power consumption. A system prototype has been built and multiple field experiments and simulations were conducted to demonstrate traffic monitoring accuracy and power reduction efficacy. The experiments have shown that the system achieves high accuracy in every variable estimation and large portion of energy is saved by adopting power management.

The introduction of synchrophasor technology to the realm of power systems has presented a myriad of novel approaches to age-old problems. In this thesis, the questions of instrument transformer calibration and transmission line parameter estimation have been examined. With synchrophasors offering real-time data for analysis, a solution to each individual problem seems feasible. A quandary however arises due to the fact that calibration methods depend on accurate knowledge of line parameters, and estimation of these parameters depend on calibrated measurements. Traditional methods of determining the parameters may not be the most accurate due to a variety of fluctuations possible on the system, which is why real-time estimation could prove beneficial. This work analyzes each problem and a feasible solution and proposes a method to achieve transducer calibration as well as parameter estimation together, while employing synchronized phasor measurements. / Master of Science / Synchrophasor Measurement Units (PMUs) provide the magnitude and angle of the quantity being measured, along with GPS time synchronization. Voltage, current and frequency data can be sent to a central control centre at the rate of 30 or 60 times per second. With a sufficient number of PMUs deployed on the electric grid, system operators now have available essentially a ‘snapshot’ of the system, which aids to monitor the grid, predict abnormal conditions as well as quickly identify troubled areas and accordingly take remedial actions. In order to facilitate the safe and reliable operation of the electric power grid, there are numerous devices that monitor quantities such as voltage, current, frequency etc. Most of these devices however are incapable of handling high levels of voltage and currents that are common to the power network. Instrument transformers (IT) are used to step down the measured quantities to much lower magnitudes that can then be analyzed by downstream devices. Each instrument transformer has a specified transformation ratio. For example, a voltage transformer with a transformation ratio of 100:1 would step down 500V to 5V. With time, these ITs may experience wear which might lead to degradation of its ratio, which would in turn be detrimental for applications relying on accurate measurements. Therefore routine calibration of ITs is desired. Traditional methods of calibration however involve taking the device out of service temporarily. As one can imagine, this is cost, labour and time intensive. With the availability of PMU data, it is now possible to perform calibration of these devices without having to take the device offline, provided we have accurate knowledge of the transmission line parameters. The parameters of a transmission line include the resistance, reactance and susceptance of the line and depend on the type of conductor used, the length and ambient temperature. Therefore seasonal and daily temperature variations can cause changes in the line parameters. With PMU data, we now have the capability to estimate these parameters, so that we have the most accurate idea of the present parameters. However for this, calibrated voltages and currents are required. Herein we face a quandary: we need to calibrate the ITs, which require accurate line parameters, but to estimate the current line parameters we need calibrated voltages and currents. This is the problem this thesis addresses. First, methods to perform both tasks, i.e. instrument transformer calibration as well as line parameter estimation using PMU measurements are analyzed. Finally an iterative method is proposed that can be applied to solve both problems together.

This thesis presents the development and implementation of a software framework for estimating the position of a drone during flight. This framework is based on an algorithm known as the Unscented Kalman Filter (UKF), a recursive method of estimating the state of a highly nonlinear system, such as an aircraft. In this thesis, we present a UKF formulation specially designed for a quadcopter carrying an Inertial Measurement Unit (IMU) and a downward-facing camera. The UKF fuses data from each of these sensors to track the position of the quadcopter over time. This work supports a number of similar efforts in the robotics and aerospace communities to navigate in GPS-denied environments with minimal hardware and minimal computational complexity. The software framework explored in this thesis provides a means for roboticists to easily implement similar UKF-based state estimators for a wide variety of systems, including surface vessels, undersea vehicles, and automobiles. We test the system's effectiveness by comparing its position estimates to those of a commercial motion capture system and then discuss possible applications. / Master of Science / This thesis presents the development and implementation of a software framework for estimating the position of a drone during flight. This framework is based on an algorithm known as the Unscented Kalman Filter (UKF), a recursive method of estimating the state of a highly nonlinear system, such as an aircraft. In this thesis, we present a UKF formulation specially designed for a quadcopter carrying an Inertial Measurement Unit (IMU) and a downwardfacing camera. The UKF fuses data from each of these sensors to track the position of the quadcopter over time. This work supports a number of similar efforts in the robotics and aerospace communities to navigate in GPS-denied environments with minimal hardware and minimal computational complexity. The software framework explored in this thesis provides a means for roboticists to easily implement similar UKF-based state estimators for a wide variety of systems, including surface vessels, undersea vehicles, and automobiles. We test the system’s effectiveness by comparing its position estimates to those of a commercial motion capture system and then discuss possible applications.