The goal of this thesis is to develop a test platform for measuring surface effect ship (SES) response to wave loads. The platform is designed and built incorporating a self-propelled vehicle with data acquisition and navigation capabilities. Theoretical analysis is performed, various hardware and electronic parts are designed and built and software applications developed. Wave tank experiments are conducted for test platform evaluation and determination of vehicle response to a range of wave conditions. Furthermore, a three-dimensional model of the AIRCAT scale model SES is created. The theoretical analysis shows that the scale effects in some cases are great, so resonance phenomena cannot be observed. The experimental results clearly show that the heave, pitch and aircushion excess pressure fluctuations increase as the air-blower input level increases. The bow skirt arrangement needs improvements and further experimentation is necessary in order to draw conclusions about the wave loads applied on the skirt. / by Nicholas Kouvaras. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

Lam, Kin Kwok. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 99-103). / Abstracts in English and Chinese. / Abstract --- p.ii / 摘要 --- p.iii / Acknowledgements --- p.iv / Table of Contents --- p.v / List of Figures --- p.viii / List of Tables --- p.xi / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Intrinsic Problem of Today's Pose Estimation Systems --- p.1 / Chapter 1.2 --- Multi-sensors Data Fusion --- p.2 / Chapter 1.3 --- Objectives and Contributions --- p.3 / Chapter 1.4 --- Organization of the dissertation --- p.4 / Chapter Chapter 2 --- Architecture of Sensing System --- p.5 / Chapter 2.1 --- Hardware for Pose Estimation System --- p.5 / Chapter 2.2 --- Software for Pose Estimation System --- p.6 / Chapter Chapter 3 --- Inertial Measurement System --- p.7 / Chapter 3.1 --- Basic knowledge of Inertial Measurement System --- p.7 / Chapter 3.2 --- Strapdown Inertial Navigation --- p.8 / Chapter 3.2.1 --- Tracking Orientation --- p.9 / Chapter 3.2.2 --- Discussion of Attitude Representations --- p.14 / Chapter 3.2.3 --- Tracking Position --- p.16 / Chapter 3.3 --- Summary of Strapdown Inertial Navigation --- p.16 / Chapter Chapter 4 --- Visual Tracking System --- p.17 / Chapter 4.1 --- Background of Visual Tracking System --- p.17 / Chapter 4.2 --- Basic knowledge of Camera Calibration and Model --- p.18 / Chapter 4.2.1 --- Related Coordinate Frames --- p.18 / Chapter 4.2.2 --- Pinhole Camera Model --- p.20 / Chapter 4.2.3 --- Calibration for Nonlinear Model --- p.21 / Chapter 4.3 --- Implementation of Process to Calibrate Camera --- p.22 / Chapter 4.3.1 --- Image Capture and Corners Extraction --- p.22 / Chapter 4.3.2 --- Camera Calibration --- p.23 / Chapter 4.4 --- Perspective-n-Point Problem --- p.25 / Chapter 4.5 --- Camera Pose Estimation Algorithms --- p.26 / Chapter 4.5.1 --- Pose Estimation Using Quadrangular Targets --- p.27 / Chapter 4.5.2 --- Efficient Perspective-n-Point Camera Pose Estimation --- p.31 / Chapter 4.5.3 --- Linear N-Point Camera Pose Determination --- p.33 / Chapter 4.5.4 --- Pose Estimation from Orthography and Scaling with Iterations --- p.36 / Chapter 4.6 --- Experimental Results of Camera Pose Estimation Algorithms --- p.40 / Chapter 4.6.1 --- Simulation Test --- p.40 / Chapter 4.6.2 --- Real Images Test --- p.43 / Chapter 4.6.3 --- Summary --- p.46 / Chapter Chapter 5 --- Kalman Filter --- p.47 / Chapter 5.1 --- Linear Dynamic System Model --- p.48 / Chapter 5.2 --- Time Update --- p.48 / Chapter 5.3 --- Measurement Update --- p.49 / Chapter 5.3.1 --- Maximum a Posterior Probability --- p.49 / Chapter 5.3.2 --- Batch Least-Square Estimation --- p.51 / Chapter 5.3.3 --- Measurement Update in Kalman Filter --- p.54 / Chapter 5.4 --- Summary of Kalman Filter --- p.56 / Chapter Chapter 6 --- Extended Kalman Filter --- p.58 / Chapter 6.1 --- Linearization of Nonlinear Systems --- p.58 / Chapter 6.2 --- Extended Kalman Filter --- p.59 / Chapter Chapter 7 --- Unscented Kalman Filter --- p.61 / Chapter 7.1 --- Least-square Estimator Structure --- p.61 / Chapter 7.2 --- Unscented Transform --- p.62 / Chapter 7.3 --- Unscented Kalman Filter --- p.64 / Chapter Chapter 8 --- Data Fusion Algorithm --- p.68 / Chapter 8.1 --- Traditional Multi-Sensor Data Fusion --- p.69 / Chapter 8.1.1 --- Measurement Fusion --- p.69 / Chapter 8.1.2 --- Track-to-Track Fusion --- p.71 / Chapter 8.2 --- Multi-Sensor Data Fusion using Extended Kalman Filter --- p.72 / Chapter 8.2.1 --- Time Update Model --- p.73 / Chapter 8.2.2 --- Measurement Update Model --- p.74 / Chapter 8.3 --- Multi-Sensor Data Fusion using Unscented Kalman Filter --- p.75 / Chapter 8.3.1 --- Time Update Model --- p.75 / Chapter 8.3.2 --- Measurement Update Model --- p.76 / Chapter 8.4 --- Simulation Test --- p.76 / Chapter 8.5 --- Experimental Test --- p.80 / Chapter 8.5.1 --- Rotational Test --- p.81 / Chapter 8.5.2 --- Translational Test --- p.86 / Chapter Chapter 9 --- Future Work --- p.93 / Chapter 9.1 --- Zero Velocity Compensation --- p.93 / Chapter 9.1.1 --- Stroke Segmentation --- p.93 / Chapter 9.1.2 --- Zero Velocity Compensation (ZVC) --- p.94 / Chapter 9.1.3 --- Experimental Results --- p.94 / Chapter 9.2 --- Random Sample Consensus Algorithm (RANSAC) --- p.96 / Chapter Chapter 10 --- Conclusion --- p.97 / Bibliography --- p.99

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND AERO. / Includes bibliographical references. / by Alison Kay Brown. / M.S.

The widespread use of mobile devices and the rise of Global Navigation Satellite Systems (GNSS) have allowed mobile tracking applications to become very popular and valuable in outdoor environments. However, tracking pedestrians in indoor environments with Global Positioning System (GPS)-based schemes is still very challenging given the lack of enough signals to locate the user. Along with indoor tracking, the ability to recognize pedestrian behavior and activities can lead to considerable growth in location-based applications including pervasive healthcare, leisure and guide services (such as, museum, airports, stores, etc.), and emergency services, among the most important ones. This thesis presents a system for pedestrian tracking and activity recognition in indoor environments using exclusively common off-the-shelf sensors embedded in smartphones (accelerometer, gyroscope, magnetometer and barometer). The proposed system combines the knowledge found in biomechanical patterns of the human body while accomplishing basic activities, such as walking or climbing stairs up and down, along with identifiable signatures that certain indoor locations (such as turns or elevators) introduce on sensing data. The system was implemented and tested on Android-based mobile phones with a fixed phone position. The system provides accurate step detection and count with an error of 3% in flat floor motion traces and 3.33% in stairs. The detection of user changes of direction and altitude are performed with 98.88% and 96.66% accuracy, respectively. In addition, the activity recognition module has an accuracy of 95%. The combination of modules leads to a total tracking error of 90.81% in common human motion indoor displacements.

A novel solution for the localization of wireless networked systems is presented. The solution is based on cooperative estimation, inter-node ranging and strap-down inertial navigation. This approach overrides limitations that are commonly found in currently available localization/positioning solutions. Some solutions, such as GPS, make use of previously deployed infrastructure. In other methods, computations are performed in a central fusion center. In the robotics field, current localization techniques rely on a simultaneous localization and mapping, (SLAM), process, which is slow and requires sensors such as laser range finders or cameras. One of the main attributes of this research is the holistic view of the problem and a systems-engineering approach, which begins with analyzing requirements and establishing metrics for localization. The all encompassing approach provides for concurrent consideration and integration of several aspects of the localization problem, from sensor fusion algorithms for position estimation to the communication protocols required for enabling cooperative localization. As a result, a conceptual solution is presented, which is flexible, general and one that can be adapted to a variety of application scenarios. A major advantage of the solution resides in the utilization of wireless network interfaces for communications and for exteroceptive sensing. In addition, the localization solution can be seamlessly integrated into other localization schemes, which will provide faster convergence, higher accuracy and less latency. Two case-studies for developing the main aspects of cooperative localization were employed. Wireless sensor networks and multi-robot systems, composed of ground robots, provided an information base from which this research was launched. In the wireless sensor network field, novel nonlinear cooperative estimation algorithms are proposed for sequential position estimation. In the field of multi-robot systems the issues of mobility and proprioception, which uses inertial measurement systems for estimating motion, are contemplated. Motion information, in conjunction with range information and communications, can be used for accurate localization and tracking of mobile nodes. A novel partitioning of the sensor fusion problem is presented, which combines an extended Kalman filter for dead-reckoning and particle filters for aiding navigation.

Spacecraft navigation is a critical component of any space mission. Space navigation uses on-board sensors and other techniques to determine the spacecraft’s current position and velocity, with permissible accuracy. It also provides requisite information to navigate to a desired position, while following the desired trajectory. Developments in technology have resulted in new techniques of space navigation. However, inertial navigation systems have consistently been the bedrock for space navigation. Recently, the successful space mission GOCE used on-board gravity gradiometer for mapping Earth’s gravitational field. This has motivated the development of new techniques like cold atom accelerometers, to create ultra-sensitive gravity gradiometers, specifically suited for space applications, including autonomous orbital navigation. This research aims to highlight the existing developments in the field of gravity gradiometry and its potential space navigation applications. The study aims to use the Linear Covariance Theory to determine specific sensor requirements to enable autonomous space navigation for different flight regimes.

This thesis presents work done towards a Personal Remote Sensing (PRS) system: small Unmanned Aerial Vehicles (UAVs) with electronic, control, and sensing subsystems. Based on papers presented to conferences (AutoTestCon2008 and MESA2009), as well as other work on PRS, multiple levels of engineering are detailed: complex multi-UAV data flow; attitude estimation filters; real-time microprocessor functionality; and small, mobile power systems. Wherever possible, Open-Source tools and designs have been used, modified, or studied, providing excellent cost to performance ratios in most cases. First, the overall PRS UAV architecture, AggieAir, is presented with a motivating examples (GhostEye and EagleEye camera payloads). Then, AggieNav, an inertial navigation system for small UAVs, is detailed, along with information about a Kalman filter for estimation of UAV navigation, position, and attitude. Finally the Spatial Environment Autonomous Logger (SEAL), a general-purpose wireless datalogger for small UAV applications, is presented, with application examples with and without small UAVs. This work represents designs based on two years of organic small UAV system growth, and provides integrated solutions to many problems of small UAV communication, sensing, and control.

GPS has become very popular in recent years. It is used in wide range of applications including aircraft navigation, search and rescue, space borne attitude and position determination and cellular network synchronization. Each application places demands on GPS for various levels of accuracy, integrity, system availability and continuity of service. Radio frequency interference (RFI) which results from many sources such as TV/FM harmonics, radar or mobile satellite systems, presents a challenge to the use of GPS. It can affect all the service performance indices mentioned above. To improve the accuracy of GPS positioning, a continuously operating reference station (CORS) network can be used. A CORS network provides all the enabled GPS users in an area with corrections to the fundamental measurements, producing more precise positioning. A threat to these networks is a threat to all high-accuracy GPS users. It is therefore necessary to monitor the quality of the received signal with the objective of promptly detecting the presence of RFI and providing a timely warning of the degradation of system accuracy, thereby boosting the integrity of GPS. This research was focused on four main tasks: a) Detection. The focus here is on a power spectral density fluctuation detection technique, in which statistical inference is used to detect narrowband continuous-wave (CW) interference in the GPS signal band after being captured by the RF front-end. An optimal detector algorithm is proposed. At this optimal point, for a fixed Detection Threshold (DT), probability of false alarm becomes minimal and for a fixed probability of false alarm, we can achieve the minimum value for the detection threshold. Experiments show that at this point we have the minimum computational load. This theoretical result is supported by real experiments. Finally this algorithm is employed to detect a real GPS interference signal generated by a TV transmitter in Sydney. b) Characterization. In the characterization section, using the GNSS signal structure and the baseband signal processing inside the GNSS receiver, a closed formula is derived for the received signal quality in terms of effective carrier to noise ratio ( ). This formula is tested and proved by calculating the C/No using the I and Q data from a software GPS receiver. For pulsed CW, a similar analysis is done to characterize the effect of parameters such as pulse repetition period (PRP) and also duty cycle on the received signal quality. Considering this characterization and the commonality between the GPS C/A code and Galileo signal as a basis to build up a common term for satellite availability, the probability of satellite availability in the presence of CW interference is defined and for the two currently available satellite navigation systems (GPS L1 signal and Galileo signal (GIOVE-A BOC(1, 1) in the E1/L1 band)) it is shown that they can be considered as alternatives to each other in the presence of different RFI frequencies as their availability in the presence of CW RFI is different in terms of RFI frequency. c) Mitigation. The last section of the research presents a new concept of ?Satellite Exclusion Zone?. In this technique, using our previously developed characterization techniques, and considering the fact that RFI has different effects on different satellite signals at different times depending on satellite Doppler frequency, the idea of excluding the most vulnerable satellite signal from positioning calculations is proposed. Using real data and real interference, the effectiveness of this technique is proven and its performance analyzed. d) Hardware implementation. The above detection technique is implemented using the UNSW FPGA receiver board called NAMURU.

This thesis deals with estimating position and orientation in real-time, using measurements from vision and inertial sensors. A system has been developed to solve this problem in unprepared environments, assuming that a map or scene model is available. Compared to ‘camera-only’ systems, the combination of the complementary sensors yields an accurate and robust system which can handle periods with uninformative or no vision data and reduces the need for high frequency vision updates. The system achieves real-time pose estimation by fusing vision and inertial sensors using the framework of nonlinear state estimation for which state space models have been developed. The performance of the system has been evaluated using an augmented reality application where the output from the system is used to superimpose virtual graphics on the live video stream. Furthermore, experiments have been performed where an industrial robot providing ground truth data is used to move the sensor unit. In both cases the system performed well. Calibration of the relative position and orientation of the camera and the inertial sensor turn out to be essential for proper operation of the system. A new and easy-to-use algorithm for estimating these has been developed using a gray-box system identification approach. Experimental results show that the algorithm works well in practice.

In this thesis, rapid transfer alignment algorithm for a helicopter launched guided munition is studied. Transfer alignment is the process of initialization of a guided munition&rsquo / s inertial navigation system with the aid of the carrier platform&rsquo / s navigation system, which is generally done by comparing the navigation data of missile and carrier&rsquo / s navigation data. In the literature, there are different studies of transfer alignment, especially for aircraft launched munitions. One important problem in transfer alignment is the attitude uncertainty of lever arm between munition&rsquo / s and carrier&rsquo / s navigation systems. In order to overcome this problem, most of the studies in the literature do not use carrier&rsquo / s attitude data in the transfer alignment, only velocity data is used. In order to estimate attitude and related inertial sensor errors, specific maneuvers of carrier platform are required which can take 1-5 minutes. The purpose of this thesis is to compensate the errors arising from the dynamics of the Helicopter, lever arm, mechanical vibration effects and inertial sensor error amplification, thus designing a transfer alignment algorithm under real environment conditions. The algorithm design begins with observability analysis, which is not done for helicopter transfer alignment in literature. In order to make proper compensations, characterization and modeling of vibration and lever arm environment is done for the helicopter. Also, vibration based errors of MEMS based inertial sensors are experimentally shown. The developed transfer alignment algorithm is tested by simulated and experimental data