Thesis. 1975. M.S.--Massachusetts Institute of Technology. Dept. of Mechanical Engineering. / Bibliography: leaves 194-195. / by Richard James Casler, Jr. / M.S.

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. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. / Microfiche copy available in Archives and Aero. / Includes bibliographical references. / M.S.

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.

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

GPS is a widely accepted means of navigation, whether it is for civilian or military means. With the implementation of GPS on smart projectiles, these weapons have been able to achieve remarkable accuracy. Even though the improvements in accuracy are impressive, GPS signals are susceptible to jamming and spoofing by a sufficiently motivated enemy. The work reported here examines the viability of constructing a navigation solution using ground based signals of opportunity that provide range and range rate information. Using a generalized sensor model encompassing the key error terms, a variety of physical devices are included in the analysis. For a typical indirect fire trajectory, navigation solutions are computed as a function of the number and density of signal sources, terrain type, and sensor errors. Systematic studies were performed using these parameters in order to better understand the merits and demerits of this type of system to create a useful navigation solution. Based on these studies, results indicate that navigation solutions can be computed with the same accuracy as current GPS systems with a moderate number of signal sources. Generally, more accurate solutions are obtained when the projectile is directly over the signal sources and there is variation of signal source location in all three axes.

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.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 206-216).

Thesis (M.S.)--Ohio University, June, 2000. / Title from PDF t.p.