Thesis (M.S.)--University of Florida, 2002. / Title from title page of source document. Includes vita. Includes bibliographical references.

Thesis (M.S.)--University of Texas at El Paso, 2009. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

The thesis covers data fusion for aircraft navigation systems in distributed sensor systems. Data fusion methodologies are developed for the design, development, analysis and simulation of multisensor aircraft navigation systems. The problems of sensor failure detection and isolation (FDI), distributed data fusion algorithms and inertial state integrity monitoring in inertial network systems are studied. Various existing integrated navigation systems and Kalman filter architectures are reviewed and a new generalised multisensor data fusion model is presented for the design and development of multisensor navigation systems. Normalised navigation algorithms are described for data fusion filter design of inertial network systems. A normalised measurement model of skewed redundant inertial measurement units (SRIMU) is presented and performance criteria are developed to evaluate optimal configurations of SRIMUs in terms of the measurement accuracy and FDI capability. Novel sensor error compensation filters are designed for the correction of SRIMU measurement errors. Generalised likelihood ratio test (GLRT) methods are improved to detect various failure modes, including short time and sequential moving-window GLRT algorithms. State-identical and state-associated fusion algorithms are developed for two forms of distributed sensor network systems. In particular, innovative inertial network sensing models and inertial network fusion algorithms are developed to provide estimates of inertial vector states and similar node states. Fusion filter-based integrity monitoring algorithms are also presented to detect network sensor failures and to examine the consistency of node state estimates in the inertial network system. The FDI and data fusion algorithms developed in this thesis are tested and their performance is evaluated using a multisensor software simulation system developed during this study programme. The moving-window GLRT algorithms for optimal SRIMU configurations are shown to perform well and are also able to detect jump and drift failures in an inertial network system. It is concluded that the inertial network fusion algorithms could be used in a low-cost inertial network system and are capable of correctly estimating the inertial vector states and the node states.

University of Macau / Faculty of Science and Technology / Department of Electrical and Computer Engineering

D.Ing. / Aerial Vehicle (UAV)-based observation system, by using the principles of strapdown inertial measurement and navigation systems. Effort is concentrated around the mathematical implementation thereof and analysis and proof of the concept in a computer simulation environment. Although the principles of the strapdown system approach to camera-to-target vector estimation are universal to any type of airborne platform that can carry the observation payload, the application thereof is specifically tailored for a UAV system. More specifically, the operational scenario and UAV parameters of a typical close-range UAV system that is used for artillery observation, is used in the derivation of the models and equations. The secondary objective of this research is to derive a realizable mathematical implementation for this strapdown system based camera-to-target vector estimation methodology, by performing a systematic tradeoff between the use of Euler angles and quaternions for describing the camera-to-target vector, and by incorporating the principles of Kalman filtering. This dissertation fully describes the approach that was followed in the derivation of the strapdown system equations for the camera-to-target vector estimation. The mathematical models and principles used are universal for any airborne targeting application with a real-time video down-link. The results as presented in this dissertation, prove that the methodology provides satisfactory results in both a pure digital computer simulation environment, as well as in a digital computer simulation that is hybridized with experimentally determined sensor outputs. It has led to a realizable and workable implementation that could form the basis of practical implementation thereof in operational targeting systems. It further proves that the slant range between a camera and a stationary target on the ground, can be estimated effectively without the use of a laser rangefinder.

This thesis deals with the challenge of autonomous navigation of the ExoMars rover. The absence of global positioning systems (GPS) in space, added to the limitations of wheel odometry makes autonomous navigation based on these two techniques - as done in the literature - an inviable solution and necessitates the use of other approaches. That, among other reasons, motivates this work to use solely visual data to solve the robot’s Egomotion problem. The homogeneity of Mars’ terrain makes the robustness of the low level image processing technique a critical requirement. In the first part of the thesis, novel solutions are presented to tackle this specific problem. Detection of robust features against illumination changes and unique matching and association of features is a sought after capability. A solution for robustness of features against illumination variation is proposed combining Harris corner detection together with moment image representation. Whereas the first provides a technique for efficient feature detection, the moment images add the necessary brightness invariance. Moreover, a bucketing strategy is used to guarantee that features are homogeneously distributed within the images. Then, the addition of local feature descriptors guarantees the unique identification of image cues. In the second part, reliable and precise motion estimation for the Mars’s robot is studied. A number of successful approaches are thoroughly analysed. Visual Simultaneous Localisation And Mapping (VSLAM) is investigated, proposing enhancements and integrating it with the robust feature methodology. Then, linear and nonlinear optimisation techniques are explored. Alternative photogrammetry reprojection concepts are tested. Lastly, data fusion techniques are proposed to deal with the integration of multiple stereo view data. Our robust visual scheme allows good feature repeatability. Because of this, dimensionality reduction of the feature data can be used without compromising the overall performance of the proposed solutions for motion estimation. Also, the developed Egomotion techniques have been extensively validated using both simulated and real data collected at ESA-ESTEC facilities. Multiple stereo view solutions for robot motion estimation are introduced, presenting interesting benefits. The obtained results prove the innovative methods presented here to be accurate and reliable approaches capable to solve the Egomotion problem in a Mars environment.

The Combined Laser and Scan Sonar (CLASS) system is an extended range imaging system, incorporating both high-resolution laser images and high frequency sonar images. Both the laser and sonar images are collected simultaneously during testing to provide dual mode imagery of an underwater target, displaying both a 2D image of the target (laser image) and a 3D overlay of the target (sonar image). The laser component of the system is a Multiple Overlapping Field of view Serial Laser Imager (MOFSLI), capable of generating high-resolution sub-centimeter 2D images. MOFSLI generates the images by way of a near diffraction-limited 532 [nm] continuous wave (CW) laser beam being scanned over the target. Initial field tests resulted in high-quality images of the ocean floor, but also indicated the need for additional research on MOFSLI. In this thesis, we focus on the calibration of MOFSLI and on the evaluation of the image quality generated by this system, as a function of range, source power, receiver gain and water turbidity. This work was completed in the specialized underwater electrooptics testing facility located in the Ocean Visibility and Optics laboratory at Harbor Branch Oceanographic Institute (HBOI). Laboratory testing revealed the operational limits of the system, which functioned well until just beyond five attenuation lengths, where it becomes contrast limited due attenuation of target signal and the collection of non-image bearing backscattered photons. Testing also revealed the optimal settings of the system at given environmental conditions. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Stephen G. Sifferlen. / M.S.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND AERO. / Bibliography: leaves 83-84. / by Steven Ray Hall. / M.S.

Yip, Wai Lee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 119-124). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.ii / Acknowledgement --- p.iii / Table of Content --- p.iv / List of Figure --- p.vi / List of table --- p.viii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Background information --- p.2 / Chapter 1.2.1 --- Overview of positioning technologies --- p.2 / Chapter 1.2.2 --- Comparison between different positioning systems --- p.7 / Chapter 1.2.3 --- Recent works related to INS --- p.9 / Chapter 1.3 --- Objective --- p.11 / Chapter 1.4 --- Organization of thesis --- p.11 / Chapter Chapter 2 --- Literature Study --- p.13 / Chapter 2.1 --- Introduction to INS --- p.13 / Chapter 2.1.1 --- Coordinate Frames --- p.13 / Chapter 2.1.2 --- Gimbaled INS --- p.16 / Chapter 2.1.3 --- Strapdown INS --- p.17 / Chapter 2.1.4 --- Conventional algorithm of strapdown INS --- p.17 / Chapter 2.2 --- Inertial sensors --- p.19 / Chapter 2.2.1 --- Gyroscope --- p.19 / Chapter 2.2.2 --- Accelerometer --- p.20 / Chapter 2.3 --- Previous works --- p.22 / Chapter 2.4 --- GF-INS --- p.23 / Chapter 2.5 --- Summary --- p.25 / Chapter Chapter 3 --- Performance of MEMS accelerometer in position determination --- p.27 / Chapter 3.1 --- Basic principle --- p.27 / Chapter 3.2 --- Numeric integration --- p.28 / Chapter 3.3 --- Experimental setup --- p.30 / Chapter 3.3.1 --- MEMS Accelerometer --- p.30 / Chapter 3.3.2 --- Microcontroller --- p.32 / Chapter 3.3.3 --- System architecture --- p.33 / Chapter 3.3.4 --- Testing platform --- p.34 / Chapter 3.4 --- Initial calibration and filtering --- p.37 / Chapter 3.4.1 --- Convert ADC reading to acceleration --- p.37 / Chapter 3.4.2 --- Identify configuration error --- p.38 / Chapter 3.4.3 --- Implement low pass filter --- p.39 / Chapter 3.5 --- Experimental results --- p.40 / Chapter 3.5.1 --- Results --- p.40 / Chapter 3.5.2 --- Discussion --- p.43 / Chapter 3.6 --- Summary --- p.45 / Chapter Chapter 4 --- Performance Improvement --- p.46 / Chapter 4.1 --- Fuzzy logic based steady state detector --- p.46 / Chapter 4.1.1 --- Principle --- p.46 / Chapter 4.1.2 --- Experimental result --- p.48 / Chapter 4.2 --- Kalman filtering --- p.50 / Chapter 4.2.1 --- Discrete Kalman filter --- p.50 / Chapter 4.2.2 --- Combine with fuzzy logic based steady state detector --- p.52 / Chapter 4.2.3 --- Experimental results --- p.54 / Chapter 4.3 --- Summary --- p.58 / Chapter Chapter 5 --- Construction of GF-INS --- p.59 / Chapter 5.1 --- Principle of GF-INS --- p.59 / Chapter 5.1.1 --- Algorithm --- p.59 / Chapter 5.1.2 --- Comparing error of GF-INS and conventional INS --- p.66 / Chapter 5.1.3 --- Simulation study --- p.67 / Chapter 5.2 --- Experimental setup --- p.73 / Chapter 5.3 --- Experimental Results --- p.75 / Chapter 5.4 --- Summary --- p.81 / Chapter Chapter 6 --- Improvement on the GF-INS --- p.82 / Chapter 6.1 --- Configuration error compensation --- p.82 / Chapter 6.1.1 --- "Identify bias, scale factor and sensing direction error" --- p.83 / Chapter 6.1.2 --- Identify position error --- p.86 / Chapter 6.1.3 --- Compensator design --- p.89 / Chapter 6.1.4 --- Simulation --- p.91 / Chapter 6.2 --- Fuzzy rule based motion state detector --- p.97 / Chapter 6.2.1 --- Relation of data in different motions --- p.97 / Chapter 6.2.2 --- Fuzzy system --- p.99 / Chapter 6.2.3 --- Membership function training with gradient descent --- p.101 / Chapter 6.3 --- Experimental results and discussion --- p.104 / Chapter 6.3.1 --- Configuration errors --- p.104 / Chapter 6.3.2 --- Compensator --- p.106 / Chapter 6.3.3 --- Fuzzy rule based motion state detector --- p.107 / Chapter 6.3.4 --- Comparing the performance of both methods --- p.110 / Chapter 6.3.5 --- Comparing GF-INS and one dimensional INS --- p.112 / Chapter 6.3.6 --- Discussion --- p.113 / Chapter 6.4 --- Summary --- p.115 / Chapter Chapter 7 --- Conclusions and Future works --- p.116 / Reference --- p.119