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

<p>This thesis aims the problem of underwater vehicle positioning. The vehicle usedwas a Saab Seaeye Falcon which was equipped with a Doppler Velocity Log(DVL)manufactured by RD Instruments and an inertial measurement unit (IMU) fromXsense. During the work several different Extended Kalman Filter (EKF) havebeen tested both with a hydrodynamic model of the vehicle and a model withconstant acceleration and constant angular velocity. The filters were tested withdata from test runs in lake Vättern. The EKF with constant acceleration andconstant angular velocity appeared to be the better one. The misalignment of thesensors were also tried to be estimated but with poor result.</p>

A method is proposed to fuse the information from two navigation systems whose relative position is unknown, but where there exists an upper limit on how far apart the two systems can be. The proposed information fusion method is applied to a scenario in which a pedestrian is equipped with two foot-mounted zero-velocity-aided inertial navigation systems; one system on each foot. The performance of the method is studied using experimental data. The results show that the method has the capability to significantly improve the navigation performance when compared to using two uncoupled foot-mounted systems. / <p>QC 20121221</p>

This thesis aims the problem of underwater vehicle positioning. The vehicle usedwas a Saab Seaeye Falcon which was equipped with a Doppler Velocity Log(DVL)manufactured by RD Instruments and an inertial measurement unit (IMU) fromXsense. During the work several different Extended Kalman Filter (EKF) havebeen tested both with a hydrodynamic model of the vehicle and a model withconstant acceleration and constant angular velocity. The filters were tested withdata from test runs in lake Vättern. The EKF with constant acceleration andconstant angular velocity appeared to be the better one. The misalignment of thesensors were also tried to be estimated but with poor result.

A general automotive navigation system is a satellite navigation system designed for use inautomobiles. It typically uses GPS to acquire position data to locate the user on a road in the unit's map database. However, due to recent improvements in the performance of small and lightweight micro-machined electromechanical systems (MEMS) inertial sensors have made the application of inertial techniques to such problems, possible. This has resulted in an increased interest in the topic of inertial navigation. In location tracking system, sensors are used either individually or in conjunction like in data fusion. However, still they remain noisy, and so there is a need to measure maximum data and then make an efficient system that can remove the noise from data and provide a better estimate. The task of this thesis work was to take data from two sensors, and use an estimation technique toprovide an accurate estimate of the true location. The proposed sensors were an accelerometer and a GPS device. This thesis however deals with using accelerometer sensor and using estimation scheme, Kalman filter. The thesis report presents an insight to both the proposed sensors and different estimation techniques. Within the scope of the work, the task was performed using simulation software Matlab. Kalman filter’s efficiency was examined using different noise levels.

Thesis (Ph. D.)--Ohio University, 2002. / Title from PDF t.p.

Thesis (M.S.)--Ohio University, August, 2003. / Title from PDF t.p. Includes bibliographical references (leaves 33-34).

Thesis (M. Eng.)--Carleton University, 2001. / Includes bibliographical references p. (115-117). Also available in electronic format on the Internet.