Error reduction techniques for a MEMS accelerometer-based digital input device.

Tsang, Chi Chiu.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 66-69).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiStatement of Originality --- p.vTable of Contents --- p.viiList of Figures --- p.xNomenclature --- p.xiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Objectives --- p.3Chapter 1.3 --- Contributions --- p.3Chapter 1.4 --- Thesis Organization --- p.4Chapter 2 --- A Ubiquitous Digital Writing System --- p.5Chapter 2.1 --- Introduction --- p.5Chapter 2.2 --- MEMS Motion Sensing Technology --- p.6Chapter 2.2.1 --- Micro-Electro-Mechanical Systems (MEMS) --- p.6Chapter 2.2.2 --- Principle of a MEMS Accelerometer --- p.6Chapter 2.2.3 --- Principle of a MEMS Gyroscope --- p.7Chapter 2.3 --- Architecture of Ubiquitous Digital Writing System --- p.8Chapter 2.3.1 --- Micro Inertial Measurement Unit (μlMU) --- p.8Chapter 2.3.2 --- Data Transmission Module --- p.10Chapter 2.3.3 --- User Interface Software --- p.10Chapter 2.4 --- Summary --- p.12Chapter 3 --- Calibration of μ-Inertial Measurement Unit --- p.13Chapter 3.1 --- Introduction --- p.13Chapter 3.2 --- Sources of Error --- p.13Chapter 3.2.1 --- Deterministic Errors --- p.13Chapter 3.2.2 --- Stochastic Error --- p.14Chapter 3.3 --- Calibration of Accelerometers --- p.14Chapter 3.4 --- Coordinate Transformation with Gravity Compensation --- p.15Chapter 3.4.1 --- Coordinate Transformation --- p.16Chapter 3.4.2 --- Attitude Determination --- p.18Chapter 3.4.3 --- Gravity Compensation --- p.19Chapter 3.5 --- Summary --- p.20Chapter 4 --- Zero Velocity Compensation --- p.21Chapter 4.1 --- Introduction --- p.21Chapter 4.2 --- Algorithm Description --- p.21Chapter 4.2.1 --- Stroke Segmentation --- p.22Chapter 4.2.2 --- Zero Velocity Compensation (ZVC) --- p.22Chapter 4.3 --- Experimental Results and Discussion --- p.23Chapter 4.4 --- Summary --- p.24Chapter 5 --- Kalman Filtering --- p.28Chapter 5.1 --- Introduction --- p.28Chapter 5.2 --- Summary of Kalman filtering algorithm --- p.28Chapter 5.2.1 --- System Model --- p.28Chapter 5.2.2 --- Initialization --- p.29Chapter 5.2.3 --- Time Update --- p.32Chapter 5.2.4 --- Measurement Update --- p.33Chapter 5.2.5 --- Stroke Segmentation --- p.34Chapter 5.3 --- Summary --- p.34Chapter 6 --- Error Compensation from Position Feedback --- p.35Chapter 6.1 --- Introduction --- p.35Chapter 6.2 --- Global Positioning System (GPS) --- p.35Chapter 6.3 --- Zero z-axis Kalman Filtering --- p.36Chapter 6.3.1 --- Algorithm Implementation --- p.36Chapter 6.3.2 --- Experimental Results and Discussion --- p.40Chapter 6.4 --- Combined Electromagnetic Resonance (EMR) Position Detection Board and μlMU --- p.43Chapter 6.4.1 --- EMR Position Detection System --- p.43Chapter 6.4.2 --- A Combined Scheme --- p.44Chapter 6.4.3 --- Algorithm Implementation --- p.46Chapter 6.4.4 --- Synchronization --- p.50Chapter 6.4.5 --- Experimental Results and Discussion --- p.50Chapter 6.5 --- Summary --- p.54Chapter 7 --- Conclusion --- p.55Chapter 7.1 --- Future Work --- p.56Chapter 7.1.1 --- Improvement in the μlMU --- p.56Chapter 7.1.2 --- Combined Camera Optical Tracking and μlMU --- p.57Chapter 7.2 --- Concluding Remarks --- p.58Chapter A --- Derivation of Kalman Filtering Algorithm --- p.59Chapter A.1 --- Introduction --- p.59Chapter A.2 --- Derivation of a Priori State Estimation Equation --- p.60Chapter A.3 --- Derivation of a Posteriori State Estimation Equation --- p.60Chapter A.4 --- Derivation of a Priori Error Covariance Matrix --- p.61Chapter A.5 --- Derivation of the Optimal Kalman Gain --- p.62Chapter A.6 --- Derivation of a Posteriori Error Covariance Matrix --- p.63Chapter B --- Derivation of Process Noise Covariance Matrix --- p.64Bibliography --- p.66Publications --- p.6