Position determination of mobile unit based on inertial navigation system.

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