ContentsPage Noslist of contents i-iiiList of Figures iv - vList of Tables vi1. Introduction11.1 Navigation11.2 Inertial Navigation System11.3 Principle Of Inertial Navigation System32. Inertial Navigation52.1 Inertial System Configurations62.1.1 Stable Platform Systems62.1.2 Strapdown Systems73. Gyroscopes83.1 Types of Gyroscope83.1.1 Mechanical83.1.2 Optical103.1.3 MEMS Gyroscopes103.2 MEMS Gyro Error Characteristics123.2.1 Constant Bias123.2.2 Thermo-Mechanical White Noise / Angle Random Walk133.2.3 Flicker Noise / Bias Stability143.2.4 Temperature Effects143.2.5 Calibration Errors153.2.6 Summary164. Linear Accelerometers174.1 Types of Accelerometer174.1.1 Mechanical174.1.2 Solid State184.1.3 MEMS Accelerometers184.2 MEMS Accelerometer Error Characteristics184.2.1 Constant Bias194.2.2 Thermo-Mechanical White Noise / Velocity Random Walk194.2.3 Flicker Noise / Bias Stability204.2.4 Temperature Effects204.2.5 Calibration Errors215. Signal Noise Analysis235.1 Allan Variance235.2 Xsens Mtx Analysis256. Strapdown Inertial Navigation276.1 Tracking Orientation276.1.1 Theory276.1.2 Implementation266.1.3 Propagation of Errors276.2 Tracking Position286.2.1 Theory286.2.2 Implementation296.2.3 Propagation of Errors316.3 Mtx INS Example327. Strapdown INS Simulation377.1 IMU Simulation377.2 Simulation Validation387.3 Gyroscope Errors428. Reducing Drift In Inertial Navigation Systems448.1 Sensor Fusion448.1.1 Fusion with Absolute Positioning Systems448.1.2 Fusion with Magnetometers458.2 Domain Specific Assumptions459.LabVIEW479.1 Introduction479.2 Dataflow Programming479.3 Graphical Programming479.4 Benefits489.5 Usage Of Labview519.6 Front Panel519.7 Block Diagram519.8 Front Panel And Block Diagram Tools519.9 Result52Conclusion54References55

LIST OF FIGURES Page NosFig2. 1: The body and global frames of reference5Fig 2.2: A Stable Platform IMU6Fig 2.3: Stable Platform inertial Navigation Algorithm7Fig2. 4: Strapdown inertial Navigation Algorithm7Fig3.1: A conventional mechanical gyroscope9Fig3.2: The Sagnac effect10Fig3.3: A vibrating mass gyroscope11Fig4.1: A mechanical accelerometer17Fig4.2: A surface acoustic wave accelerometer18Fig5.1: A possible log-log plot of Allan Deviation analysis results24Fig5.2: Allan Deviation plot for the Mtx gyroscopes25Fig5.3: Allan Deviation plot for the Mtx accelerometers26Fig6.1: Strap down inertial navigation algorithm27Fig6.2: A plot showing how the average drift in position 34incurred the system increases over timeFig6.3: Ten paths obtained by applying the strapdown algorithm to a 32stationary Xsens device for 60 second periods(a) Zoomed out. The paths obtained after removing gyroscope noise are not visible35(b) Zoomed in, showing the paths obtained after all gyroscope noise was removed35Fig6.4: The average drift with selectively removed noise sources36Fig6.5: A log-log plot of the average drift with selectively removed noise sources36

LIST OF FIGURES Page NosFig7.1: The first 5 seconds of example random noise sequences39(a) White noise sequence39(b) Random walk sequence39Fig7.2: Average position drifts measured for the simulated and real devices41Fig7.3: A log-log plot of the simulated average drift with selectively removed noise sources41Fig7.4: A plot of the simulated average drifts when the gyro signals are perturbed by selective noise processes42Fig7.5: The simulation user interface43Fig8.1: The performance gain obtained by using the Xsens sensor fusion algorithm rather than the INS attitude algorithm to calculate the orientation of the device46Fig9.1: Simulation Of Inertial Navigation System53Fig9.2: Model Of Inertial Navigation System54

LIST OF TABLES Page NosTable3.1: Specifications for the Honeywell GG1320AN and GG5300 gyroscopes12Table 3.2: A Summary of Gyro Error Sources16Table 4.1: A Summary of Accelerometer Error Sources22Table 5.1: Gyroscope Noise Measurements24Table 5.2: Accelerometer Noise Measurements25Table 6.1: simulation noise parameters corresponding to the Mtx device39

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