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Enhancing the Accuracy of Automotive Integrated Navigation System Using High-Order Error Model of Inertial Sensors Utilizing Auxiliary Sensors

Ziaee Shahraki, Mohammad Reza | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57079 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Salarieh, Hassan
  7. Abstract:
  8. Navigation in various systems for land, air and sea movement has been of special importance for a long time. Determining the location, direction, speed and acceleration of moving has been a vital issue for mankind and it is also a basic prerequisite for starting the control process. Various methods have been used for this problem, the most important of which is the global positioning system. This method is satellite-based and any user from any part of the world can perform positioning. But in some places, such as tunnels or crowded places in the city, where it is not possible to send a signal, other methods are used. Also, in order to reduce errors, we can use the inertial navigation system or any other sensor that gives us usable information. In the inertial navigation system, sensors such as accelerometers and gyroscopes are used to recursively obtain the speed and then the moving position by integration. Inertial sensors have various errors and integration causes a cumulative increase of these errors. Among the types of these errors, we can mention bias, scale factor and non-perpendicularity of sensor axes. By using sensor modeling, these errors can be estimated and then removed. Random models and Gauss Markov are among these models. Sometimes the error is modeled only with a constant number or low-order Gaussian Markov models. In the present project, higher order Gauss Markov models should be used to estimate the bias error of inertial sensors. Also, by using some other sensors in a car system, it is possible to improve the accuracy of positioning systems
  9. Keywords:
  10. Integrated Navigation System ; Inertial Navigation System ; Global Position System (GPS) ; Error Estimation ; Inertial Sensor Error ; Car Navigation

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