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Enhancing the Accuracy of Automotive Integrated Navigation System with Data Fusion of Inertial Navigation System and Dynamic Model

Jafari Shalmazari, Amir Abbas | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57108 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Salarieh, Hassan; Narimani, Roya
  7. Abstract:
  8. Navigation has been of special importance in various systems for a long time. In fact, determining the location, direction, speed and acceleration of a moving object has been a vital and important issue for mankind. Various methods have been used for this problem, the most important of which is perhaps the use of GPS. But in some situations, such as inside tunnels or places where it is not possible to send signals well, in order to continue navigation, we must use alternative methods such as model-based navigation and INS and in the form of integrated navigation system. Our final goal in this thesis is to prove that the used model-based navigation (based on the proposed dynamic model for the vehicle) has the ability to improve the INS navigation and can reduce its error to a great extent. So far, various methods have been used to modify INS. But in this thesis, we want to use the dynamic model suitable for the car and the sensors in it, in order to modify the INS. For this purpose, after reviewing the related research done in this field, we have described a dynamic model suitable for the car. This dynamic model is described according to the available sensors of the car. After describing the model, the increase in the accuracy of car navigation is proven through the integration of INS data with dynamic model data, without noise and in the presence of noise, via a computer simulation. The method used to integrate the dynamic model with INS data is the Kalman filter. After the simulation, during a practical test of the investigated vehicle, data was collected from the various required sensors and modules, and through the equations of the dynamic model of the vehicle, INS and the set Kalman filter, more accurate positioning was achieved and increase in the accuracy of the integrated positioning of the INS and the dynamic model compared to mere INS positioning is practically proven. Of course, there is still a high error in this integrated navigation system, the reason of which can be explained
  9. Keywords:
  10. Integrated Navigation System ; Intertial Navigation ; Model-aided Navigation ; Modeling ; Vehicle Dynamics ; Kalman Filters ; Dynamic Vehicle Model

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