|M.Sc Student||Zak Idan|
|Subject||Improving Autonomous Platforms INS Alignment Process|
using Machine Learning Methods
|Department||Department of Autonomous Systems and Robotics||Supervisors||ASSOCIATE PROF. Reuven Katz|
|DR. Itzik Klein|
|Full Thesis text|
Inertial navigation systems (INS) provides the platform's position, velocity, and attitude. To that end, initial conditions are required before system operation. While initial position and velocity are provided by external means, the initial attitude can be determined using the system's inertial sensors in a process known as coarse alignment. For low-cost inertial sensors, only the accelerometers readings are processed to estimate the initial roll and pitch angles. The accuracy of the coarse alignment procedure is vitally important for the navigation solution accuracy, particularly for pure-inertial scenarios, due to the navigation solution drift accumulating over time.
In this research, we propose using machine learning (ML) approaches, instead of traditional ones, to conduct the coarse alignment procedure in stationary conditions. A new methodology for the alignment process is proposed, based on ML algorithms such as Random Forest and some more advanced boosting methods like gradient tree XGBoost. Results from a simulated alignment of stationary INS scenarios are presented accompanied by field experiments results. ML results are compared with the traditional coarse alignment method in terms of time to convergence and accuracy performance. Results obtained using the proposed approach shows significant improvement of the accuracy and time required for the alignment process.