A Feasibility Study of Machine Learning Based Coarse Alignment

Idan Zak, Itzik Klein, Reuven Katz

Research output: Contribution to journalArticlepeer-review


Inertial navigation systems (INSs) require an initial attitude before its operation. To that end, the coarse alignment process is applied using inertial sensors readings. For low-cost inertial sensors, only the accelerometers readings are processed to yield the initial roll and pitch angles. The accuracy of the coarse alignment procedure is vitally important for the navigation solution accuracy due to the navigation solution drift accumulating over time. In this paper, we propose using machine learning (ML) approaches, instead of traditional approaches, to conduct the coarse alignment procedure. To that end, a new methodology for the alignment process is proposed, based on state-of-the-art ML algorithms such as random forest (RF) and the more advanced boosting method of gradient tree XGBoost. Results from a simulated alignment of stationary INS scenarios are presented accompanied by a feasibility study. ML results are compared with the traditional coarse alignment methods in terms of time to convergence and accuracy performance. When using the proposed approach, with the examined scenarios, results show a significant improvement of the accuracy and time required for the alignment process.
Original languageEnglish
Article number50
Issue number1
StatePublished - 2019
Externally publishedYes


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