On Performance Evaluation of Inertial Navigation Systems: The Case of Stochastic Calibration

Article Article scientifique Article On Performance Evaluation of Inertial Navigation Systems: The Case of Stochastic Calibration eng 2023 author Cucci Davide A. 0000-0002-7358-564X Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland author Voirol Lionel 0000-0003-1696-1407 Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland 817891 Research center for statistics (STATI) author Khaghani Mehran 0000-0002-2188-9350 Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland 922782 author Guerrier Stéphane 0000-0003-0002-8794 Faculty of Science and the Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland 625357 Recherche et enseignement GSEM (RESEM) 1-17 10.1109/TIM.2023.3267360 0018-9456 https://ieeexplore.ieee.org/document/10104150/ In this work we address the problem of rigorously evaluating the performances of a inertial navigation system under design in presence of multiple alternative choices. We introduce a framework based on Monte-Carlo simulations in which a standard extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms and attempts to recover a reference trajectory from noisy measurements. The evaluation of several statistical metrics of the solution, aggregated over hundreds of realizations, gives a reasonable estimate of the expected performances of the system in real-world conditions and allow the user to operate the choice between alternative setups. To show the generality of our approach, we consider an example application to the problem of stochastic calibration. Two compet- ing stochastic modeling techniques, namely, the widely popular Allan variance linear regression, and the emerging generalised method of wavelet moments are rigorously compared in terms of the framework defined metrics and in multiple scenarios. We find that the latter provides substantial advantages and should be preferred, at least for certain classes of inertial sensors. Our framework allows to consider a wide range of problems related to the quantification of navigation system performances such as, for example, the robustness of an INS with respect to outliers or other modeling imperfections. While real world experiments are essential to assess to performance of new methods they tend to be costly and are typically unable to lead to a sufficient number of replicates to evaluate, for example, the correctness of estimated uncertainty. Therefore, our method can bridge the gap between these experiments and pure statistical consideration as done, for example, in the stochastic calibration literature Kalman Filter Monte-Carlo Simulations Allan variance Generalized Method of Wavelet Momen Dewey 310 Swiss National Science Foundation New Challenges for Statistical Methods in Large and Complex Data Settings: Analysis of Dependent Data and Model Selection 176843 CH Careers 117 Research Center for Statistics S5 false 1377 A disregarded past IEEE transactions on instrumentation and measurement 72 5abb8697-2f69-4b64-ae44-c911a6e7d61a Article (Submitted version) Public Article-scientifique-from-unpaywall.pdf 2207540 application/pdf CC BY 98a6392d937c2837fee80fc95e095f696c5e2ae6b4e6b9483b7ab85580ac9e9d openAccess Faculté d'économie et de management / Research Center for Statistics

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On Performance Evaluation of Inertial Navigation Systems: The Case of Stochastic Calibration	IEEE transactions on instrumentation and measurement			2023	80	115

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Khaghani, Mehran