Towards an Online Sensor Model Validation and Estimation Framework

Juan D. Jurado, John F. Raquet

Abstract:	Conventional methods for assessing navigation sensor performance focus on detecting sensor failures through the use of measurement residuals in a Kalman filter or Extended Kalman Filter. Additionally, even though there has been a considerable amount of research into sensor-specific online calibration algorithms aimed at estimating sensor model parameters, none of these methods consider the possibility a sensor model has been improperly specified prior to integration into the ongoing navigation solution (i.e., unmodeled biases or inaccurate measurement noise strength). Due to the growing interest in alternative, all-source navigation, there exists a need for a general, sensor-independent statistical framework for validating the specified sensor model prior to integration into the navigation solution. This research proposes a generalized sensor validation algorithm, in which sensor models are statistically validated using a novel online likelihood ratio test, while mitigating the risk of corrupting the ongoing solution due to corrupted measurements. This validation process is part of a larger three-phased framework aimed at managing alternative sensor model validation, integrating sensor-specific online calibration routines, and providing a means for online sensor remodeling instead of simply declaring a failure and rejecting its measurements. The proposed validation algorithm is developed in the context of the larger framework and its benefits are demonstrated and compared to conventional methods using simulated navigation data.
Published in:	2018 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 23 - 26, 2018 Hyatt Regency Hotel Monterey, CA
Pages:	1319 - 1325
Cite this article:	Jurado, Juan D., Raquet, John F., "Towards an Online Sensor Model Validation and Estimation Framework," 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, April 2018, pp. 1319-1325. https://doi.org/10.1109/PLANS.2018.8373522
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