A Comparison of Methods for Online Lever Arm Estimation in GPS/INS Integration

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dc.creatorMontalbano, Nick
dc.creatorHumphreys, Todd E.
dc.date.accessioned2020-08-04T13:54:29Z
dc.date.available2020-08-04T13:54:29Z
dc.date.issued2018
dc.description.abstractA comparison of neural network, state augmentation, and multiple model-based approaches to online location of inertial sensors on a vehicle is presented that exploits dualantenna carrier-phase-differential GNSS. The best technique among these is shown to yield a significant improvement on a priori calibration with a short window of data. Estimation of Inertial Measurement Unit (IMU) parameters is a mature field, with state augmentation being a strong favorite for practical implementation, to the potential detriment of other approaches. A simple modification of the standard state augmentation technique for determining IMU location is presented that determines which model of an enumerated set best fits the measurements of this IMU. A neural network is also trained on batches of IMU and GNSS data to identify the lever arm of the IMU. A comparison of these techniques is performed and it is demonstrated on simulated data that state augmentation outperforms these other methods.en_US
dc.description.departmentAerospace Engineeringen_US
dc.identifier.citationNick Montalbano, and Todd E. Humphreys "A Comparison of Methods for Online Lever Arm Estimation in GPS/INS Integration," in Proceedings of the IEEE/ION PLANS Meeting, Monterey, CA, 2018.en_US
dc.identifier.urihttps://hdl.handle.net/2152/82437
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/9442
dc.language.isoengen_US
dc.relation.ispartofRadionavigation Laboratory Conference Proceedingsen_US
dc.rights.restrictionOpenen_US
dc.subjectGPS/INS integrationen_US
dc.subjectneural networken_US
dc.titleA Comparison of Methods for Online Lever Arm Estimation in GPS/INS Integrationen_US
dc.typeConference paperen_US

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