Proceedings ArticleDOI
Multisensor fusion for autonomous UAV navigation based on the Unscented Kalman Filter with Sequential Measurement Updates
Seung-Min Oh
- pp 217-222
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TLDR
The performance and error analysis of the integrated navigation system based on this new multisensor fusion filter are assessed in a realistic simulation environment by comparing performance with that of an existing Extended Kalman Filter-based navigation system.Citations
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References
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Journal ArticleDOI
A new method for the nonlinear transformation of means and covariances in filters and estimators
TL;DR: A new approach for generalizing the Kalman filter to nonlinear systems is described, which yields a filter that is more accurate than an extendedKalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter.
Sigma-point kalman filters for probabilistic inference in dynamic state-space models
TL;DR: This work has consistently shown that there are large performance benefits to be gained by applying Sigma-Point Kalman filters to areas where EKFs have been used as the de facto standard in the past, as well as in new areas where the use of the EKF is impossible.
Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor Fusion: Applications to Integrated Navigation
TL;DR: In this article, a probabilistic framework called Sigma-Point Kalman Filters (SPKF) was applied to the problem domain addressed by the extended Kalman Filter (EKF).
Sigma-Point Kalman Filters for Integrated Navigation
TL;DR: The improved state estimation performance of the SPKF is demonstrated by applying it to the problem of loosely coupled GPS/INS integration and an approximate 30% error reduction in both attitude and position estimates relative to the baseline EKF implementation is demonstrated.
Proceedings ArticleDOI
Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation
TL;DR: A probabilistic framework, called Sigma-point Kalman Filters (SPKF) was applied to the problem domain addressed by the extended Kalman Filter, and the SPKF-based sensor latency compensation technique is used to demonstrate the lagged GPS measurements.