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Recursive Update Filtering for Nonlinear Estimation
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DOI：10.1109/TAC.2011.2178334.png)
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摘要
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Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. This work proposes a novel nonlinear estimator whose additional computational cost is comparable to (N - 1) EKF updates, where N is the number of recursions, a tuning parameter. The higher N the less the filter relies on the linearization assumption. A second algorithm is proposed with a differential update, which is equivalent to the recursive update as N tends to infinity.
Keyword:
Estimation
filtering
Kalman filtering
nonlinear estimation
recursive Kalman filter
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期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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A new method for the nonlinear transformation of means and covariances in filters and estimators
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