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- W2076177807 abstract "This paper presents an algorithm for a class of suitably constrained reduced-order filters which minimize the variance of the estimated variables. The algorithm generates both the filter gain history and the true estimation error covariance. The algorithm provides a quantitative criterion which can be used to measure the performance of any reduced-order estimator. Both continuous and discrete estimators are considered. Several examples are treated including an application of the technique to a hybrid navigation system of high order." @default.
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- W2076177807 date "1975-09-01" @default.
- W2076177807 modified "2023-09-26" @default.
- W2076177807 title "Applications of Mininum Variance Reduced-State Estimators" @default.
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- W2076177807 doi "https://doi.org/10.1109/taes.1975.307988" @default.
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