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- W4200611937 abstract "The desensitized Kalman filter Karlgaard and Shen (2013)[1] is a practical and intuitive robust filtering method. However, a thorough analysis of its stability and impact of assumptions is missing. This paper expands the theory of desensitized Kalman filtering by proposing a stochastic approach to reduce estimation error sensitivity to parameters. The novel approach leads to the exact desensitized Kalman filter that does not neglect the gain sensitivity to a parameter. The suboptimal form equivalent to the original desensitized Kalman filter in a special form is proposed. The stability analysis and the definition of stability conditions are possible due to the proposed form that can be interpreted as the Kalman filter with correlated process and measurement noise with time-variant statistics. Furthermore, adaptive normalization of objectives is introduced, which improves the desensitizing performance." @default.
- W4200611937 created "2021-12-31" @default.
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- W4200611937 date "2022-02-01" @default.
- W4200611937 modified "2023-10-14" @default.
- W4200611937 title "Reduction of prediction error sensitivity to parameters in Kalman filter" @default.
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- W4200611937 doi "https://doi.org/10.1016/j.jfranklin.2021.12.019" @default.
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