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- W4313201621 abstract "The central differential Kalman filter (CDKF) and square root central differential Kalman filter (SR-CDKF) are widely used methods for addressing nonlinear problems in target motion analysis. However, for the bearings-only target motion analysis by single observer, sometimes the CDKF and the SR-CDKF experience the divergence problem or even filter interruption due to the observer’s mobility and system noise. To solve these problems, SR-CDKF is improved, and an adaptive singular value decomposition square root center difference Kalman filter (ASVDSR-CDKF) is proposed. The covariance square root update method based on the singular value decomposition method is deduced. The error discriminant statistics and adaptive factors are constructed. When the disturbance is too large, the adaptive factor is automatically adjusted and the square root updating form is selected. The simulation of bearings only target motion analysis is carried out under three different conditions, and the performance of CDKF, SR-CDKF, square root unscented Kalman filter (SR-UKF) and the proposed ASVDSR-CDKF method are compared. The simulation results demonstrate that the proposed method not only has high accuracy, but is also more stable than the other three methods." @default.
- W4313201621 created "2023-01-06" @default.
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- W4313201621 date "2022-01-01" @default.
- W4313201621 modified "2023-09-27" @default.
- W4313201621 title "A Method of Square Root Central Difference Kalman Filter for Target Motion Analysis" @default.
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- W4313201621 doi "https://doi.org/10.1007/978-981-19-9195-0_27" @default.
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