| Citation: | YE Wen, CAI Chenguang, YANG Ping, et al. UKF estimation method incorporating Gaussian process regression[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(6): 1081-1087. doi: 10.13700/j.bh.1001-5965.2018.0591(in Chinese)
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The high-precision filter estimation is a key technology in the SINS/GNSS integrated navigation system, and its estimation accuracy has direct influence on the accuracy of navigation. The traditional filter estimation method is based on inertial navigation error model, and does not take its uncertainty into account. Aimed at the problem, a high-precision filter estimation method is presented, which uses Gaussian process regression (GPR) to enhance the capabilities of prediction and estimation for parametric unscented Kalman filter (UKF). On one hand, it can estimate the state vector of the nonlinear parametric UKF on condition that trained data is limited; on the other hand, GPR can also take both the noise and the uncertainty in the nonlinear parametric UKF into consideration. The real vehicle-mounted experiment results show that the proposed method can effectively enhance filter estimation precision through applying the enhanced GPR-UKF into the SINS/GNSS integrated navigation system.
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