Robust partly strong tracking consider SDRE filter for direct INS/GNSS integration with biases
Taishan Lou, Xiaoqian Wang, Hongmei Zhao, Zhiwu Chen · Measurement Science and Technology · 2020
Abstract In order to counteract the adverse effects of biases in direct inertial navigation system/global navigation satellite system integration, a novel robust partly strong tracking consider state-dependent Riccati equation filter (PSTCSDREF) algorithm is proposed. A nonlinear ‘consider’ approach is utilized to incorporate bias statistics into a state estimation error covariance of the state-dependent Riccati equation filter (SDREF), and a new consider SDREF (CSDREF) is proposed. Next, the prediction covariance of the state is partly multiplied by a strong tracking factor, which does not include bias covariance, so as to mitigate the adverse effects of model uncertainties on navigation performance. Numerical simulation demonstrates that the proposed PSTCSDREF demonstrates better navigational accuracy as compared with the extended Kalman filter, SDREF, and CSDREF.