A non-linear combination filtering algorithm in MINS/GNSS navigation system
Yue Peng, Yu Liu, Chengtao Cai · 2016
Considering the nonlinear and uncertainty in the MINS/GNSS navigation system, a nonlinear Sage-Husa noise maximum posterior estimator was designed. Since the estimator cannot solve problems both of system noise and observation, a Bi-parallel BP neural network controller is designed to approximate the estimator. Then an adaptive UKF algorithm based on Bi-parallel neural network is proposed. In the case of uncertain noise, the simulation and analysis were shown that data saturation was emerged in the preceding filtering algorithm. A strong tracking UKF algorithm based on variance inflation factor was combined with the preceding algorithm in some conversion condition. The simulation in the paper was shown that the combined adaptive nonlinear filtering algorithm could suppress divergence and ensure precision.