A Novel Polarized Skylight Navigation Model for Bionic Navigation With Marginalized Unscented Kalman Filter

Abstract

Bionic navigation is an essential technology in GPS-denied environment for vehicle navigation. This paper combined strapdown inertial navigation system, polarized skylight sensors and odometer to design a new navigation model. In particular, a novel measurement model is developed based on polarized skylight. This model primarily utilizes the cross product to compute the error between the measured polarization vector and the theoretical polarization vector. It differs from the error computed by strapdown inertial navigation system in design vector measurement model. It deals with the problem of the directional ambiguity of polarization vector difference directly. Considering the measured polarization vector error due to computation and multiple scattering from atmospheric molecules, the states are augmented with the measured vector error to form a partially nonlinear, bionic integrated navigation model. To reduce the computation burden, marginalised unscented Kalman filter is presented to estimate the unknown states. The observability analysis method of piece-wise constant systems is applied to compute the observability matrix and singular value. Simulation results show that linear maneuver and angular maneuver can improve the degree of the observability of attitude and measured polarized vector error. Finally, experiments further verify the effectiveness of proposed models and method.