Relative navigation method based on modified likelihood filtering for unmanned aerial vehicle formation

A modified likelihood cubature Kalman filtering (ML-CKF) is proposed to solve the problem that the measurements of vision-based relative navigation sensor for unmanned aerial vehicle formation are randomly delayed by multiple steps. The measurement model is modified by the Bernoulli random variables to describe the random delay. The likelihood function of the filtering is calculated by marginalizing out the delay variable to extract accurate information from the delayed measurements. The third-degree spherical-radial rule is utilized to compute the Gaussian-weighted integrals for the nonlinear system. The proposed modified likelihood filtering has the property of adaptive filtering because the weighting factors of the filtering are tuned based on the characteristics of the received measurements. By utilizing the Rodrigues parameters to denote the attitude errors, the relative navigation filter of unmanned aerial vehicle formation is designed based on the ML-CKF. Simulation results indicate that the proposed filtering algorithm could accurately estimate the relative position, velocity and attitude between the leader and follower. Moreover, the estimation accuracy of ML-CKF is superior to cubature Kalman filtering and conventional randomly delayed filtering. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.