Cooperative positioning method based on iterated Kalman filter in sparse-beacon environments

YANG Jin-yi; GUO Yan; JIANG Peng-fei

doi:10.7641/CTA.2023.30272

Abstract：In recent years,the cooperative positioning of multiple unmanned platforms has been widely applied,and each platform can further improve its positioning accuracy by using range measurements and other mutual observations.In the inertial/ranging combined cooperative positioning system,it is generally necessary to have four or more known base stations in the environment to provide stable ranging,and to correct the accumulated positioning error of inertial navigation solution through mutual ranging of each platform.However,in some environments with large communication distances or occlusion,the range measurements received by the platforms are sparse,and the commonly used cooperative positioning method based on the error state extended Kalman filter has poor accuracy.This paper proposes an inertial/ranging cooperative positioning method based on the iterated Kalman filter.Based on the extended Kalman filtering for error states,the iterative calculation of error states during the filtering update process is carried out with ranging accuracy as the threshold,reducing the nonlinear error caused by the omission of high-order Taylor expansion terms by the extended Kalman filtering,and thereby improving the positioning accuracy of each node participating in the cooperation.Simulation and physical experiments have verified the effectiveness of the proposed method in different sparse-ranging environments.