A Novel Adaptive Sliding Observation-Based Cooperative Positioning Algorithm Under Factor Graph Framework for Multiple UUVs
Qingxin Wang, Shiwei Fan, Ya Zhang, Wei Gao, Jianxiong Wei, Yanyan Wang
Abstract
In the leader–follower-based multiple underwater unmanned vehicles (UUVs) cooperative positioning system, the precision of the compass and Doppler velocity log equipped with the follower UUV is low, which will inevitably lead to yaw error and velocity error (YEaVE) in the output data. These errors will seriously affect the positioning accuracy of follower UUV. In addition, the observation information often has many outliers due to the influence of underwater multipath, which will cause the increase of the positioning error. Herein, we propose a novel adaptive sliding observation-based cooperative positioning algorithm under the factor graph framework. First, the influence mechanism of YEaVE on positioning accuracy of the follower UUV is analyzed. Thereafter, under the factor graph framework, a cooperative positioning algorithm based on maximum correntropy criterion is designed in which the estimation and compensation of YEaVE are innovatively added. At last, an adaptive sliding observation method is designed to deal with outliers in observation information. The proposed algorithm is verified by simulations and shipboard experiment, and the results show that it can accurately estimate the YEaVE. At the same time, compared with the ordinary factor graph algorithm, the positioning accuracy of the proposed algorithm has been improved by 88.31% and 81.63% in simulation and shipboard experiment.