An Improved Fuzzy Adaptive Iterated SCKF for Cooperative Navigation

To further improve the positioning accuracy of the multiple unmanned surface vessel (USV) systems, we introduce an information fusion algorithm equipped with adaptive fuzzy logic (FZ) control. The FZ controller is designed for the filter to effectively respond to measurement noise amidst dynamic disturbances. To address the inefficient innovation updating of the measurements for the nonlinear model and the potential lack of error covariance positive definiteness, a maximum a posteriori-based iterated square-root cubature Kalman Filter (MISCKF) is employed. To fully utilize the relative information among the USVs, a local-cooperation navigation framework is proposed. The effectiveness of the proposed framework and the FZMISCKF is validated through simulations and experiments.