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Aiming at the out-of-sequence measurement (OOSM) problem, the update equations of the nonlinear single-step-lag OOSM are derived based on the existing methods. By introducing the unscented transformation (UT), the covariance between state vector and corresponding measurement vector are computed such that the single-step-lag OOSM can be effectively solved under the nonlinear Gaussian system with nonlinear measurement equation and linear dynamic equation. Furthermore, a single-step-lag OOSM fusion algorithm based on UT is presented to confront the problem of the single-step-lag OOSM in multi-sensor system. The proposed algorithm has some advantages over the EKF A1 based on the extended Kalman filter frame and the optimal method without lags. For example, it can be used when the Jacobian matrix or the Hessian matrix of nonlinear measurement equation is nonexistent; its filtering performance is better; and its complexity has the same order of magnitude as that of the EKF A1 algorithm.
Aiming at the out-of-sequence measurement (OOSM) problem, the update equations of the nonlinear single-step-lag OOSM are derived based on the existing methods. By introducing the unscented transformation (UT), the covariance between state vectors and corresponding measurement vector are computed such that the single-step-lag OOSM can be capable solved under the nonlinear system with nonlinear measurement equation and linear dynamic equation. Furthermore, a single-step-lag OOSM fusion algorithm based on UT is presented to confront the problem of the single-step-lag OOSM in multi-sensor system. The proposed algorithm has some advantages over the EKF A1 based on the extended Kalman filter frame and the optimal method without lags. For example, it can be used when the Jacobian matrix or the Hessian matrix of nonlinear measurement equation is nonexistent; its filtering performance is better; and its complexity has the same order of magnitude as that of the EKF A1 algorithm.