An Adaptive Filtering-disturbance Observer-based State Estimation Algorithm for Large Ships
In order to meet the state estimation requirements of large ship targets such as aircraft carriers,an interactive multi-model strong compensating cubature Kalman filtering algorithm is proposed,which is formed by the fusion of nonlinear disturbance observer and strong tracking cubature Kalman filtering algorithm.The nonlinear disturbance observer is introduced to estimate the total amount of disturbance caused by external uncertainties and prove the stability of the observer,and then the estimated disturbance value is used to modify the process parameters of the strong tracking cubature Kalman filter in real time,which finally forms the interactive multi-model strong compensating cubature Kalman filtering algorithm and completes the relatively accurate estimation of target state.The results show that the proposed filtering algorithm can complete the more accurate estimation of target state,and has higher estimation accuracy in the estimation of target position and velocity compared with the variable-structure multi-model particle filtering algorithm,the variable-structure multi-model unscented Kalman filtering algorithm,and the interactive multi-model strong tracking cubature Kalman filtering algorithm.
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