Extended Kalman filter (EKF) is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering (PF) is introduced. PF can yield better performance than that o...Extended Kalman filter (EKF) is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering (PF) is introduced. PF can yield better performance than that of EKF, because PF does not involve the linearization approximating to nonlinear systems,that is required by the EKF. PF has been shown to be a superior alternative to the EKF in a variety of applications. The base idea of PF is the approximation of relevant probability distributions using the concepts of sequential importance sampling and approximation of probability distributions using a set of discrete random samples with associated weights. PF methods still need to be improved in the aspects of accuracy and calculating speed.展开更多
A marine INS/GPS adaptive navigation system is presented in this paper. GPS with two antenna providing vessel’s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid sy...A marine INS/GPS adaptive navigation system is presented in this paper. GPS with two antenna providing vessel’s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter (SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.展开更多
文摘Extended Kalman filter (EKF) is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering (PF) is introduced. PF can yield better performance than that of EKF, because PF does not involve the linearization approximating to nonlinear systems,that is required by the EKF. PF has been shown to be a superior alternative to the EKF in a variety of applications. The base idea of PF is the approximation of relevant probability distributions using the concepts of sequential importance sampling and approximation of probability distributions using a set of discrete random samples with associated weights. PF methods still need to be improved in the aspects of accuracy and calculating speed.
基金This project was supported by the National Natural Science Foundation of China (40125013 &40376011)
文摘A marine INS/GPS adaptive navigation system is presented in this paper. GPS with two antenna providing vessel’s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter (SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.
