Nonlinear Filtering Algorithm Applied To Satellite Autonomous Naviation System

In the orbits, the satellite will be disturbed by known or/and unknown factors. Therefore, the accurate satellite orbit dynamics cannot be established. The uncertainties are summarized as uncertainty noise, uncertainty parameters, and random disturbance and so on. Furthermore, the measurements will be randomly delayed with the increase of the number of sensors on the satellite and the limited communications capability. So the filtering problems for the satellite autonomous navigation system with uncertainty noise, Non-Gaussian noise, uncertainty parameters, random disturbance and delayed sensor measurements are raised. In order to solve these problems, the paper is carried out the follow study:The filtering problem for the satellite autonomous navigation system with colored measurement noise and correlated noise is considered. Firstly, the colored measurement noise is converted into white noise. And then, optimal filtering framework based on the minimum mean square-error estimation was derived. Secondly, unscented transformation(UT) and square root factorization are applied to the calculation of the state estimation and covariance of the nonlinear system states. Finally, the proposed Square-Root Unscented Kalman Filter was applied to the satellite autonomous celestial navigation system, and the simulation results shows that it is effective.An improved Strong Track Square-Root Unscented Kalman Filter for the system with uncertainty interference was proposed. Firstly, the defects of Strong Track filter were analyzed in this paper. Secondly, double multi-adaptive factors were introduced to guarantee the covariance matrix symmetric positive definite and unscented transformation was applied to calculate the adaptive factor avoided the calculation of Jacobian matrix, and a modified square-root decomposition method was adopted, which improved the stability and computational efficiency of filter. At last, the proposed filter was applied to satellite autonomous celestial navigation systems, and simulation results show that it is effective.The robust filtering algorithm was studied in this paper. A robust Unscented Kalman filtering algorithm for nonlinear systems with uncertainty in model parameters or/and in the statistics of the noises. Firstly, the upper bound of the estimation error covariance matrix was found. And then, unscented transformation was applied to the calculation of the state estimation and covariance of the nonlinear system states. At last, the robust unscented kalman filtering algorithm was applied in x-ray pulsar based on satellite autonomous navigation systems, and the simulation results shows that it is effective. A robust Extend Kalman Filter for the nonlinear system with parameters uncertainty and both additive and multiplicative noises was proposed. Firstly, the error equations for state prediction and estimation were got. Further, the upper bound of state prediction and estimation error covariance matrix was found. Then, the gain matrix was designed by minimizing the upper bound on state estimation error covariance for all admissible uncertainties. Finally, the robust extend kalman filter was applied in x-ray pulsar based on satellite autonomous navigation systems, and the simulation results shows that it is effective.The filtering problem for nonlinear system with randomly delayed measurements, randomly interference and both additive and multiplicative noises were considered. An adaptive and robust kalman filter was designed. The upper bound of estimation error covariance matrix was calculated by introducing two different multiple adaptive factors. The gain matrix was obtained by minimizing the upper bound on state estimation error covariance and the adaptive factor was calculated based on the estimation of filter residual. The filtering algorithm meets two constraints. The filter was applied in the satellite autonomous navigation system on the bias of star sensor and optical navigation camera, and the simulation results shows that it is effective.