Robust Filtering For Integrated Navigation System

The requirements of precision and reliability for modern navigation systems become higher and higher. It demands that navigation systems can provide overall and precise information of navigation positioning and orientation, which shall not be restricted by climate, and have the properties of strong concealment and independence, and high anti-jamming performance. Up to now, there is no single navigation equipment that can meet these requirements. Alternatively, an appropriate combination of several navigation systems may solve this problem, which can compensate each to other and improve a system's precision and stability. Such integrated navigation systems turn out to be a development trend in navigation and in navigation systems in multi-sensor data fusion as well.Among algorithms in integrated navigation systems, Kalman filtering is the most successful method for the application of single sensor data fusion. However, Kalman filtering requires that the dynamic model of systems and statistical characteristics of state and measured noise must be exactly known. Kalman filtering is sensitive to system parameter and noise uncertainty, thus makes it difficult to converge. Developing along with robust control theory, robust filtering theory, especially H_∞filtering theory, primarily resolves the problem occurring in Kalman filtering in theory.In this thesis, the robust filtering problem of integrated navigation systems in context of GPS/DR is studied. Aiming at existing problems of uncertainty of model and uncertainty of noise, we analyze applicability and precision of filtering algorithm, filtering algorithm, and federated H_∞H 2 /H_∞H_∞filtering algorithm. The main study as follow. Firstly, several mathematic models in GPS/DR integrated navigation system are derived. Priority algorithm of H_∞filtering based on matrix theory is improved. Stable filtering algorithm based on LMI is developed. Robust Kalman filtering algorithm and robust filtering algorithm are applied to GPS/DR integrated navigation system, this is an attempt to develop the application of model uncertainty algorithm in integrated navigation system. Extended Kalman filtering and extended H filtering are applied to integrated navigation system based on current statistical nonlinear model.Secondly, mixed filtering algorithm under constraint of error covariance is derived. The results confirm the existence of infimum of traditional mixed filtering algorithm, and manifest its weakness inconvenient for engineering implement. Accordingly, we deduce new iterative mixed H 2 /H_∞filtering algorithm.Thirdly, aiming at the filter weakness of centralized parameter system and federated Kalman filter, we specifically propose federated H_∞filtering algorithm, which can overcome the weakness of federated Kalman filtering algorithm. By comparing it with federated H_∞filtering algorithm, we prove that they are united.Fourthly, experiment system of GPS/DR integrated navigation is built, practical experiments are done, and those algorithm in this paper are calculated off-line by practical data. The result of experiment proves that the algorithms in this paper are better than kalman filtering algorithm and applicable in practice.