The Theory And Application Research Of Adaptive-Robust UKF For Satellite Integrated Navigation System

Recently, the navigation systems often used in the aircrafts include:the inertial navigation systems, the satellite navigation system, the Doppler navigation systems, the celestial navigation systems, and the terrain-aided navigation system, and so on. In the last 70's century, the integrated navigation system has been emerged in view of the shortcomings of the single navigation system. This new kind of navigation system has been used in the fields of the maritime, the aviation and the aerospace. With the development of the computer technology, especially the micro-computer technology and the modern-control theory, the integrated navigation system is becoming an important development direction of the modern navigation system. The main characteristic of the integrated system is that a variety of single navigation systems can be combined together using some algorithms and their advantages can be used to enforce the whole performance of the system. For many different forms of the integrated navigation systems, their typical functions include:Firstly, the surpass capability. The integrated navigation system has many additional functions which a single subsystem does not have, because it is can take full advantage of the sub-systems. Secondly, the complementary function. The integrated system has the expanded scope of the application, because of the combination of the information from the different subsystems. Thirdly, the redundancy capability. More signal information can be obtained from the subsystems to improve the reliability of the integrated system.The key technology of information processing phase in the integrated navigation system is the Kalman filtering algorithm. This processing algorithm estimates the system errors based on the Kalman filtering algorithm to correct the positioning errors of the integrated system. The main signal information is obtained from two or more subsystems' output to achieve a comprehensive purpose. Currently, many researchers at home and abroad have studied the Kalman filtering algorithm used in the satellite navigation and the satellite integrated navigation. The main research fields include robust Kalman filtering algorithm, adaptive Kalman filtering algorithm, as well as their improved algorithm. However, few or no analysis has been focused on the robust Unscented Kalman filtering (UKF) algorithm and the adaptive UKF algorithm.This dissertation focuses on the application of the UKF in the GPS/SINS Integrated Navigation System, its positioning accuracy and the advantages and disadvantages of this filtering algorithm. The dissertation puts forward an improved robust-UKF algorithm based on the main disadvantages of the standard-UKF algorithm, which is combining the adaptive-estimation theory into the robust filtering algorithm. The main contributions of the dissertation include:·Firstly, the principles of the satellite integrated navigation system were studied. An improved system tight-combination method is presented, which is based on a full portfolio of hardware and software combination method. This new method is used in the GPS/SINS navigation system to become the main experimental platform of the filtering algorithm. The advantage of our new combination method is that it can simplify the hardware-designing process, as well as improve the software applications. At the same time, a detailed analysis of GPS/SINS, GPS/DR, GPS/INS/TAN and other integrated systems has been done to study their combination principle and combination chart.·Secondly, some filtering algorithm currently used in the satellite integrated navigation system are introduced, including the Kalman filtering algorithm and its improved algorithms, the particle filtering algorithm, the Federal filtering algorithm, the Sage filtering algorithm, the adaptive filtering algorithm, the robust filtering algorithm, and the intelligent filtering algorithm. A detailed analysis to the advantages and disadvantages of these filtering algorithms has been done, based on the practical application environment of the satellite navigation system.·Thirdly, the basic theory of the standard Kalman filtering algorithm is studied deeply. Several improved Kalman filtering algorithms are analyzed, including the Extended Kalman filtering algorithm (EKF), the Unscented Kalman filtering algorithm (UKF), the Federal Kalman filtering algorithm, the robust Kalman filtering algorithm and the adaptive Kalman filtering algorithm. After summarizing the performance advantages and disadvantages of UKF, we can conclude that:the main advantages are the direct use of the non-linear system model, the avoidance of the function linear-error and no need to calculate the Jacobian matrix; the main disadvantage is the requirement of the priori known to the system mathematical model and noise statistics.·An adaptive UKF algorithm based on the standard UKF algorithm are proposed to solve the problems that the system's mahematic model is non-accuracy and the noise statistics is unknown. This new algorithm is applied to the GPS/SINS integrated navigation system to analyze its performance in detail, like positioning accuracy and calculation redundancy. Comparing with the standard UKF and the Robust UKF, our new adaptive UKF algorithm can improve the positioning accuracy of the satellite navigation system significantly.·A robust UKF algorithm based on the robust estimation theory is proposed. This new algorithm is mainly used to improve the poor positioning accuracy caused by the observation-rough errors. The classical estimation model of the standard Kalman filtering algorithm is used to reduce the accidental error. The presence of gross errors will impact the estimation results inevitably and even cause a significant positioning departure. Therefore, the main study point of the robust Kalman filtering algorithm is how to make the adjustment Kalman filter model itself has the ability of anti-gross error.·Last but not least, an adaptive-robust UKF filtering algorithm is presented, after comprehensively considering of the error caused by observing gross error factors and the model error caused by error factors. Our new UKF algorithm is applied to the BD/SINS and GPS/SINS integrated satellite navigation system respectively. The simulation results indicate that this improved algorithm can achieve a high positioning accuracy, while reducing the accuracy influencing factors caused by the gross error and dynamic error model.Currently, the problem exist in the satellite integrated navigation system is a hot scientific research topic, and the applications and prospects of this system are very broad. Therefore, it is a great practical significance to study how to improve the performance of filtering algorithms to improve the navigation accuracy of the integrated systems.