Study On Autonomous Reconstruction Technology And Informaton Fusion Algorithms Of Vehicular Navigation System

Vehicular navigation is launched on its ways to intelligentization and visualization. The terms of GPS/INS integrated navigation is generally used on the conventional navigation plan whose theoretical approaches and applicational researches focus more on replacement or improvement of Kalman filtering algorithm only if the GPS and INS are both available. Subject to the coverage of the signal of GPS and the precision of the INS, however, how to improve the reliability and precision of the navigation system using low-cost system reconstruction scheme and intelligent information fusion algorithm is a research subject which needs urgent solution.With the supports of the 863 Program'Study on system reconstruction and intelligent information fusion algorithm of integrated navigation', this dissertation deeply studies the technique of vehicular navigation system reconstruction and the algorithm of information fusion. The main contents of this dissertation are as follows.Firstly, the technology of the fault detection and isolation of vehicular navigation is studied. The state signals of the sensors are conducted with the wavelet analysis in order to detect the malfunction in a short time, which is based on analyzing the problem of fault detection for the vehicular navigation system. Non-disturbance switch from the failure system to the non-fault system is executed using the fuzzy reasoning method, which can isolate the fault point, and then, the adaptive reconstruction of system is realized.Secondly, the point to line P-L Simultaneous Localization and Mapping (SLAM) algorithm which is based on the outdoor environment is researched. For the conflict between the computational complexity and the information richness mapping Algorithm which transforms the point features of environmental signs into the line characteristics is proposed to form a new method to express the information of outdoor environment. As starting with reducing the uncertainties of observable information, the state vectors of SLAM algorithm are estimated using the constraints of roads, which can reduce the positioning error and increase the map accuracy, in order to enhance the adaptive ability of map creation.Next, the issue of system reconstruction using low-cost compound mode when the signal of GPS is unavailable is worked. The system reconstruction programs based on the map building and synergic pseudo-satellite are designed and the feasibility is analyzed.Then, the technologies of noise adaptive based on the multi-model and nonlinear noise coupling are investigated to increase the accuracy of estimation of system states. Neural network-based adaptive factors are designed, which incorporate the Kalman filtering algorithm with the artificial intelligence technology, to adjust filter gain and improve the filter stability.Lastly, on the basis of the above research results, focusing on the procedure of the motor vehicle moving along a specific trajectory, the dynamic simulation is conducted, with acquiring, synchronizing and manipulating the signals of the satellite navigation and inertial navigation acquisition during vehicle moving, to illustrate the efficaciousness of the programs and algorithms which is proposed.