Design Of Intelligent Vehicle Integrated Navigation System And Research On Advanced Information Fusion Algorithm

As a new type of integrated system which is based on the functions of navigation,positioning,decision making and assistant driving,Intelligent Vehicle is the future direction of vehicles.In order to improve positioning accuracy of the integrated navigation system of Intelligent Vehicle,this paper makes an in-depth study on the improved information fusion algorithm of INS/GPS integrated navigation system.Aiming at the divergence problem of Kalman filter caused by random change of the noise statistical characteristics,the paper proposes adaptive Kalman filtering algorithm based on fuzzy logic inference innovatively,and realizes the real-time adjustment of measurement noise covariance matrix R and process noise covariance matrix Q.In the paper,design scheme of the INS/GPS integrated navigation system is presented,and hardware and software of the system are programmed and designed based on the scheme.The mathematical model of the integrated navigation system is also constructed.Simulation results show that the proposed adaptive filtering algorithm based on fuzzy logic inference can effectively solve the divergence problem of Kalman filter in information fusion process of integrated navigation,improve positioning accuracy of the integrated navigation system,enhance anti-jamming capability and dynamic tracking performance of the system,and have strong theoretical value and practical significance.The primary work and contributions can be summarized as follows:Firstly,the paper introduces the development history and research status of the Intelligent Vehicle navigation system and the multi-sensor information fusion technology.Secondly,principle,performance and error source of navigation of the INS/GPS system are analyzed in detail.The integrated navigation scheme is selected,and the principle and structure of the scheme are explained as follows:Loosely-Coupled Integration mode based on position and velocity,using indirect filtering method and output-correction mode.Thirdly,mathematical model of the INS/GPS integrated navigation system isconstructed,and the state equation and measurement equation of the system are discretized.Fourthly,in order to solve the divergence problem of filtering caused by the random change of noise statistical characteristics,the paper studies the fuzzy inference system,and analyzes its structure,design concept and controlling performance.Finally,the paper proposes an adaptive filtering algorithm based on fuzzy logic inference and introduces an improved double fuzzy inference mechanism.It also proposes an adaptive exponentially weighted filtering algorithm based on fuzzy logic inference in order to improve the performance of exponentially weighted filtering.Simulation results show that the proposed adaptive filtering algorithm based on fuzzy logic inference can effectively improve the filtering accuracy and dynamic tracking performance.