| In order to launch missile rapidly and movably under complex application environment, Information-based missile-launcher requests the navigation system not only to be accurate in providing the navigation information, but also to be high reliable, independent and anti-jamming. It is difficult to meet this requirement for any single or the simple combination navigation system. Therefore, the multi-sensors data fusion technology has provided the technical support for building a navigation platform with high quality. Using Information-based missile-launcher as the application background, aiming at improving Information-based missile-launcher positioning accuracy as well as the adaptive and the fault-tolerant ability in the complex environment, thorough research on the information fusion algorithm and INS/GPS performance enhancement technology based on multi-sensors was conducted. The major contents were summed up as follows:INS/GPS measurement noise statistical characteristics may differ with the application of environmental change, to deal with this problem, on the basis of Sage-Husa adaptive filtering and interactive multi-model estimation theory, two adaptive filtering algorithms are presented. One is fuzzy adaptive Kalman filtering algorithm. By monitoring the ratio between filter residual and actual residual, this algorithm modifies recursively the measurement noise covariance of Kalman Filtering online using the Fuzzy Inference System (FIS) to make the covariance close to real measurement covariance gradually. The other is adaptive interactive multiple model (AIMM) algorimth. AIMM combined IMM algorithm with the improved Sage-Husa adaptive filtering algorithm. AIMM algorithm can achieve the coverage of real situation through few sub-models, and the accuracy can be improved than IMM algorithm.The fuzzy adaptive Kalman information fusion algorithm based on confidence is presented. Firstly, fuzzy adaptive filter instead of the standard Kalman filter is used as sub-filter of federated Kalman filter, form fuzzy adaptive federated Kalman information fusion algorithm. The sub-filters can adaptive track the actual measurement noise statistical characteristics. Secondly, fuzzy adaptive Kalman information fusion algorithm is combined with method of confidence weighted. The algorithm on the one hand makes measurement noise covariance of sub-filters adaptive track actual measurements of noise statistics, on the other hand can automatically lower the confidence of low filter weights.To improve the accuracy of INS/GPS when GPS outages occur, kinematic constraints of land vehicle and road network constraints are regarded as two virtual sensors, used to enhance the performance of INS/GPS during GPS outages. Under ideal conditions, velocity of the vehicle in the plane perpendicular to the forward direction is zero, means there is no side slip. The constraints can be used to form measurements of a virtual sensor. Constraints of road network can be used to form measurements of the other virtual sensor based on map matching when the vehicle is on road networks. When GPS is unavailable, the two virtual sensors is used to aid INS, the accuracy of INS/GPS can be improved.Based on Matlab/RTW/xPC the Vehicle Integrated Navigation HWIL simulation platform is designed, the structure of one Matlab/Simulink/RTW host and two xPC target machine is adopted, and the real-time simulation of integrated navigation is achieved.Based on multi-sensor information fusion algorithm and the INS/GPS performance enhancement technology by virtual sensors, software and hardware of multi-sensor integrated navigation system are developed. The experimental results proved that the algorithms presented in this dissertation are valid and the multi-sensor integrated navigation system designed in this dissertation is practicable.
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