Research On Key Technology Of Integrated Navigation System In Land Vehicle

The application environment of military vehicle is very complex,in order to meet the needs of rapid mobile launch of weapon system,navigation system should have good performance such as high accuracy, high reliability, autonomy and anti-jamming. Any single navigation system is hard to meet this requirement, so multi-sensor information fusion technology has been the main technical means of navigation systems. In this paper, considering military platform as application background, aiming as to improve the navigation system accuracy and the ability to adapt complex environment and high reliability, SINS system, GPS system and EC integrated navigation system are designed. Moreover, this paper did some research on their key technologies. The main research works of this paper are demonstrated as follows:Based on FPGA and ARM, an integrated navigation system hardware platform of MSINS/GPS/EC is designed. The performance of the system has been tested by experiments. Experimental verification is done respectively and overall calibration of MIMU. Random noise of gyroscope has been analyzed and identified with Allan variance, which contributes to grasp the noise characteristics completely and comprehensively. In order to solve the problem of the system accuracy which is influenced enormously by MEMS random drift error, a gyro random noise AR model has been established. Also, state augmenting method is proposed to design Kalman filter, which is used to filter the random noise. After testing and verification this approach is able to greatly reduce the random noise of gyro, moreover, it lays the foundation for improving the system accuracy.The observable degree of SINS has been studied. The Singular Value Decomposition(SVD) method of observability matrix is widely used in the field. However, this method could only analysis the estimate accuracy of state vector, but not the convergence rate. In order to solve this problem, Relative Principal Component Analysis (RPCA) algorithm is introduced, and a new observable degree analysis algorithm based on RPCA is proposed. The estimate accuracy and estimate velocity of observable degree is distinguished. Obtain the two estimates mentioned respectively, so the observable degree information of state could become more completely and comprehensively. Analysis from the physical meaning of singular value, simplified the solution of observable degree. Simulations and experimental results demonstrate the effectiveness of this method. Moreover, it is simpler and easier to implement compared with the traditional SVD method.The data fusion method of integrated navigation system has been studied. A Federal Kalman filter has been applied in SINS/GPS/EC integrated navigation system. Considering the low accuracy of short time output of GPS and EC, measurement averaging method is introduced. However, the average time is hard to determine in conventional method. In order to solve this problem, a new algorithm combined strong tracking kalman filter with convergence criterion of tilter is proposed. This method can reduce observable noise and improve the accuracy of system without any loss of the filter divergence.Due to GPS and EC is vulnerable to interference; this paper studied Navigation algorithm for low-precision inertial when it works alone. Vehicle model and movement characteristics are introduced as constraint information to improve system accuracy. At first, considering the lateral and vertical velocity of vehicle is usually zero, the constraint information was introduced as virtual measurement to design filters, which can estimate state vector and largely improve the system accuracy. Secondly, in order to reduce the rapid growth error of azimuth, HDR algorithm is introduced. A closed loop network is designed to estimate and compensate the random drift of azimuth gyroscope real-time. At last, Interactive Multiple Model (IMM) is proposed to apply in SINS, with the equations of vehicle motion as system equation and output of SINS as measurement. Simulation and experimental results demonstrate the algorithm can further improve the system accuracy.