Research On Key Technologies Of Inertial Navigation System Performance Enhancement In GNSS Denial Environment

The great changes in the battlefield situation and operational environment brought by the technology of anti-intervention/anti-regional blockade have made the performance of GNSS badly weakened in wartime.In order to improve the ability of autonomous navigation of new combat weapons,such as hypersonic vehicle and micro-UAV,in the GNSS denial environment,enhancing the performance of the airborne SINS is an important means to realize the precise navigation and guidance of the new combat weapons.In order to comprehensively improve the accuracy and reliability of SINS,this paper has carried out corresponding research work from three aspects,namely,the improvement of SINS algorithm accuracy and real-time performance,the improvement of online using precision of inertial devices,and the enhancement of the performance of IMU.In the GNSS denial environment,the existing problem is that the accumulated error of SINS can not be compensated.Thus,the key factors that affect the accuracy and reliability of SINS are analyzed according to its working principle,so as to enhance its performance.Therefore,a new method of attitude updating optimization based on multi time scale is proposed to meet the special requirements for SINS accuracy and real time in the GNSS denial and high dynamic environment,which subdivides the attitude calculation loop into small loops with different computation frequencies,so as to improve the calculation accuracy and real-time performance of SINS algorithm.Moreover,it is presented a spiral vector integral optimization algorithm for compensating coning and sculling error,which uses the trapezoidal digital integral mode to reduce the calculation error of equivalent spiral vector and further improve the accuracy of SINS algorithm.In order to improve the on-line accuracy of inertial sensors,the research on accurate identification and modeling of stochastic errors of inertial devices is carried out in this paper.In view of a large amount of computation and low computational efficiency of the traditional ALLAN variance method based on the five parameter model,when it is used to analyze the long-time-correlated stochastic noise parameters of inertial devices,an improved optimization of ALLAN variance method based on the fitting model between partitions is proposed,which improves the fitting efficiency of the random noise parameters of inertial devices.When the least squares or weighted least squares are used to fit the parameters in the ALLAN variance method,there is a problem that the variance of the parameter estimation error is unknown and can not satisfy the condition of the same variance.Thus,an iterative weighted least squares fitting method is proposed,which can adaptively adjust the fitting weights and so as to effectively improve the fitting accuracy of the noise parameters of the traditional ALLAN variance method.The deterministic error of inertial sensors will change with the use time,the carrier maneuver and the external environment impact in the actual use.For this problem,it is researched that the online calibration of inertial device errors.By setting up the state and measurement equations based on multiplicative quaternion error,the online calibration accuracy of bias,scale factor and installation error in the inertial device is improved,which can analyze the observability of the same calibration error parameter in different maneuvering states,by extracting the independent observability of the state variables.By designing the online calibration dynamic track scheme,all the deterministic errors of inertial sensors are calibrated accurately,so that the accuracy of SINS is improved in the GNSS denial environment.In order to further improve the precision and reliability of SINS in GNSS denial condition,and guided by the optimization criteria for the accuracy and reliability evaluation of the redundant inertial devices,it is designed that an array IMU redundancy configuration scheme with optimized accuracy,reliability and space occupation ratio.Based on the design of three axis redundancy IMU array layout,the EKF and maximum likelihood estimation method are used to fuse and estimate the angular rate and the specific force of the array IMU,which improve the accuracy and enhance the reliability of SINS,by effectively utilizing the output information of the sensors in the array IMU.Finally,a comprehensive verification platform is set up for verifying strapdown inertial navigation system performance enhancement technology in GNSS denial condition.On the platform,the improved SINS algorithm and the on-line calibration algorithm of IMU error parameters under high dynamic environment are systematically verified by digital simulation.Based on the designed array redundant MEMS IMU module,the array redundancy IMU data fusion method is verified by a car experiment.The verification results show that the related algorithm can effectively improve the accuracy and reliability of SINS in the GNSS denial environment,and enhance the performance of SINS in complex flight environment.