High Precision And Fast Initial Alignment Technology Research Based On The Airborne Strapdown Inertial Navigation System

The purpose of the initial alignment of the airborne strapdown inertial navigation system is used to determine the relationship between the body coordinate system corresponding to the inertial measurement unit and the navigation coordinate system used by the inertial navigation system.As the requirement of the speed and the mobility has become much higher in modern warfare,automatically determining the attitude of the aircraft to improve the aircraft's initial alignment performance and efficiency is one of necessary conditions to achieve the precise navigation and guidance.This paper titled “High Precision and Fast Initial Alignment Technology Research Based on the Airborne Strapdown Inertial Navigation System”aims at improving the alignment accuracy of the system and shortening the system alignment time.It is respectively carried out in the following three reaserch: ground alignment,air alignment and transfer alignment of airborne strapdown inertial navigation system.When the aircraft starts on the ground,the conditions of the initial alignment of the strapdown inertial navigation system are strictly limited.Firstly,the initial alignment technique of the airborne strapdown inertial navigation system is studied.The expansion state observer in the auto-disturbance-rejection control technology is introduced,which breaks through the limitation of the traditional method for noise and estimates the misalignment angle by using control method.In addition,the global navigation satellite system is needed to assist the ground alignment due to urgent tasks.When the problems of the signal block arise,the method based on the motion constraints of the strapdown inertial navigation system ground alignment is proposed.The virtual observer model is constructed by using the motion constraint model of the aircraft.On one hand,when the GNSS signal is good,both of them work together;On the other hand,when the GNSS signal is lost or faulty,the motion constraint model can continue to assist the strapdown inertial navigation system for ground moving base alignment.Thus,reducing the alignment time while improving alignment accuracy.In case of emergency tasks,the aircraft can be aligned in the air on the basis of rapid ground alignment,considering the limitation the of the ground alignment time and accuracy of the airborne strapdown inertial navigation system.In this paper,the initial alignment method on moving base using the multi-auxiliary information is put forward because the initial alignment under the single auxiliary information takes too long and the accuracy is not high.However,since the system with the aid of the multi-auxiliary information is susceptible to varieties of factors,the air alignment method with the neural network combined with the federal filter is proposed.The estimation ability of the federal filter and the learning ability of the neural network are used to improve the robustness and precision of the air alignment algorithm.In addition,the initial alignment method using the multi-auxiliary information based on the factor graph probability model is studied because the alignment filter can not be adjusted adaptively with the change of environmental task.Based on the structure of the factor graph model,the sensor is abstracted into different factor nodes to construct the chain filter structure.The factor nodes can be dynamically adjusted to realize the effective use of multi-source asynchronous information.Therefore,the accuracy,reliability and adaptability of air alignment of the airborne strapdown inertial navigation system are comprehensively improved in two aspects.The transfer alignment method is an effective way to improve the alignment performance of airborne slave strapdown inertial navigation system.However,due to the installed position,flexural deformation and other effects,the performance of the transfer alignment can be affected.In this paper,multiple factor errors influencing the transfer alignment are analyzed and compensated.With the observability analysis method of the singular value decomposition,an adaptive transfer alignment method based on observability analysis is proposed.The Sage-Husa adaptive filtering algorithm is simplified and the fading factor is constructed with the state observability in the method.The measurement noise is estimated by using the time-varying noise estimator,which can improve the noise tolerance of the system.The influence of the state with low observability on the filter performance is weakened by use of the fading factor.The method can enhance the adaptability of the transfer alignment and improve the alignment accuracy and speed.The high speed data communication network has greatly promoted the development of airborne inertial network transfer alignment technology.Based on the distributed inertial navigation sensor,the inertial network transfer alignment has become an important research to improve the alignment performance of the system.In this paper,an airborne inertial network system for data sharing is constructed.Compared with the traditional transfer alignment algorithm,the accuracy and reliability of transfer alignment are improved.In addition,the system fault detection method based on inertial network structure is studied.With the high fault tolerance and robustness of the inertial network,the method can effectively improve the fault tolerance of the system caused by the failure of individual inertial nodes.Finally,the visualization software is used to build a simulation platform for airborne strapdown inertial navigation system alignment algorithm,including ground rapid alignment,multi-information assisted air alignment and transfer alignment of airborne strapdown inertial navigation system.The results show that the algorithm in the paper can effectively improve the alignment performance of airborne strapdown inertial navigation system,which can lay the foundation for improving the alignment accuracy and reliability of advanced military aircraft and its airborne carrier inertial navigation system.