The model-driven rapid visual evaluation platform of inertial navigation error indexes

Jingying Zhang; Lin Wang; Zhen Xiao; Cong Xu; Xiaorui Wu; ZhiChao Xue; Changquan Qiu; Mingjian Pan

doi:10.1117/12.2627832

11 March 2022 The model-driven rapid visual evaluation platform of inertial navigation error indexes

Jingying Zhang, Lin Wang, Zhen Xiao, Cong Xu, Xiaorui Wu, ZhiChao Xue, Changquan Qiu, Mingjian Pan

Proceedings Volume 12160, International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021); 121600R (2022) https://doi.org/10.1117/12.2627832
Event: International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021), 2021, Sanya, China

Abstract

The combined guidance system based on inertial navigation is most widely used in the flying vehicle. In order to meet the demand of the rapid evaluation of the modern aircraft guidance system, this paper constructs a model-driven inertial navigation error index visual rapid evaluation platform. First of all, according to the typical error indicators and error transmission relations, the error transmission mathematical model of impact point error is established. Then, the initial visual software debugging interface is realized by C language. Finally, depending on Modelica language for optimized programming, a comprehensive platform for the rapid evaluation of the inertial navigation error index is established. Through real-time calculation of ballistic data, the analysis of impact point error under the combinations of different inertial navigation error indexes is quickly completed. The simulation results show that the design efficiency is greatly improved and the platform lays a good foundation for the development of aircraft.

Citation Download Citation

Jingying Zhang, Lin Wang, Zhen Xiao, Cong Xu, Xiaorui Wu, ZhiChao Xue, Changquan Qiu, and Mingjian Pan "The model-driven rapid visual evaluation platform of inertial navigation error indexes", Proc. SPIE 12160, International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021), 121600R (11 March 2022); https://doi.org/10.1117/12.2627832

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