INS/CNS Deeply Integrated Navigation Method of Near Space Vehicles

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 20; p. 5885
• Main Authors: Mu, Rongjun, Sun, Hongchi, Li, Yuntian, Cui, Naigang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 17.10.2020; MDPI
• Subjects: Accuracy; Aerospace environments; Aircraft; Algorithms; Attitudes; Celestial navigation; Dynamic response; Efficiency; Error functions; H infinity; H-infinity filter; Identification methods; integrated navigation; Navigation systems; near space vehicles; Noise; Noise measurement; Sensors; Space vehicles; Vehicles
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20205885

Celestial navigation is required to improve the long-term accuracy preservation capability of near space vehicles. However, it takes a long time for traditional celestial navigation methods to identify the star map, which limits the improvement of the dynamic response ability. Meanwhile, the aero-optical effects caused by the near space environment can lead to the colorization of measurement noise, which affects the accuracy of the integrated navigation filter. In this paper, an INS/CNS deeply integrated navigation method, which includes a deeply integrated model and a second-order state augmented H-infinity filter, is proposed to solve these problems. The INS/CNS deeply integrated navigation model optimizes the attitude based on the gray image error function, which can estimate the attitude without star identification. The second-order state augmented H-infinity filter uses the state augmentation algorithm to whiten the measurement noise caused by the aero-optical effect, which can effectively improve the estimation accuracy of the H-infinity filter in the near space environment. Simulation results show that the proposed INS/CNS deeply integrated navigation method can reduce the computational cost by 50%, while the attitude accuracy is kept within 10” (3 σ). The attitude root mean square of the second-order state augmented H-infinity filter does not exceed 5”, even when the parameter error increases to 50%, in the near space environment. Therefore, the INS/CNS deeply integrated navigation method can effectively improve the rapid response ability of the navigation system and the filtering accuracy in the near space environment, providing a reference for the future design of near space vehicle navigation systems.