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# INS/CNS Deeply Integrated Navigation Method of Near Space Vehicles

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School of Aerospace Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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Sensors 2020, 20(20), 5885;
Received: 25 September 2020 / Revised: 14 October 2020 / Accepted: 15 October 2020 / Published: 17 October 2020
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. View Full-Text
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Mu, R.; Sun, H.; Li, Y.; Cui, N. INS/CNS Deeply Integrated Navigation Method of Near Space Vehicles. Sensors 2020, 20, 5885.