A method for accurate estimation of altitude in re-entry vehicles using flush air data sensing system (FADS)

Krishna, Shashi; Satheesan, Arun; Vidya, Shrikant; Finitha, K C; Remesh, N; Jayakumar, M; Mohan, N. Shyam

doi:10.1109/aicera.2014.6908179

Cited by 1 publication

(1 citation statement)

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“…Two sets of the gyroscope drift and accelerometer bias parameters are provided. The vertical speed and altitude is assisted by a barometric altimeter with the noise of 30 m. 25 A simulated typical maneuver flight trajectory is shown in Figure 3, which consists of different kinds of flight conditions such as take-off, climbing, varying speed, horizontal flying, turning, and so on. Moreover, a hypersonic flight trajectory of HCV is simulated with the reference of X-43 second flight test, 26,27 as shown in Figure 4.…”

Section: Simulation Distributionmentioning

confidence: 99%

A new tightly-coupled INS/CNS integrated navigation algorithm with weighted multi-stars observations

Wang

Xiong

Liu

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part G:

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Integrating celestial navigation system (CNS) and inertial navigation system (INS) is an effective way to realize autonomous navigation for hypersonic cruise vehicles. In multi-stars synchronous observation, the error levels of starlight observations are not only changing with time, but also different from star to star, which is ignored by classic INS/CNS integrated navigation. In this paper, a new tightly-coupled INS/CNS integrated navigation algorithm with weighted multistars observations is proposed. Different from the classic method, tightly-coupled model provides a novel framework for INS/CNS integration, which enables direct fusion of INS measurements with original CNS observations. Meanwhile, star subsets traversal strategy is designed to evaluate the error level of each observation. Then adaptive filter which can fuse multi-star observations with INS according to evaluated error levels is researched. The proposed new algorithm shows significant improvement in navigation accuracy than the classic method in simulation.

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Section: Simulation Distributionmentioning

confidence: 99%