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Abstract

The advance of MEMS-based inertial sensors successfully expands their applications to small unmanned aerial vehicles (UAV), thus resulting in the challenge of reliable and accurate in-flight alignment for airborne MEMS-based inertial navigation system (INS). In order to strengthen the rapid response capability for UAVs, this paper proposes a robust in-flight alignment scheme for airborne MEMS-INS aided by global navigation satellite system (GNSS). Aggravated by noisy MEMS sensors and complicated flight dynamics, a rotation-vector-based attitude determination method is devised to tackle the in-flight coarse alignment problem, and the technique of innovation-based robust Kalman filtering is used to handle the adverse impacts of measurement outliers in GNSS solutions. The results of flight test have indicated that the proposed alignment approach can accomplish accurate and reliable in-flight alignment in cases of measurement outliers, which has a significant performance improvement compared with its traditional counterparts.
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Abstract

The paper presents a method of developing a variable structure measurement system with intelligent components for flight vehicles. In order to find a distinguishing feature of a variable structure, a numerical criterion for selecting measuring sensors is proposed by quantifying the observability of different states of the system. Based on the Peter K. Anokhin’s theory of functional systems, a mechanism of “action acceptor” is built with intelligent components, e.g. self-organization algorithms. In this mechanism, firstly, prediction models of system states are constructed using self-organization algorithms; secondly, the predicted and measured values are compared; thirdly, an optimal structure of the measurement system is finally determined based on the results of comparison. According to the results of simulation with practical data and experiments obtained during field tests, the novel developed measurement system has the properties of high-accuracy, reliable operation and fault tolerance.
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