Yongyun Zhu1, Tao Zhang1,*, Mohan Li2, Di Wang1, Shaoen Wu3
CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1305-1321, 2019, DOI:10.32604/cmc.2019.06406
Abstract The observation vectors in traditional coarse alignment contain random noise caused by the errors of inertial instruments, which will slow down the convergence rate. To solve the above problem, a real-time noise reduction method, sliding fixed-interval least squares (SFI-LS), is devised to depress the noise in the observation vectors. In this paper, the least square method, improved by a sliding fixed-interval approach, is applied for the real-time noise reduction. In order to achieve a better-performed coarse alignment, the proposed method is utilized to de-noise the random noise in observation vectors. First, the principles of proposed SFI-LS algorithm and coarse alignment… More >
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