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Yu X.-T.,Aerospace Science and Industry Inertial Technology Co. | Tang M.,The Navy Military Representative Office Stationed in the Third Institute of CASIC | Zhang L.,Aerospace Science and Industry Inertial Technology Co. | Guo L.-R.,Aerospace Science and Industry Inertial Technology Co.
Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology | Year: 2012

To solve the problem that the accelerometer data is affected by the accelerometer's mechanical noise, circuit noise and ambient noise, a data de-noising method was proposed, in which the morphological filters are combined with opening-closing and closing-opening models to eliminate the noise, and the power spectral density was used to analyze the filtered data. First, an accelerometer data acquisition scheme was designed, and the test data was acquired with the data acquisition system. Secondly the data was filtered by the morphological filter. Thirdly, the power spectral density of the acceleration was calculated by the Welch's Method. The calculation results show that there are protuberant peak values at 50 Hz and the frequencies that are odd times of 50 Hz. Finally, the root mean square of the acceleration's power spectral density was calculated. The results demonstrate that the cumulative root mean square of the power spectral density after de-noising is approximately 3 dB less compared with the one without the de-noising process.


Yu X.,Aerospace Science and Industry Inertial Technology Co. | Zhang L.,Aerospace Science and Industry Inertial Technology Co. | Guo L.,Aerospace Science and Industry Inertial Technology Co. | Zhou F.,Aerospace Science and Industry Inertial Technology Co.
Journal of Control Theory and Applications | Year: 2012

The impact of temperature on accelerometer will directly influence the precision of the inertial navigation system (INS). To eliminate the measurement error of accelerometer, this paper proposes a proximal support vector regression (PSVR) algorithm for generating a linear or nonlinear regression which requires the solution to single system of linear equations. PSVR is used to identify the static temperature model of the accelerometer. In order to improve the identifying performance, the kernel parameters and penalty factors of PSVR are optimized by the canonical particle swarm optimization (CPSO). The experiments under different temperature conditions were conducted. The experimental results show that the proposed PSVR can correctly identify the static temperature model of quartz flexure accelerometer and is more efficient than those of the standard SVR and least square algorithm. © 2012 South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.

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