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Volume 39 Issue 11
Nov.  2013
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Journal of Beijing University of Aeronautics and Astronautics  > 2013  >  39(11): 1426-1430.
Wang Zhongyu, Wang Qian, Fu Jihuaet al. Pressure multi-sensor data fusion and estimation of poor information based on bootstrap-fuzzy method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(11): 1426-1430. (in Chinese)
Citation: Wang Zhongyu, Wang Qian, Fu Jihuaet al. Pressure multi-sensor data fusion and estimation of poor information based on bootstrap-fuzzy method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(11): 1426-1430. (in Chinese)
shu

Pressure multi-sensor data fusion and estimation of poor information based on bootstrap-fuzzy method

  • 1.

    School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • 2. Institute of Crustal Dynamics, China Earthquake Administration, Beijing 100085, China

  • Received Date: 04 Jan 2013
  • Publish Date: 30 Nov 2013
    Abstract
  • Pressure multi-sensor data fusion and estimation of poor information is a common problem in the field of pressure measurement. Small measurement data obtained from multi-sensor for the data fusion and estimation makes the data processing much difficult. Different from the statistical methods, a novel model based on bootstrap-fuzzy method for pressure multi-sensor data fusion and estimation of poor information was presented. The pressure multi-sensor measurement data was processed by the bootstrap sampling. The bootstrap fusion sequence was derived from the bootstrap distribution. The true value and the interval of the pressure multi-sensor measurement data were estimated. Experimental results show the model has high accuracy and the data fusion sequence is in a good agreement with the original measurement data. The validity of the proposed method is examined.

     

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