乏信息多传感器压力数据自助模糊融合估计

王中宇; 王倩; 付继华

乏信息多传感器压力数据自助模糊融合估计

1.
北京航空航天大学仪器科学与光电工程学院, 北京 100191
2. 中国地震局地壳应力研究所, 北京 100085

基金项目: 技术基础科研资助项目（J132012C001）；国家自然科学基金资助项目（50908215）

详细信息

中图分类号: C37
计量
- 文章访问数: 1331
- HTML全文浏览量: 110
- PDF下载量: 517
- 被引次数: 0
出版历程
- 收稿日期: 2013-01-04
- 网络出版日期: 2013-11-30

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

摘要

摘要: 乏信息多传感器压力测量数据的融合估计是压力测量研究的重要问题，不同于经典的统计学方法，结合自助法和模糊数学的相关算法，提出一种实现乏信息多传感器压力测量数据融合估计的自助模糊数学模型.对具有乏信息特征的多个压力传感器的测量数据进行自助抽样；利用最大熵算法构建出不同时刻与位置的多个压力传感器测量数据的自助分布；用自助分布进行加权均值计算，提取相应特征值，得到自助融合序列；通过模糊隶属函数得到所测压力值的真值与区间估计.实例计算表明：在乏信息条件下，算法精度可达87%；在大样本条件下，测量数据在置信水平99.7%下，融合估计可靠性可达95%，验证了乏信息自助模糊融合估计算法的有效性.
- 乏信息 /
- 多压力传感器 /
- 自助模糊 /
- 数据融合
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.
- poor information /
- pressure multi-sensor /
- bootstrap-fuzzy /
- data fusion

HTML全文

参考文献(17)

[1]	葛乐矣, 赵伟, 徐子帆, 等.乏信息动态测量误差灰自助预报[J].农业机械学报, 2011, 42 (7):210-214, 219 Ge Leyi, Zhao Wei, Xu Zifan, et al.Error predicting for dynamic measurement of poor information based on grey bootstrap method[J].Transactions of the Chinese Society for Agricultural Machinery, 2011, 42 (7):210-214, 219 (in Chinese)
[2]	Seiler F, Srulijes J.New results in numerical and experimental fluid mechanics IV[M].Germany:Springer-Verlag Berlin and Heidelberg GmbH & Co K, 2004:87, 124-131
[3]	Kotomin A A, Shirokova N P, Dushenok S A, et al.Detonation pressure of explosive charges applied in spacecraft division systems[J].Solar System Research, 2011, 45 (7):677-683
[4]	Tao Zui, Qin Bangyong, Li Ziwei, et al.Satellite observations of the partial pressure of carbon dioxide in the surface water of the Huanghai sea and the Bohai sea[J].Acta Oceanologica Sinica, 2012, 31 (3):67-73
[5]	Khaleghi B, Khamis A, Karray F O, et al.Multisensor data fusion:a review of the state of the art[J].Information Fusion, 2013, 14 (1):28-44
[6]	Chen Yukun, Si Xicai, Li Zhigang.Research on Kalman-filter based multisensor data fusion[J].Journal of Systems Engineering and Electronics, 2007, 18 (3):497-502
[7]	Noureldin A, El-Shafie A, Taha M R.Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation[J].Engineering Applications of Artificial Intelligence, 2007, 20 (1):49-61
[8]	Deng Zili, Zhang Peng, Qi Wenjuan, et al.Sequential covariance intersection fusion Kalman filter[J].Information Sciences, 2012, 189:293-309
[9]	Zhu Hao, Henry Leung, He Zhongshi.A variational Bayesian approach to robust sensor fusion based on Student-t distribution[J].Information Sciences, 2013, 221:201-214
[10]	Caron F, Davy M, Duflos E, et al.Particle filtering for multisensor data fusion with switching observation models:Application to land vehicle positioning[J].IEEE Transactions on Signal Processing, 2007, 55 (6Part 1):2703-2719
[11]	Noureldin A, Osman A, El-Sheimy N.A neuro-wavelet method for multi-sensor system integration for vehicular navigation[J].Measurement Science & Technology, 2004, 15 (2):404-412
[12]	Vega J, Pereira A, Portas A, et al.Data mining technique for fast retrieval of similar waveforms in Fusion massive databases[J].Fusion Engineering and Design, 2008, 83 (1):132-139
[13]	Xia Xintao, Chen Xiaoyang, Zhang Yongzhen, et al.Grey bootstrap method of evaluation of uncertainty in dynamic measurement[J].Measurement, 2008, 41 (6):687-696
[14]	Wang Qian, Fu Jihua, Wang Zhongyu, et al.A seismic intensity estimation method based on the fuzzy-norm theory[J].Soil Dynamics and Earthquake Engineering, 2012, 40:109-117
[15]	Ge Leyi, Wang Zhongyu.Novel uncertainty-evaluation method of virtual instrument small sample size[J].Journal of Testing and Evaluation, 2008, 36 (3):273-279
[16]	夏新涛, 陈晓阳, 张永振, 等.多传感器滑坡时间序列的自助融合及其灰假设检验[J].岩土力学与工程学报, 2007, 26 (9):1904-1912 Xia Xintao, ChenXiaoyang, Zhang Yongzhen, et al.Bootstrap fusion and its grey hypothesis testing for landslide time series of multi-sensor[J].Chinese Journal of Rock Mechanics and Engineering, 2007, 26 (9):1904-1912 (in Chinese)
[17]	郭科, 彭继兵, 许强, 等.滑坡多点数据融合中的多传感器目标跟踪技术应用[J].岩土力学, 2006, 27 (3):479-481 Guo Ke, Peng Jibing, Xu Qiang, et al.Application of multi-sensor target tracking to multi-station monitoring data fusion in landslide[J].Rock and Soil Mechanics, 2006, 27 (3):479-481 (in Chinese)