An Improved Multi-sensor Data Adaptive Fusion Method

DAI Haifa; BIAN Hongwei; WANG Rongying; ZHANG Jiajia

doi:10.13203/j.whugis20180475

Volume 45 Issue 10

Oct. 2020

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2020 > 45(10): 1602-1609. > DOI: 10.13203/j.whugis20180475

DAI Haifa, BIAN Hongwei, WANG Rongying, ZHANG Jiajia. An Improved Multi-sensor Data Adaptive Fusion Method[J]. Geomatics and Information Science of Wuhan University, 2020, 45(10): 1602-1609. DOI: 10.13203/j.whugis20180475

Citation:

PDF (2194 KB)

An Improved Multi-sensor Data Adaptive Fusion Method

1.
Collage of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China

Funds:

The National Natural Science Foundation of China 41876222

More Information

Author Bio:
DAI Haifa, PhD candidate, specializes in multi-sensors integrated navigation and information fusion. E-mail: daihaifa1990@163.com
Corresponding author:
BIAN Hongwei, PhD, professor. E-mail: travisbian@fox.com
Received Date: June 01, 2019
Published Date: October 04, 2020

Graphical Abstract

Abstract

Abstract

One of the major problems of multi-sensor information fusion is that sensors frequently produce spurious observations, which have a great impact on the fusion accuracy and are difficult to be modeled and predicted. Bayesian information fusion technology based on entropy theory is an effective method to solve this problem. However, this method needs the integral operation in infinite interval, and the problem of numerical instability is prone to occur. To solve this problem, this paper proposes an improved multi-sensor data adaptive fusion method. In the framework of Bayesian theory, we use the difference between the measured values of sensors to adaptively establish the posterior probability distribution model of the sensor. Combined with the theory of mutual information, the pseudo-measured values can be identified and eliminated in real time, without integral calculation. The simulation and measured data test results show that the proposed method achieves the same results as the simple Bayesian fusion method without any spurious measurement, and the information fusion performance is obviously better than the simple Bayesian fusion method.
- multi-sensor information fusion,
- Bayesian method,
- spurious data,
- adaptive modeling,
- mutual information

FullText(HTML)

References (22)

References

[1]	Groves P D, Wang L, Walter D, et al. The Four Key Challenges of Advanced Multisensor Navigation and Positioning[C]. IEEE/ION Position Location and Navigation Symposium, Monterey, CA, 2014
[2]	Bahador K, Alaa K, Fakhreddine O, et al. Multisensor Data Fusion: A Review of the State-of-the-Art[J]. Information Fusion, 2013, 14(1): 28-44 doi: 10.1016/j.inffus.2011.08.001
[3]	Groves P D. The PNT Boom: Future Trends in Integrated Navigation[J]. Inside GNSS, 2013, 8: 44-49 http://www.researchgate.net/publication/268387539_The_PNT_Boom_Future_Trends_in_Integrated_Navigation
[4]	Waltz E, James L. Handbook of Multisensor Data Fusion[J]. Artech House Radar Library, 2001, 39(5): 180-184 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=caeae85bcf3c35cb25c94f03335a29c0
[5]	Wang G, Hall David L. McMullen Sonya AH: Mathematical Techniques in Multisensor Data Fusion[J]. BioMedical Engineering OnLine, 2005, 4(1): 23 doi: 10.1186/1475-925X-4-23
[6]	Martin Liggins I I, Hall D, Llinas J. Handbook of Multisensor Data Fusion: Theory and Practice, Second Edition[J]. Artech House Radar Library, 2008, 39(5): 180 - 184 http://www.crcpress.com/product/isbn/9781420053081?source=crcpress.com&utm_source=productpage&utm_medium=website&utm_campaign=RelatedTitles
[7]	Groves P D. The Complexity Problem in Future Multisensor Navigation and Positioning Systems: A Modular Solution[J]. Journal of Navigation, 2014, 67(2): 311-326 doi: 10.1017/S0373463313000696
[8]	Young S Y R, McGraw G A. Method and System for Fault Detection and Exclusion for Multi-Sensor Navigation Systems[J]. Google Patents, 2007, 5:631 314 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=US20030631314
[9]	Cai Li, Yang Lijian. A Smooth Simultaneous Confidence Band for Conditional Variance Function[J]. Test, 2015, 24(3): 632-655 http://smartsearch.nstl.gov.cn/paper_detail.html?id=4b8b4c538575b6617b49b5cc408d26ce
[10]	Wang Rong, Xiong Zhi, Liu Jianye, et al. Chi-square and SPRT Combined Fault Detection for Multisensor Navigation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(3): 1 352-1 365 doi: 10.1109/TAES.2016.140860
[11]	Yun S H, Kang C W, Park C G. Reducing the Computation Time in the State Chi-Square Test for IMU Fault Detection[C].2014 14th International Conference on Control, Automation and Systems (ICCAS 2014), IEEE, Kintex, Korea, 2014
[12]	Wen Xin, Ji Long, Zhang Xingwang, et al. Fault Detection and Diagnosis in the INS/GPS Navigation System[C]. World Automation Congress, IEEE, Shengyang, China, 2014
[13]	杨元喜, 高为广.基于多传感器观测信息抗差估计的自适应融合导航[J].武汉大学学报·信息科学版, 2004, 29(10):885-888 http://ch.whu.edu.cn/article/id/4492 Yang Yuanxi, Gao Weiguang. Integrated Navigation Based on Robust Estimation Outputs of Multi-sensor Measurements and Adaptive Weights of Dynamic Model Information[J].Geomatics and Information Science of Wuhan University, 2004, 29(10):885-888 http://ch.whu.edu.cn/article/id/4492
[14]	Li G R, Wang Y. Automatic ARIMA Modeling-Based Data Aggregation Scheme in Wireless Sensor Networks[J]. Eurasip Journal on Wireless Communications & Networking, 2013, 2 013(1): 85 doi: 10.1186/1687-1499-2013-85
[15]	Zhao X, Wang S C, Zhang J S, et al. Real-Time Fault Detection Method Based on Belief Rule Base for Aircraft Navigation System[J]. Chinese Journal of Aeronautics, 2013, 26(3): 717-729 doi: 10.1016/j.cja.2013.04.039
[16]	Mahmood Y A, Ahmadi A, Verma A K, et al. Fuzzy Fault Tree Analysis: A Review of Concept and Application[J]. International Journal of System Assurance Engineering & Management, 2013, 4(1): 19-32 doi: 10.1007/s13198-013-0145-x
[17]	Jaradat M A, Abdel-Hafez M F, Saadeddin K, et al. Intelligent Fault Detection and Fusion for INS/GPS Navigation System[C]. International Symposium on Mechatronics & Its Applications, IEEE, Amman, Jordan, 2013
[18]	Penner M R, Mizumori S J Y. Neural Systems Analysis of Decision Making During Goal-Directed Navigation[J]. Progress in Neurobiology, 2012, 96(1): 96-135 doi: 10.1016/j.pneurobio.2011.08.010
[19]	孙文舟, 殷晓冬, 李树军.基于熵权重的水下载体导航信息融合方法[J].武汉大学学报·信息科学版, 2018, 43(10): 1 465-1 471 doi: 10.13203/j.whugis20160550 Sun Wenzhou, Yin Xiaodong, Li Shujun. Underwater Carrier Navigation Information Fusion Method Based on Entropy Weight[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1 465-1 471 doi: 10.13203/j.whugis20160550
[20]	Kumar M. Stochastic Ddaptive Sensor Modeling and Data Fusion[J]. Proc SPIE, 2006, 6 174: 246-251 doi: 10.1117/12.658478
[21]	Kumar M, Garg D P, Zachery R A. A Eneralized Approach for Inconsistency Detection in Data Fusion From Multiple Sensors[C]. American Control Conference, IEEE, Minneapolis, MN, Xplore, USA, 2006
[22]	Davison A J. Active Search for Real-Time Vision[C]. Tenth IEEE International Conference on Computer Vision, IEEE Computer Society, Beijing, China, 2005

[1]	NIU Quanfu, LIU Mingzhi, ZHANG Man, CHENG Weiming. Vegetation Dynamic Change and Its Response to Climate and Topography in Altay Region of Xinjiang in Recent 20 Years[J]. Geomatics and Information Science of Wuhan University, 2023, 48(9): 1522-1530. DOI: 10.13203/j.whugis20210189
[2]	ZHANG Chen, HE Biao, GUO Renzhong, MA Ding, CHEN Yebin. A Data Conversion Method from Oblique Photogrammetric 3D Models to Renderable Assets in Unreal Engine 4[J]. Geomatics and Information Science of Wuhan University, 2023, 48(4): 514-524. DOI: 10.13203/j.whugis20210574
[3]	WANG Pengxin, CHEN Chi, ZHANG Yue, ZHANG Shuyu, LIU Junming. Estimation of Winter Wheat Yield Using Assimilated Bi-variables and PCA-Copula Method[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1201-1212. DOI: 10.13203/j.whugis20220038
[4]	LEI Lei, LI Zhenhong, YANG Hao, YANG Guijun. Extraction of the Leaf Area Density of Maize Using UAV-LiDAR Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(11): 1737-1745. DOI: 10.13203/j.whugis20200674
[5]	YU Ying, SONG Zhangliang, FAN Wenyi, YANG Xiguang. Scale Conversion from Canopy Spectra to Leaf Spectra[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1560-1565, 1573. DOI: 10.13203/j.whugis20160552
[6]	QIN Zhanfei, SHEN Jian, XIE Baoni, YAN Lin, CHANG Qingrui. Hyperspectral Estimation Model for Predicting LAI of Rice in Ningxia Irrigation Zone[J]. Geomatics and Information Science of Wuhan University, 2017, 42(8): 1159-1166. DOI: 10.13203/j.whugis20150132
[7]	ZHANG Xun, ZHONG Ershun, ZHANG Xiaohu, WANG Shaohua, LI Shaojun. A Modified Algorithm to Construct Gridded Area Cartogramsby Scale Effect Index[J]. Geomatics and Information Science of Wuhan University, 2015, 40(8): 1100-1104. DOI: 10.13203/j.whugis20130370
[8]	LIU　Hui. Extraction　of 　the　Floor　Area　Ratio　in 　the　Central　District 　of 　Fuzhou　Cit y Based　on　an　Improved　Shadow　Index　Model[J]. Geomatics and Information Science of Wuhan University, 2014, 39(10): 1241-1247.
[9]	XIONG Jinguo, WANG Shixin, ZHOU Yi, Yan Fuli. Influence of Landscape Pattern Index on the Area Extracted from ETM~+ and MODIS[J]. Geomatics and Information Science of Wuhan University, 2011, 36(1): 98-103.
[10]	WANG Xiaohong, LIU Yaolin, DU Xiao. Drought Monitoring Using Vegetation Leaf Water in China[J]. Geomatics and Information Science of Wuhan University, 2007, 32(6): 498-501.

Cited By

Get Citation

PDF

XML

Article views (1582) PDF downloads (138)

An Improved Multi-sensor Data Adaptive Fusion Method

Abstract

References

Related Articles

Catalog

Related

An Improved Multi-sensor Data Adaptive Fusion Method

Abstract

References

Related Articles

Catalog

Related

Export File

Citation

Format

Content