-
摘要: 传感器的异常观测是多传感器信息融合的一个重要问题, 且对融合精度有很大的影响。贝叶斯信息融合技术是解决该问题的一种有效方法, 但是该方法需要进行无穷区间的积分运算, 容易出现数值不稳定的问题。针对该问题提出了一种改进的多传感器自适应融合方法, 利用传感器测量值之间的差值自适应建立传感器的后验概率分布模型, 并结合互信息的理论实时识别和剔除异常观测值, 从而避免了求熵时的积分计算。仿真和实测数据试验结果表明, 所提方法在无异常观测值的条件下得到的结果与简单贝叶斯融合方法相当; 对于存在异常观测值的情况下, 信息融合的性能明显优于一般的贝叶斯融合方法。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.
-
-
![]()
图 3 卫星导航接收机的经纬度测量值
Figure 3. Latitude and Longitude Measurements of Satellite Navigation Receivers
表 1 传感器性能参数设置
Table 1 Performance Parameters of Sensors
传感器 正常观测值 异常观测值 概率 分布 概率 分布 1 0.98 N(20, 4) 0.02 N(30, 4) 2 0.90 N(20, 6.25) 0.10 N(35, 6.25) 3 0.80 N(20, 9) 0.20 N(38, 9) 
下载: 导出CSV
表 2 3种方法的均值和均方差比较
Table 2 Comparison of Mean Values and Covariance of Three Methods
方法 均值 均方差 1 22.28 2.60 2 21.49 2.08 3 20.23 1.86
下载: 导出CSV
-
[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
-
期刊类型引用(4)
1. 朱杰，郑加柱，陈红华，杨静，胡平昌，陆敏燕. 结合POI数据的南京市商业中心识别与集聚特征研究. 现代测绘. 2022(06): 34-39 . 
百度学术
2. 金澄，安晓亚，陈占龙，马啸川. 矢量居民地多边形多级图划分聚类方法. 武汉大学学报(信息科学版). 2021(01): 19-29 . 百度学术
3. 张铭龙，何贞铭. 基于因子分析法的城市商业中心抽取研究. 地理空间信息. 2021(08): 58-60+64+5 . 百度学术
4. 李卫东，张铭龙，段金龙. 基于POI数据的南京市空间格局定量研究. 世界地理研究. 2020(02): 317-326 . 百度学术
其他类型引用(3)
计量
- 文章访问数: 1585
- HTML全文浏览量: 234
- PDF下载量: 139
- 被引次数: 7
