不确定信息的融合方法及其应用研究
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摘要
随着各种物理、生化类先进传感器的发展，特别是某些传感器技术还不是很成熟，使得测量的信息呈现量大、多维、动态、不确定、不完整和时空变异等特点，使得系统对后续信息处理提出了更高的要求。由于传统的数据分析方法已经很难适应要求，因此如何将这些传感器测量数据进行分析，最大限度的降低单个传感器所提供信息的误差以及多传感器所提供信息的不确定性，是亟待解决的问题。本文主要对不确定信息的融合方法进行了研究，获得了一些有意义的成果，其主要研究内容如下：
1．针对土壤参数野外测量时存在多种因素干扰问题，采用粗糙集理论对多传感器采集的原始数据进行属性约简和目标约简，减少采样时的噪声和冗余，再根据预处理的信息结构构建支持向量机的信息预测系统，解决小样本和不确定条件下的多传感器数据融合问题，为了获得最优的融合精度，采用粒子群算法来优化融合参数。
2．针对DS证据理论处理不确定信息存在无法解决冲突信息的缺陷，提出一种基于加权证据距离的数据融合来提高融合效率。首先采用加权的DS理论对证据进行处理，然后以各加权证据与期望证据构成修正的证据距离。其次采用最优化的思想，将修正的证据距离作为改进粒子群优化(Particle Swarm Optimization, PSO)算法优化的目标函数，在此基础上获得证据距离最小时的各个证据权值，最后采用DS组合方法进行加权合成，得出融合结果。
3．针对递推加权最小二乘融合存在算法鲁棒性较差等缺陷，在传统最小二乘算法研究的基础上，提出一种基于自适应权值的鲁棒加权最小二乘算法。按照不同的权值设计准则，得出所需的测量估计值，并且采用鲁棒权因子和学习因子的加权原则，充分利用测量数据中冗余信息，进一步提高冗余系统测量数据的估计精度。
4．针对传感器管理在数据融合系统中地位与作用，提出一种智能化的传感器管理算法，通过进化算法自下而上地评价每一个控制决策使得系统的不确定性最小。首先为了取得最优的传感器测量参数将采用粒子群算法在参数空间中进行自下而上地搜索，再采用贝叶斯网络直接设计系统目标与全局性能指标之间的关系，从而使得算法更适合评估当前环境形势以及正确选择基于系统条件和传感器输入的合适性能指标，最后建立全局性能指标，并采用粒子群算法搜索最佳选择的传感器参数，最终实现对传感器资源的优化配置。
With the development of various kinds of advanced physical, chemical and biologicalsensors, and some sensing technologies are not mature, so that measurement information wasbeing quantities, multidimensional, dynamic, uncertain, incomplete and spatiotemporalvariation, and thus these technologies put forward the higher request for the cominginformation processing. It is difficult to meet the requirement with the traditional data analysis.Therefore, it is an urgent problem for processing these sensor measurement data that reducethe error from single sensor or the uncertainty from multiple sensors. In this dissertation, anumber of key issues of uncertain information fusion technology were studied, and got somesignificant results. The main content and contributions of this dissertation are summarized asfollows:
1. In order to solve the problem of the soil properties measurement error because ofmultiple factors interference, the dissertation adopts Rough Sets for reduction of the attributeset and target set for the raw data from various sensors, thus the noise and redundancy will bereduced in sampling. Then constructs information prediction system of Support VectorMachine according to the preprocessing information structure, and solves the problem ofmultisensor data fusion in the situation of small sample and uncertainty. In order to get theoptimal fusion accuracy, it uses Particle Swarm Optimization (PSO) for fusion parameters. Tomake operate faster, and also to increase the accuracy of the fusion, a feature selection processwith PSO is used in this dissertation to optimize the fusion accuracy by its the superiority ofoptimal search ability.
2. Although DS evidence theory has many advantages, but the uncertainty formaldescription method based on DS theory has some defects. To reduce the defects and enhancethe fusion efficiency, a new data fusion algorithm based on weighed evidence synthetictechnique is presented. Firstly, the concept of weight of sensor evidence itself and evidencedistance based on a quantification of the similarity between sets to acquire the reliabilityweight of the relationship between evidences is set up. Then considering the disadvantages ofthe improved DS theory, a best method of obtaining evidence weight value is presented by animproved PSO. Finally, we use the improved PSO to acquire the reliability weight of therelationship between evidences to modify DS theory.
3. Considering uncertainty measurement and the principle of Least Squares, recursiveweighted least squares fusion algorithm is deduced, and sensor weight coefficient is closerelationship with measuring variance. However, weight coefficient often is deduced frommeasuring variance, due to measurement attribute weight is different, and the dimensionalinconsistencies, then it is difficult to realize the effective measure for the sensor precision. Meanwhile, the sensor itself variance parameter is assigned or given in advance according tothe experience. Thus the anti-interference ability of fusion results is weak, and the robustnessof the algorithms is low. The robust least squares estimation approach proposed in thisdissertation employs an adaptive weighted viewpoint. For the different weight design criteria,the measurement estimation value is got. Due to introduce robust weight factor and learningfactor, we take full advantages of redundancy information of measurement data, and improvethe estimated precision of redundant system measurement data.
4. Sensor management is very essential to the multisensor system in the targetassignment, and become the core element of the information fusion system to improve itsperformance. This dissertation describes an intelligent sensor management paradigm thatminimizes system uncertainties with an evolutionary algorithm that evaluates each controldecision from the bottom up. PSO algorithm works from the bottom up searching theparameter space for the best sensor measurement setting. The Bayesian network is moresuited towards assessing the situation and selecting proper performance goals based onconditions and operator inputs. Bayesian networks allow the designer to direct therelationship between the system goals and global performance values providing the flexibilityto define these performance values to fit any situation. Once a relationship between the globalperformance values is well-defined, PSO algorithm searches to optimally select the sensorparameters that achieve the desired global performance, and then the problem ofautomatically allocating the resources of multi-sensor is solved.
1．针对土壤参数野外测量时存在多种因素干扰问题，采用粗糙集理论对多传感器采集的原始数据进行属性约简和目标约简，减少采样时的噪声和冗余，再根据预处理的信息结构构建支持向量机的信息预测系统，解决小样本和不确定条件下的多传感器数据融合问题，为了获得最优的融合精度，采用粒子群算法来优化融合参数。
2．针对DS证据理论处理不确定信息存在无法解决冲突信息的缺陷，提出一种基于加权证据距离的数据融合来提高融合效率。首先采用加权的DS理论对证据进行处理，然后以各加权证据与期望证据构成修正的证据距离。其次采用最优化的思想，将修正的证据距离作为改进粒子群优化(Particle Swarm Optimization, PSO)算法优化的目标函数，在此基础上获得证据距离最小时的各个证据权值，最后采用DS组合方法进行加权合成，得出融合结果。
3．针对递推加权最小二乘融合存在算法鲁棒性较差等缺陷，在传统最小二乘算法研究的基础上，提出一种基于自适应权值的鲁棒加权最小二乘算法。按照不同的权值设计准则，得出所需的测量估计值，并且采用鲁棒权因子和学习因子的加权原则，充分利用测量数据中冗余信息，进一步提高冗余系统测量数据的估计精度。
4．针对传感器管理在数据融合系统中地位与作用，提出一种智能化的传感器管理算法，通过进化算法自下而上地评价每一个控制决策使得系统的不确定性最小。首先为了取得最优的传感器测量参数将采用粒子群算法在参数空间中进行自下而上地搜索，再采用贝叶斯网络直接设计系统目标与全局性能指标之间的关系，从而使得算法更适合评估当前环境形势以及正确选择基于系统条件和传感器输入的合适性能指标，最后建立全局性能指标，并采用粒子群算法搜索最佳选择的传感器参数，最终实现对传感器资源的优化配置。
With the development of various kinds of advanced physical, chemical and biologicalsensors, and some sensing technologies are not mature, so that measurement information wasbeing quantities, multidimensional, dynamic, uncertain, incomplete and spatiotemporalvariation, and thus these technologies put forward the higher request for the cominginformation processing. It is difficult to meet the requirement with the traditional data analysis.Therefore, it is an urgent problem for processing these sensor measurement data that reducethe error from single sensor or the uncertainty from multiple sensors. In this dissertation, anumber of key issues of uncertain information fusion technology were studied, and got somesignificant results. The main content and contributions of this dissertation are summarized asfollows:
1. In order to solve the problem of the soil properties measurement error because ofmultiple factors interference, the dissertation adopts Rough Sets for reduction of the attributeset and target set for the raw data from various sensors, thus the noise and redundancy will bereduced in sampling. Then constructs information prediction system of Support VectorMachine according to the preprocessing information structure, and solves the problem ofmultisensor data fusion in the situation of small sample and uncertainty. In order to get theoptimal fusion accuracy, it uses Particle Swarm Optimization (PSO) for fusion parameters. Tomake operate faster, and also to increase the accuracy of the fusion, a feature selection processwith PSO is used in this dissertation to optimize the fusion accuracy by its the superiority ofoptimal search ability.
2. Although DS evidence theory has many advantages, but the uncertainty formaldescription method based on DS theory has some defects. To reduce the defects and enhancethe fusion efficiency, a new data fusion algorithm based on weighed evidence synthetictechnique is presented. Firstly, the concept of weight of sensor evidence itself and evidencedistance based on a quantification of the similarity between sets to acquire the reliabilityweight of the relationship between evidences is set up. Then considering the disadvantages ofthe improved DS theory, a best method of obtaining evidence weight value is presented by animproved PSO. Finally, we use the improved PSO to acquire the reliability weight of therelationship between evidences to modify DS theory.
3. Considering uncertainty measurement and the principle of Least Squares, recursiveweighted least squares fusion algorithm is deduced, and sensor weight coefficient is closerelationship with measuring variance. However, weight coefficient often is deduced frommeasuring variance, due to measurement attribute weight is different, and the dimensionalinconsistencies, then it is difficult to realize the effective measure for the sensor precision. Meanwhile, the sensor itself variance parameter is assigned or given in advance according tothe experience. Thus the anti-interference ability of fusion results is weak, and the robustnessof the algorithms is low. The robust least squares estimation approach proposed in thisdissertation employs an adaptive weighted viewpoint. For the different weight design criteria,the measurement estimation value is got. Due to introduce robust weight factor and learningfactor, we take full advantages of redundancy information of measurement data, and improvethe estimated precision of redundant system measurement data.
4. Sensor management is very essential to the multisensor system in the targetassignment, and become the core element of the information fusion system to improve itsperformance. This dissertation describes an intelligent sensor management paradigm thatminimizes system uncertainties with an evolutionary algorithm that evaluates each controldecision from the bottom up. PSO algorithm works from the bottom up searching theparameter space for the best sensor measurement setting. The Bayesian network is moresuited towards assessing the situation and selecting proper performance goals based onconditions and operator inputs. Bayesian networks allow the designer to direct therelationship between the system goals and global performance values providing the flexibilityto define these performance values to fit any situation. Once a relationship between the globalperformance values is well-defined, PSO algorithm searches to optimally select the sensorparameters that achieve the desired global performance, and then the problem ofautomatically allocating the resources of multi-sensor is solved.
引文
1. Waltz E, Lilnas J. Multi-sensor data fusion [M]. Boston: Artech House.2000.
2. Halld L. Mathematical technique in multi-sensor data fusion [M]. London: Artech House,2000.
3. Richard T. Principles of effective multi-sensor data fusion [J]. Military Technology,2003,27(5):29-37.
4.李小昱,王为等.多传感器信息融合技术及在农业工程中的应用前景[J].农业工程学报,2003,19(3):10-13.
5. Dabid H, James L. An introduction to multi-sensor data fusion[C]. Proceedings of the IEEE,1997:6-
23.
6. Vershinin Y A. A data fusion algorithm for multi-sensor systems [C]. Proceedings of the Fainternational Conference on Information Fusion.2009,1(8):3l4-345.
7. Huang G S, Wang S W, Xiao F, Sun Y J. A data fusion scheme for building automation systems ofbuilding central chilling plants [J]. Automation in Construction,2009,18(3):302-309.
8. Hyder A K. Multisensor fusion [M]. Netherlands: Kluwer Academic Publishers,2006:1-39.
9.夏胜平,滕召胜,郁文贤等.智能粮食收购系统的设计及其水分检测数据融合[J].农业现代化研究,2000,21(1):24-27.
10. Zhu P Y, XU B G. Fusion of ECadata using SVM and rough sets theory augmented by PSO [J]. Journalof Computational Information Systems,2011.7(1):295-302.
11.刘顺国,邢岩,李金凤等.“数字土壤”及其在测土配方施肥中的应用前景[J].安徽农业科学.2007,35(6):1470-1471.
12. Wong M T F, Asseng S, Robertson M. J. and Oliver Y. Mapping subsoil acidity and shallow soil acrossa field with information from yield maps, geophysical sensing and the grower [J]. Precision Agriculture,2008,9(1-2): pp3-15.
13. Luo B C, Yuan K, Chen J L. Uncertainty analysis based dynamic multi-sensor data fusion [J]. ActaAutomatic Sinica,2009,30(3):407-415.
14. Oberkampf W L, Helton J C, Joslyn C A, et a1. Challenge problems: uncertainty in system responsegiven uncertain parameters[J]. Reliability Engineering&System Safety,2004,85(1-3):11-19.
15. Oberkampf W L, Helton J C, Sentz K. Mathematical representation of uncertainty [C]．Proceedings ofthe41st AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference,AIAA-2001-1645, Seattle, April2001,16-19.
16. Klir G J. Uncertainty and information: foundations of generalized information theory [M]. Wiley-Interscience, Hoboken, NJ,2006.
17. Walley P. Measures of uncertainty in expert systems [J]. Artificial Intelligence,1996,83(1):1-58.
18.王清印,刘志勇.不确定性信息的概念、类别及其数学表述[J].运筹与管理,2001(4):9-15.
19.赵秀恒.不确定性系统理论及其在预测与决策中的应用[M].冶金工业出版社,2010.
20.万玉成.基于未确知性的预测与决策方法及其应用研究[D].东南大学博士学位论文,2004年.
21.王清印,刘志勇.不确定性系统理论基础概要[C].第二届不确定系统年会论文集.2004,
22. White F E. Data fusion lexicon. U. S. Department of defence, data fusion sub-panel of the jointdirectors of laboratories, Technical Panel for C3,1987.
23. Klein L A. Sensor and data fusion foncepts and application (second edition)[M]. Bellingham: SPIEOptical Press,1990.
24.韩崇昭,朱洪艳,段战胜等.多源信息融合[M].北京:清华大学出版社,2006.
25. Hall D, Linas L J. An introduction to multi-sensor data fusion [J]. Proc of the IEEE,1997,85(1):6-23.
26. Bahador K, Alaa K, et al. Multisensor data fusion: A review of the state-of-the-art [J]. InformationFusion, In Press.
27. Smets P. Analyzing the combination of conflicting belief functions [J]. Information Fusion,2007,8(4):387-412.
28. Mahler. R P S.“Statistics101” for multisensor, multitarget data fusion [J]. IEEE Aerospace andElectronic Systems Magazine,2004,19(1):53-64.
29. Smith D, Singh S. Approaches to multisensor data fusion in target tracking: a survey [J], IEEETransactions on Knowledge and Data Engineering,2006,18(12):1696-1710.
30. Zhu Y, Song E, Zhou J, You Z. Optimal dimensionality reduction of sensor data in multisensorestimation fusion [J], IEEE Transactions on Signal Processing,2005,53(5):1631-1639.
31. Milenova B L, Campos M M. Mining high-dimensional data for information fusion: a database-centricapproach[C]. Proc. of International Conference on Information Fusion,2005:638-645.
32. Yager R R. A framework for multi-source data fusion [J]. Information Sciences,2004,(163):175-200.
33.王壮. C4ISR系统目标综合识别理论与技术研究[D].国防科技大学博士学位论文,2001.
34.徐毓.雷达网数据融合问题研究[D].清华大学博士学位论文,2003.
35.王明辉.多传感器数据融合跟踪算法研究[D].清华大学博士后出站报告,2003.
36. Svensson P. Information fusion in the future Swedish RMA defence [J]. Journal of Military Technology,2002,2000(3):22-28.
37.马骏.信息融合及其在目标识别的应用[D].国防科技大学博士学位论文,1997.
38.杨露菁,余华.多源信息融合理论与应用[M].北京:北京邮电大学出版社,2006.
39.管天云.多传感器信息融合研究[D].浙江大学博士学位论文,1998.
40. Kam M, Zhu X, Kalata P. Sensor fusion for mobile robot navigation[C]. Proc. Of the IEEE SpecialIssue on Sensor Fusion,1997,85(1):108-119.
41. Wu Y G, Tang J Y, Liu K. Obstacle detection and environment modeling based on multisensor fusionfor robot navigation[J]. AI in Engineering,1996,10(4):323-333.
42. Allen P k. Integration vision and touch for object recognition tasks [J]. International Journal ofRobotics Research,1988,7(6):15-33.
43. Hernandez A I, Garrault G, Mora F et al. Multisensor fusion for atria and ventricular activity detectionin coronary care monitoring [J]. IEEE Transon Biomed Eng,1999,46(10):1186-1190.
44. Mort N, Prajitno P. A multi-sensor data fusion based target tracing system [C].2002IEEEInternational Conference on industrial Technology, Dec.2002,1(11-14):427-432.
45.敬忠良.神经网络技术与应用[M].北京:国防工业出版社,1995.
46.杨万海.多传感器数据融合及其应用[M].西安:西安电子科技大学出版社,2004.
47.康耀红.数据融合理论与应用[M].西安:西安电子科技大学出版社,1997.
48.权太范.信息融合神经网络-模糊推理理论与应用[M].北京:国防工业出版社,2002.
49.周宏仁,敬忠良,王培德.机动目标跟踪[M].北京:国防工业出版社,1991.
50.王润生.信息融合[M].北京:科学出版社,2007.
51.刘同明,夏祖勋,解洪成.数据融合技术及应用[M].北京:国防工业出版社,1998.
52.文成林,周东华.多尺度估计理论及应用[M].北京:清华大学出版社,2002.
53.文成林.多尺度动态建模理论及其应用[M].北京:科学出版社,2008.
54.韩崇昭,朱洪艳.多源信息融合[M].北京:清华大学出版社,2006.
55.高倩.鲁棒数据校正理论与应用研究[D].上海交通大学博士论文,2007.
56.何友.信息融合理论及应用[M].北京:电子工业出版社,2010.
57.潘泉,梁彦,杨峰.现代目标跟踪与信息融合[M].北京:国防工业出版社,2009.
58.邓自立.最优估计理论及其应用——建模滤波信息融合估计[M].哈尔滨:哈尔滨工业大学出版社,2005.
59.邓自立.信息融合滤波理论及其应用[M].哈尔滨:哈尔滨工业大学出版社,2007.
60.彭冬亮,文成林,薛安克.多传感器多源信息融合理论及应用[M].北京:科学出版社,2010.
61. Noguchi N, Reid J F, Will J, et al. Vehicle automation system based on multi-sensor integration[C].ASAE Annual International Meeting, Orlando, FL, USA,1998.
62. Nunez, J. Multiresolution-based image fusion with additive wavelet decomposition [J]. IEEE Trans.Geosci. Remote Sens.,1999,37(3):1204-1211.
63. Bruzzone L, Prieto D F. An adaptive semiparametric and context-based approach to unsupervisedchange detection in multitemporal remote-sensing images [J]. IEEE Transactions on Image Processing,2002,11(4):452-466.
64. Steinmetz V, Crochon M, Bellon-Maurel V, et al. Sensors for fruit firmness assessment: comparisonand fusion [J]. Journal of Agricultural Engineering Research,1996,64:15-28.
65. Hirafuji, M, Fukatsu T and Hu H M. Full-Wireless Field Monitoring server for advanced sensornetwork[C], Proc. of AFITA/WCCA, Bangkok, Thailand, Aug.2004,686-691.
66. Sun Y R, Ma D K, Schulze L P. On-the-go measurement of soil water content and mechanicalresistance by a combined horizontal penetrometer [J]. Soil Till Res,2006,86:209-217.
67. Topp G C, Lapen D R, Edwards M J, et a1. Laboratory calibration, in-field validation and used of a soilpenetrometer measuring cone resistance and water content [J]. Vadose Zone Journal,2003,2:633-641.
68.王文杰,唐娉,朱重光.一种基于小波变换的图像融合算法[J].中国图像图形学报,2001,6(11):1130-1135.
69.滕召胜,蔡铁,王可宁.一种智能化粮情自动检测系统[J].农业工程学报,2001,17(4):144-157.
70.滕召胜.基于自适应加权数据融合的粮食水分快速测定仪[J].农业机械学报,1999,30(6):64-67.
71.张明社,李小昱,雷廷武等.基于神经网络的数据融合法测量水流泥沙含量[J].农业工程学报,2002,18(4):41-43.
72.刘杏梅,徐建明,章明奎等.太湖流域土壤养分空间变异特征分析——以浙江省平湖市为例[J].浙江大学学报(农业与生命科学版),2003,29(1):76-82.
73. Sun B, Zhou, S L, Zhao Q G. Evaluation of spatial and temporal changes of soil quality based ongeostatistical analysis in the hill region of subtropical China [J]. Geoderma,2003,115:85-99.
74.钱永兰,杨邦杰,雷廷武.数据融合及其在农情遥感监测中的应用与展望[J].农业工程学报,2004,20(4):286-290.
75.钱永兰,杨邦杰,焦险峰.基于遥感抽样的国家尺度农作物面积统计方法评估[J].农业工程学报,2007,23(11):180-186.
76.唐娟,王文娣,王辉等.温室番茄生长中多传感器数据融合的应用[J].微计算机信息,2007,23(16):192-193,228.
77.许宗林,苟曦,李昆等.四川省耕地土壤养分分布特征与动态变化趋势探讨[J].西南农业学报,2008,21(3):718-725.
78.张宇林.计算智能在土壤信息融合中的应用研究[D].江南大学博士学位论文,2009.
79.王小晟,吴顺祥.对几种处理不确定性信息的理论的比较研究[J].计算机工程与应用,2005.12:51-55.
80. Schumann G, Pappenberger F and Matgen P. Estimating uncertainty associated with water stages froma single SAR image [J]. Advances in Water Resources,2008,31(8):1038-1047.
81. Tang Y, Sun S and Li Z. The generalized combination of evidence involved a priori knowledge[C].Proc.2003IEEE Int'l Conf. on Systems, Man and Cybern., Washington, D.C., Oct.5-8,2003,5:4998-5003.
82. Zhu P Y, Zhong Q, Yang J, Xu B G. An improved D-S evidence theory for the application ofbio-acoustic frightening system [C]. International Conference in Electrics, Communication andAutomatic Control, Springer, v165, LNEE,2011,1573-1580
83. Zhu P Y, Xiong W L. Qin N N, Xu B G. D-S theory based on an improved PSO for data fusion [J].Journal of Networks,2012,7(2):270-276.
84. Mahler. R P S. Statistical multisource-multitarget information fusion [M]. London: Artech House.2007.
85. Khaleghi B, Khamis A, and Karray F. Random finite set theoretic based soft/hard data fusion withapplication for target tracking[C]. IEEE Conference on Multisensor Fusion and Integration forIntelligent Systems (MFI),2010,50-55.
86. Archie G E. The electric resistivity log as an aid in determining some reservoir characteristics [J].Trans. Am. Inst. Min. Metall. Pet. Eng.1942,146,54-62.
87. Corwin D L, Lesch S M. Apparent soil electrical conductivity measurements in agriculture [J].Computers and Electronics in Agriculture,2005,46:11-43.
88. Huber P J. Robust Statistics [M]. New York: Wiley,1981.
89. Simon D. Optimal state estimations. John Wiley&Sons, Inc., Hoboken, New Jersey,2006.
90. Mitchell H B. Data fusion: concepts and ideas [M]. Springer, Heidelberg,2012.
91.王国胤,姚一豫,于洪.粗糙集理论与应用研究综述[J].计算机学报,2009,32(7):1229-1246.
92. Dempster A. Upper and lower probabilities induced by multivalued mapping [J]. Ann. Math. Statist.1967,38:325-339.
93.原新,朱齐丹,兰海.基于粗糙集理论的多传感器信息融合[J].哈尔滨工业大学学报,2006,38(10):1669-1672.
94. Vapnik V. An overview of statistical learning theory [J]. IEEE Transactions on Neural Networks,1999,10(5):988-999.
95.朱培逸,钟强,徐保国.一类简约的粒子群算法[J].小型微型计算机系统,2012,33(4):800-803.
96.朱培逸,张宇林.基于动态权值的粒子群算法的多样性分析[J].石油化工高等学校学报,2008,21(4):91-94.
97. Shafer G. A mathematical theory of evidence [M]. America: Princeton University Press,1976.
98. Murphy C K. Combining belief functions when evidence conflicts [J]. Decision and Support Systems,2000,29:1-9.
99. Zhang Z Y. Determining the epipolar geometry and its uncertainty a review [J]. International Journalcomputer vision,1998,27(2):161-198.
100. Fischler M A, Bolles R C. Random sample consensus: A paradigm for model fitting with applicationsto image analysis and automated cartography [J]. Communication of the ACM,1981,24(6):381-395.
101. Chen J H, Chen C S, Chen Y S. Fast algorithm for robust template matching with M-estimators [J].IEEE Transactions on Signal Processing,2003,51(1):230-243.
102. Zhu P Y, Zhong Q, Xiong W L. Xu B G. A robust least squares for information estimation fusion [J].International Journal of Digital Content Technology and its Applications,2011,5(4):65-73.
103. Zhu P Y, Liu Z, Xu B L, Xu B G. Soil information fusion estimation algorithm by robust least squaresand wavelet analysis [J]. Journal of Convergence Information Technology,2011,6(6):263-270.
104. Tanja V, Haars V, etc. Automatic sensor management: challenges and solutions [C]. Proc. of SPIEDefence and Security Conference, Optics and Photonics in Global Homeland Security, Orlando (FL),March2008,6945:1-11.
105. Bolderheij F, Van Genderen P. Mission driven sensor management[C]. Stockholm: Proc.7th Int. Conf.on Information Fusion;2004.799-804.
106. Van Norden W L, DeJong J L, Bolderheij F, Rothkrantz L J M. Intelligent task scheduling in sensornetwork[C]. Philadelphia: P roc.8th Int. Conf. on Information Fusion,2005.
107. Van Norden W L. Intelligent task scheduling in sensor networks: introducing three new schedulingmethodologies [D], Delft University of Technology, Netherlands,2005.
108. Alfred O H III, Douglas C. Sensor management: past, present, and future [J]. IEEE Sensors Journal,2011,11(12):3064-3075.
109. Musick S, Malhotra R. Chasing the elusive sensor manager[C]. Proc. IEEE Nat. Aerosp. Electron.Conf.(NAECON),1994,606-613.
110. Hero A D, Casta ón A D. Cochran, K. Kastella. Foundations and applications of sensor management[M]. Springer, Heidelberg,2008.
111. Bertsekas D P, Casta ón D A. Rollout algorithms for stochastic scheduling problems [J]. Journal ofHeuristics,1999,5(1):89-108.
112. Krishnamurthy V, Evans R. J. Correction to ‘Hidden Markov model multiarm bandits: A methodologyfor beam scheduling in multi-target tracking [J]. IEEE Trans. Signal Process,2003,51(6):1662-1663.
113. Krishnamurthy V, and Evans R. J. Hidden Markov model multiarm bandits: A methodology for beamscheduling in multitarget tracking [J]. IEEE Trans. Signal Process.2001,49(12):2893-2908.
114. Goodman N A, Venkata, P R, Neifeld M. A. Adaptive waveform design and sequential hypothesistesting for target recognition with active sensors [J]. IEEE Journal of Selected Topics in SignalProcessing.2007,1(1):105-113.
115. Washburn R B, Schneider M K, Fox J J. Stochastic dynamic programming based approaches to sensorresource management[C]. In Proceedings of the5th International Conference on Information Fusion,Annapolis, Maryland, USA,2002,1:608-615.
116. Zhu P Y, Zhong Q, Xiong W L, Xu B G. A sensor management method based on an improved psoalgorithm [J]. International Journal of Advancements in Computing Technology,2012,4(9):259-265.
117. Cheng X Z, Narahari B, Simha R etc. Strong minimum energy topology in wireless sensor networks:np-completeness and heuristics [J]. IEEE Transactions on Mobile Computing,2003,2(3):248-256.
118. Shi Y H, Eberhart R C. Parameter selection in particle swarm optimization[C].The7th AnnualConference on Evolutionary Programming, San Diego, USA.
119. Kalyan V, Lisa O. Dynamic sensor management using multiobjective particle swarm optimizer[C].SPIE Aerosense2004.
120. Varshney P K. Distributed detection and data fusion [M]. Springer-Verlag,1997.
2. Halld L. Mathematical technique in multi-sensor data fusion [M]. London: Artech House,2000.
3. Richard T. Principles of effective multi-sensor data fusion [J]. Military Technology,2003,27(5):29-37.
4.李小昱,王为等.多传感器信息融合技术及在农业工程中的应用前景[J].农业工程学报,2003,19(3):10-13.
5. Dabid H, James L. An introduction to multi-sensor data fusion[C]. Proceedings of the IEEE,1997:6-
23.
6. Vershinin Y A. A data fusion algorithm for multi-sensor systems [C]. Proceedings of the Fainternational Conference on Information Fusion.2009,1(8):3l4-345.
7. Huang G S, Wang S W, Xiao F, Sun Y J. A data fusion scheme for building automation systems ofbuilding central chilling plants [J]. Automation in Construction,2009,18(3):302-309.
8. Hyder A K. Multisensor fusion [M]. Netherlands: Kluwer Academic Publishers,2006:1-39.
9.夏胜平,滕召胜,郁文贤等.智能粮食收购系统的设计及其水分检测数据融合[J].农业现代化研究,2000,21(1):24-27.
10. Zhu P Y, XU B G. Fusion of ECadata using SVM and rough sets theory augmented by PSO [J]. Journalof Computational Information Systems,2011.7(1):295-302.
11.刘顺国,邢岩,李金凤等.“数字土壤”及其在测土配方施肥中的应用前景[J].安徽农业科学.2007,35(6):1470-1471.
12. Wong M T F, Asseng S, Robertson M. J. and Oliver Y. Mapping subsoil acidity and shallow soil acrossa field with information from yield maps, geophysical sensing and the grower [J]. Precision Agriculture,2008,9(1-2): pp3-15.
13. Luo B C, Yuan K, Chen J L. Uncertainty analysis based dynamic multi-sensor data fusion [J]. ActaAutomatic Sinica,2009,30(3):407-415.
14. Oberkampf W L, Helton J C, Joslyn C A, et a1. Challenge problems: uncertainty in system responsegiven uncertain parameters[J]. Reliability Engineering&System Safety,2004,85(1-3):11-19.
15. Oberkampf W L, Helton J C, Sentz K. Mathematical representation of uncertainty [C]．Proceedings ofthe41st AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference,AIAA-2001-1645, Seattle, April2001,16-19.
16. Klir G J. Uncertainty and information: foundations of generalized information theory [M]. Wiley-Interscience, Hoboken, NJ,2006.
17. Walley P. Measures of uncertainty in expert systems [J]. Artificial Intelligence,1996,83(1):1-58.
18.王清印,刘志勇.不确定性信息的概念、类别及其数学表述[J].运筹与管理,2001(4):9-15.
19.赵秀恒.不确定性系统理论及其在预测与决策中的应用[M].冶金工业出版社,2010.
20.万玉成.基于未确知性的预测与决策方法及其应用研究[D].东南大学博士学位论文,2004年.
21.王清印,刘志勇.不确定性系统理论基础概要[C].第二届不确定系统年会论文集.2004,
22. White F E. Data fusion lexicon. U. S. Department of defence, data fusion sub-panel of the jointdirectors of laboratories, Technical Panel for C3,1987.
23. Klein L A. Sensor and data fusion foncepts and application (second edition)[M]. Bellingham: SPIEOptical Press,1990.
24.韩崇昭,朱洪艳,段战胜等.多源信息融合[M].北京:清华大学出版社,2006.
25. Hall D, Linas L J. An introduction to multi-sensor data fusion [J]. Proc of the IEEE,1997,85(1):6-23.
26. Bahador K, Alaa K, et al. Multisensor data fusion: A review of the state-of-the-art [J]. InformationFusion, In Press.
27. Smets P. Analyzing the combination of conflicting belief functions [J]. Information Fusion,2007,8(4):387-412.
28. Mahler. R P S.“Statistics101” for multisensor, multitarget data fusion [J]. IEEE Aerospace andElectronic Systems Magazine,2004,19(1):53-64.
29. Smith D, Singh S. Approaches to multisensor data fusion in target tracking: a survey [J], IEEETransactions on Knowledge and Data Engineering,2006,18(12):1696-1710.
30. Zhu Y, Song E, Zhou J, You Z. Optimal dimensionality reduction of sensor data in multisensorestimation fusion [J], IEEE Transactions on Signal Processing,2005,53(5):1631-1639.
31. Milenova B L, Campos M M. Mining high-dimensional data for information fusion: a database-centricapproach[C]. Proc. of International Conference on Information Fusion,2005:638-645.
32. Yager R R. A framework for multi-source data fusion [J]. Information Sciences,2004,(163):175-200.
33.王壮. C4ISR系统目标综合识别理论与技术研究[D].国防科技大学博士学位论文,2001.
34.徐毓.雷达网数据融合问题研究[D].清华大学博士学位论文,2003.
35.王明辉.多传感器数据融合跟踪算法研究[D].清华大学博士后出站报告,2003.
36. Svensson P. Information fusion in the future Swedish RMA defence [J]. Journal of Military Technology,2002,2000(3):22-28.
37.马骏.信息融合及其在目标识别的应用[D].国防科技大学博士学位论文,1997.
38.杨露菁,余华.多源信息融合理论与应用[M].北京:北京邮电大学出版社,2006.
39.管天云.多传感器信息融合研究[D].浙江大学博士学位论文,1998.
40. Kam M, Zhu X, Kalata P. Sensor fusion for mobile robot navigation[C]. Proc. Of the IEEE SpecialIssue on Sensor Fusion,1997,85(1):108-119.
41. Wu Y G, Tang J Y, Liu K. Obstacle detection and environment modeling based on multisensor fusionfor robot navigation[J]. AI in Engineering,1996,10(4):323-333.
42. Allen P k. Integration vision and touch for object recognition tasks [J]. International Journal ofRobotics Research,1988,7(6):15-33.
43. Hernandez A I, Garrault G, Mora F et al. Multisensor fusion for atria and ventricular activity detectionin coronary care monitoring [J]. IEEE Transon Biomed Eng,1999,46(10):1186-1190.
44. Mort N, Prajitno P. A multi-sensor data fusion based target tracing system [C].2002IEEEInternational Conference on industrial Technology, Dec.2002,1(11-14):427-432.
45.敬忠良.神经网络技术与应用[M].北京:国防工业出版社,1995.
46.杨万海.多传感器数据融合及其应用[M].西安:西安电子科技大学出版社,2004.
47.康耀红.数据融合理论与应用[M].西安:西安电子科技大学出版社,1997.
48.权太范.信息融合神经网络-模糊推理理论与应用[M].北京:国防工业出版社,2002.
49.周宏仁,敬忠良,王培德.机动目标跟踪[M].北京:国防工业出版社,1991.
50.王润生.信息融合[M].北京:科学出版社,2007.
51.刘同明,夏祖勋,解洪成.数据融合技术及应用[M].北京:国防工业出版社,1998.
52.文成林,周东华.多尺度估计理论及应用[M].北京:清华大学出版社,2002.
53.文成林.多尺度动态建模理论及其应用[M].北京:科学出版社,2008.
54.韩崇昭,朱洪艳.多源信息融合[M].北京:清华大学出版社,2006.
55.高倩.鲁棒数据校正理论与应用研究[D].上海交通大学博士论文,2007.
56.何友.信息融合理论及应用[M].北京:电子工业出版社,2010.
57.潘泉,梁彦,杨峰.现代目标跟踪与信息融合[M].北京:国防工业出版社,2009.
58.邓自立.最优估计理论及其应用——建模滤波信息融合估计[M].哈尔滨:哈尔滨工业大学出版社,2005.
59.邓自立.信息融合滤波理论及其应用[M].哈尔滨:哈尔滨工业大学出版社,2007.
60.彭冬亮,文成林,薛安克.多传感器多源信息融合理论及应用[M].北京:科学出版社,2010.
61. Noguchi N, Reid J F, Will J, et al. Vehicle automation system based on multi-sensor integration[C].ASAE Annual International Meeting, Orlando, FL, USA,1998.
62. Nunez, J. Multiresolution-based image fusion with additive wavelet decomposition [J]. IEEE Trans.Geosci. Remote Sens.,1999,37(3):1204-1211.
63. Bruzzone L, Prieto D F. An adaptive semiparametric and context-based approach to unsupervisedchange detection in multitemporal remote-sensing images [J]. IEEE Transactions on Image Processing,2002,11(4):452-466.
64. Steinmetz V, Crochon M, Bellon-Maurel V, et al. Sensors for fruit firmness assessment: comparisonand fusion [J]. Journal of Agricultural Engineering Research,1996,64:15-28.
65. Hirafuji, M, Fukatsu T and Hu H M. Full-Wireless Field Monitoring server for advanced sensornetwork[C], Proc. of AFITA/WCCA, Bangkok, Thailand, Aug.2004,686-691.
66. Sun Y R, Ma D K, Schulze L P. On-the-go measurement of soil water content and mechanicalresistance by a combined horizontal penetrometer [J]. Soil Till Res,2006,86:209-217.
67. Topp G C, Lapen D R, Edwards M J, et a1. Laboratory calibration, in-field validation and used of a soilpenetrometer measuring cone resistance and water content [J]. Vadose Zone Journal,2003,2:633-641.
68.王文杰,唐娉,朱重光.一种基于小波变换的图像融合算法[J].中国图像图形学报,2001,6(11):1130-1135.
69.滕召胜,蔡铁,王可宁.一种智能化粮情自动检测系统[J].农业工程学报,2001,17(4):144-157.
70.滕召胜.基于自适应加权数据融合的粮食水分快速测定仪[J].农业机械学报,1999,30(6):64-67.
71.张明社,李小昱,雷廷武等.基于神经网络的数据融合法测量水流泥沙含量[J].农业工程学报,2002,18(4):41-43.
72.刘杏梅,徐建明,章明奎等.太湖流域土壤养分空间变异特征分析——以浙江省平湖市为例[J].浙江大学学报(农业与生命科学版),2003,29(1):76-82.
73. Sun B, Zhou, S L, Zhao Q G. Evaluation of spatial and temporal changes of soil quality based ongeostatistical analysis in the hill region of subtropical China [J]. Geoderma,2003,115:85-99.
74.钱永兰,杨邦杰,雷廷武.数据融合及其在农情遥感监测中的应用与展望[J].农业工程学报,2004,20(4):286-290.
75.钱永兰,杨邦杰,焦险峰.基于遥感抽样的国家尺度农作物面积统计方法评估[J].农业工程学报,2007,23(11):180-186.
76.唐娟,王文娣,王辉等.温室番茄生长中多传感器数据融合的应用[J].微计算机信息,2007,23(16):192-193,228.
77.许宗林,苟曦,李昆等.四川省耕地土壤养分分布特征与动态变化趋势探讨[J].西南农业学报,2008,21(3):718-725.
78.张宇林.计算智能在土壤信息融合中的应用研究[D].江南大学博士学位论文,2009.
79.王小晟,吴顺祥.对几种处理不确定性信息的理论的比较研究[J].计算机工程与应用,2005.12:51-55.
80. Schumann G, Pappenberger F and Matgen P. Estimating uncertainty associated with water stages froma single SAR image [J]. Advances in Water Resources,2008,31(8):1038-1047.
81. Tang Y, Sun S and Li Z. The generalized combination of evidence involved a priori knowledge[C].Proc.2003IEEE Int'l Conf. on Systems, Man and Cybern., Washington, D.C., Oct.5-8,2003,5:4998-5003.
82. Zhu P Y, Zhong Q, Yang J, Xu B G. An improved D-S evidence theory for the application ofbio-acoustic frightening system [C]. International Conference in Electrics, Communication andAutomatic Control, Springer, v165, LNEE,2011,1573-1580
83. Zhu P Y, Xiong W L. Qin N N, Xu B G. D-S theory based on an improved PSO for data fusion [J].Journal of Networks,2012,7(2):270-276.
84. Mahler. R P S. Statistical multisource-multitarget information fusion [M]. London: Artech House.2007.
85. Khaleghi B, Khamis A, and Karray F. Random finite set theoretic based soft/hard data fusion withapplication for target tracking[C]. IEEE Conference on Multisensor Fusion and Integration forIntelligent Systems (MFI),2010,50-55.
86. Archie G E. The electric resistivity log as an aid in determining some reservoir characteristics [J].Trans. Am. Inst. Min. Metall. Pet. Eng.1942,146,54-62.
87. Corwin D L, Lesch S M. Apparent soil electrical conductivity measurements in agriculture [J].Computers and Electronics in Agriculture,2005,46:11-43.
88. Huber P J. Robust Statistics [M]. New York: Wiley,1981.
89. Simon D. Optimal state estimations. John Wiley&Sons, Inc., Hoboken, New Jersey,2006.
90. Mitchell H B. Data fusion: concepts and ideas [M]. Springer, Heidelberg,2012.
91.王国胤,姚一豫,于洪.粗糙集理论与应用研究综述[J].计算机学报,2009,32(7):1229-1246.
92. Dempster A. Upper and lower probabilities induced by multivalued mapping [J]. Ann. Math. Statist.1967,38:325-339.
93.原新,朱齐丹,兰海.基于粗糙集理论的多传感器信息融合[J].哈尔滨工业大学学报,2006,38(10):1669-1672.
94. Vapnik V. An overview of statistical learning theory [J]. IEEE Transactions on Neural Networks,1999,10(5):988-999.
95.朱培逸,钟强,徐保国.一类简约的粒子群算法[J].小型微型计算机系统,2012,33(4):800-803.
96.朱培逸,张宇林.基于动态权值的粒子群算法的多样性分析[J].石油化工高等学校学报,2008,21(4):91-94.
97. Shafer G. A mathematical theory of evidence [M]. America: Princeton University Press,1976.
98. Murphy C K. Combining belief functions when evidence conflicts [J]. Decision and Support Systems,2000,29:1-9.
99. Zhang Z Y. Determining the epipolar geometry and its uncertainty a review [J]. International Journalcomputer vision,1998,27(2):161-198.
100. Fischler M A, Bolles R C. Random sample consensus: A paradigm for model fitting with applicationsto image analysis and automated cartography [J]. Communication of the ACM,1981,24(6):381-395.
101. Chen J H, Chen C S, Chen Y S. Fast algorithm for robust template matching with M-estimators [J].IEEE Transactions on Signal Processing,2003,51(1):230-243.
102. Zhu P Y, Zhong Q, Xiong W L. Xu B G. A robust least squares for information estimation fusion [J].International Journal of Digital Content Technology and its Applications,2011,5(4):65-73.
103. Zhu P Y, Liu Z, Xu B L, Xu B G. Soil information fusion estimation algorithm by robust least squaresand wavelet analysis [J]. Journal of Convergence Information Technology,2011,6(6):263-270.
104. Tanja V, Haars V, etc. Automatic sensor management: challenges and solutions [C]. Proc. of SPIEDefence and Security Conference, Optics and Photonics in Global Homeland Security, Orlando (FL),March2008,6945:1-11.
105. Bolderheij F, Van Genderen P. Mission driven sensor management[C]. Stockholm: Proc.7th Int. Conf.on Information Fusion;2004.799-804.
106. Van Norden W L, DeJong J L, Bolderheij F, Rothkrantz L J M. Intelligent task scheduling in sensornetwork[C]. Philadelphia: P roc.8th Int. Conf. on Information Fusion,2005.
107. Van Norden W L. Intelligent task scheduling in sensor networks: introducing three new schedulingmethodologies [D], Delft University of Technology, Netherlands,2005.
108. Alfred O H III, Douglas C. Sensor management: past, present, and future [J]. IEEE Sensors Journal,2011,11(12):3064-3075.
109. Musick S, Malhotra R. Chasing the elusive sensor manager[C]. Proc. IEEE Nat. Aerosp. Electron.Conf.(NAECON),1994,606-613.
110. Hero A D, Casta ón A D. Cochran, K. Kastella. Foundations and applications of sensor management[M]. Springer, Heidelberg,2008.
111. Bertsekas D P, Casta ón D A. Rollout algorithms for stochastic scheduling problems [J]. Journal ofHeuristics,1999,5(1):89-108.
112. Krishnamurthy V, Evans R. J. Correction to ‘Hidden Markov model multiarm bandits: A methodologyfor beam scheduling in multi-target tracking [J]. IEEE Trans. Signal Process,2003,51(6):1662-1663.
113. Krishnamurthy V, and Evans R. J. Hidden Markov model multiarm bandits: A methodology for beamscheduling in multitarget tracking [J]. IEEE Trans. Signal Process.2001,49(12):2893-2908.
114. Goodman N A, Venkata, P R, Neifeld M. A. Adaptive waveform design and sequential hypothesistesting for target recognition with active sensors [J]. IEEE Journal of Selected Topics in SignalProcessing.2007,1(1):105-113.
115. Washburn R B, Schneider M K, Fox J J. Stochastic dynamic programming based approaches to sensorresource management[C]. In Proceedings of the5th International Conference on Information Fusion,Annapolis, Maryland, USA,2002,1:608-615.
116. Zhu P Y, Zhong Q, Xiong W L, Xu B G. A sensor management method based on an improved psoalgorithm [J]. International Journal of Advancements in Computing Technology,2012,4(9):259-265.
117. Cheng X Z, Narahari B, Simha R etc. Strong minimum energy topology in wireless sensor networks:np-completeness and heuristics [J]. IEEE Transactions on Mobile Computing,2003,2(3):248-256.
118. Shi Y H, Eberhart R C. Parameter selection in particle swarm optimization[C].The7th AnnualConference on Evolutionary Programming, San Diego, USA.
119. Kalyan V, Lisa O. Dynamic sensor management using multiobjective particle swarm optimizer[C].SPIE Aerosense2004.
120. Varshney P K. Distributed detection and data fusion [M]. Springer-Verlag,1997.
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