自适应数据融合技术研究
|
摘要
数据融合系统的性能通常受到传感器特性、环境和目标特性、融合模型、融合过程等多种因素影响。传感器、环境和目标特性的动态变化使得融合过程、融合模型、融合参数固定不变的数据融合系统难以取得较为理想的结果。提高数据融合系统的自适应性、减弱外界因素对融合系统性能的影响,是数据融合技术的重要研究内容。论文围绕数据融合系统自适应能力提升开展研究工作,主要包括:
进行了航迹关联不确定度评定技术研究。基于信息不确定性度量理论,明确了航迹关联不确定度的概念;参考测量不确定度评定方法,对航迹关联不确定度评定方法进行了改进,使得评定方法更易理解、更加简单。
提出并实现了一种自适应航迹数据相关方法。传统的航迹序贯相关方法中,序贯长度通常为设定的固定值。论文基于航迹关联不确定度评定结果,在线调整航迹相关方法的序贯长度,使得航迹相关判定的及时性与可靠性相统一,提升航迹序贯相关方法对传感器精度、目标分布及航路的适应性。
提出并实现了基于数据驱动的系统误差估计方法。针对传统的传感器系统误差估计方法存在较大残余误差的问题,采用迭代学习方法,利用协作目标的高精度定位信息,建立传感器系统误差数值模型。
进行了自适应数据融合系统的系统结构、处理流程和软件设计、实现与仿真实验。实验结果表明,航迹相关方法的序贯长度能够根据目标分布、航路以及航迹精度自动调整,能够及时输出可靠的判决结果;传感器系统误差经校准后,残余系统误差较小;数据融合系统对传感器特性和目标特性的变化具有一定的自适应。
The overall performance of data fusion systems is affected by the nature of the sensors, the operational environment and the targets, the adopted fusion models, process, and so on. The dynamic changes of the nature of the sensors, the operational environment and the targets lead to that the data fusion systems with changeless fusion models, fusion process and fusion parameter wouldn’t get perfect results. Enhancing the adaptability of data fusion systems and weakening the effect of outside factors on the performances of fusion system is a hot research problem. This thesis thus focuses on the improvement in the adaptability of data fusion system. The idiographic research is as follows:
The evaluation of the track correlation uncertainty is researched. The concept of the uncertainty of track correlation is defined based on the uncertainty theory. The evaluation method for track correlation uncertainty is revised based on the measurement uncertainty. The revised method can be comprehended and implemented more easily.
An adaptive method for data correlation is presented and implemented. The length of sequence disposal usually is set changelessly. With the evaluating of track correlation uncertainty, the length of sequence is made to be self- adjustable, which makes the decicision of the data correlation more timely and more reliable, and so improved the adaptability of data correlation considering the different sensor accuracy, target distribution and routes.
A sensor bias estimation method is presented and implemented with the base of data driven control theory. The traditional estimation methods often have large residual errors. Based on iterative learning approach, a data model of bias estimation is implemented with the use of the high precision positioning information of the collaborative targets.
The architecture and process model of an adaptive data fusion system is designed, the corresponding software is developed and the simulation experiments is made. The experiment results show that the data correlation can adjust the length of sequence disposal according to the sensor accuracy, the target distribution and routes, the bias estimation method can acquire less residual bias and the data fusion system thus achives the capability in adapting the change of the nature of sensors and targets.
进行了航迹关联不确定度评定技术研究。基于信息不确定性度量理论,明确了航迹关联不确定度的概念;参考测量不确定度评定方法,对航迹关联不确定度评定方法进行了改进,使得评定方法更易理解、更加简单。
提出并实现了一种自适应航迹数据相关方法。传统的航迹序贯相关方法中,序贯长度通常为设定的固定值。论文基于航迹关联不确定度评定结果,在线调整航迹相关方法的序贯长度,使得航迹相关判定的及时性与可靠性相统一,提升航迹序贯相关方法对传感器精度、目标分布及航路的适应性。
提出并实现了基于数据驱动的系统误差估计方法。针对传统的传感器系统误差估计方法存在较大残余误差的问题,采用迭代学习方法,利用协作目标的高精度定位信息,建立传感器系统误差数值模型。
进行了自适应数据融合系统的系统结构、处理流程和软件设计、实现与仿真实验。实验结果表明,航迹相关方法的序贯长度能够根据目标分布、航路以及航迹精度自动调整,能够及时输出可靠的判决结果;传感器系统误差经校准后,残余系统误差较小;数据融合系统对传感器特性和目标特性的变化具有一定的自适应。
The overall performance of data fusion systems is affected by the nature of the sensors, the operational environment and the targets, the adopted fusion models, process, and so on. The dynamic changes of the nature of the sensors, the operational environment and the targets lead to that the data fusion systems with changeless fusion models, fusion process and fusion parameter wouldn’t get perfect results. Enhancing the adaptability of data fusion systems and weakening the effect of outside factors on the performances of fusion system is a hot research problem. This thesis thus focuses on the improvement in the adaptability of data fusion system. The idiographic research is as follows:
The evaluation of the track correlation uncertainty is researched. The concept of the uncertainty of track correlation is defined based on the uncertainty theory. The evaluation method for track correlation uncertainty is revised based on the measurement uncertainty. The revised method can be comprehended and implemented more easily.
An adaptive method for data correlation is presented and implemented. The length of sequence disposal usually is set changelessly. With the evaluating of track correlation uncertainty, the length of sequence is made to be self- adjustable, which makes the decicision of the data correlation more timely and more reliable, and so improved the adaptability of data correlation considering the different sensor accuracy, target distribution and routes.
A sensor bias estimation method is presented and implemented with the base of data driven control theory. The traditional estimation methods often have large residual errors. Based on iterative learning approach, a data model of bias estimation is implemented with the use of the high precision positioning information of the collaborative targets.
The architecture and process model of an adaptive data fusion system is designed, the corresponding software is developed and the simulation experiments is made. The experiment results show that the data correlation can adjust the length of sequence disposal according to the sensor accuracy, the target distribution and routes, the bias estimation method can acquire less residual bias and the data fusion system thus achives the capability in adapting the change of the nature of sensors and targets.
引文
[1] X.Rong Li. Evaluation of estimation algorithms part 1[J]: Incomprehensive measures of performance. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(4):1340-1358.
[2]李金海.误差理论与测量不确定度评定[M].北京:中国计量出版社, 2003:108-110.
[3]韩崇昭、朱洪艳.多源信息融合[M].北京:清华大学出版社, 2007: 87-99.
[4]韩崇昭、周彬、元向辉等.共同杂波环境下多目标量测数据的误差传递与校正[J].自动化学报, 2007, 33(7):771-774.
[5]陈世友、李燕菲.航迹关联不确定度评定方法[J].华中科技大学学报, 2010(1):61-65.
[6]马琦、张记龙等.无线传感器网络簇内自适应融合算法研究[J].计算机应用研究, 2009, 26(7):2502-2507.
[7]陈亮、吴小俊等.基于Jerk的常增益目标跟踪及其自适应算法[J].计算机工程与应用, 2008, 44(31):72-77.
[8]赵智勇、吴小俊等.基于遗传算法的自适应机动多目标跟踪算法[J].弹箭与制导学报. 2009, 29(2):209-211.
[9]高世伟、郭雷等.一种新的粒子滤波目标跟踪算法[J].上海交通大学学报. 2009, 43(3):485-489.
[10]肖龙远、曾超.一种自适应二次数据融合算法[J].传感器与微系统, 2007(4).
[11]刘以安、陈松灿、杨华明.灰色优势分析在多雷达低空小目标跟踪中的应用[J].南京航空航天大学学报, 2002, 34(4):354-357.
[12] Zhou B Y, Leung H. Minimum entropy approach for multisensor data fusion[A]. In:Larson Ped. IEEE Signal Processing Workshop on High-Order Statistics(SPW-HOS`97)[C]. NY:Prentice Hall, 1997, 336-339.
[13] Jansen M, Roose D. Bayesian correction of wavelet threshold procedures for images denoising[J]. Bayesian Statistical Science, 1998, 35:245-251.
[14] Couso I, Montes S. Measurec fo fuzziness and information theory[A]. In:Soheil S ed. Information Processing and Management of Uncertainty in Knowledge[C]. NY: Prentice Hall, 1996.501-505.
[15] Fioretto A, Sgarro A. A second step information measure and the uncertainty of bodies of evidence[A]. In:Soheil S ed. Information Processing and Management of Uncertainty in Knowledge[C]. NY:Prentice Hall, 1996.687-691.
[16]尹国成、张德干等.基于熵模型的自适应信息融合方法[J].东北大学学报, 2002(3).
[17] Eseamilla-Ambrosio P J, Mort N. Hybrid Kalman filter-fuzzy logic adaptive muhisensor data fusion architectures[A]. Proceedings of the 42nd IEEE Conference on Decision and Control[C]. Maui, 2003:5215-5220.
[18] Tafti A D, Sadati N. Novel adaptive Kalman filtering and fuzzy track fusion approach for real time applications[A]. Proceedings of the 3rd IEEE Conference on Industrial Electronics and Application[C]. Singapore, 2008:120-125.
[19] Smith D, Singh S. Approaches to muhisensor data fusion in target tracking:A survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(2):1696-1710.
[20]贾彩娟、许晖.多模自适应滤波算法的性能改进方法[J].火力与指挥控制, 2009, 34(5).
[21] Yager R R. Uncertainty representation using fuzzy measures[J]. Fuzzy System, Man and Cybernetics, Part B, IEEE Transactions on, 2002, 32(1):13-20.
[22]陈湘晖等.扩展的粗糙集模型及其不确定性量度[J].清华大学学报(自软科学版), 2002, 42(1):128-131.
[23] Klir G J, Smith R M. On measuring uncertainty and uncertainty-based information: Recent developments[J]. Annals of Mathematecs and Artificial Intelligence, 2001, 32(1-4):5-33.
[24]王清印、刘志勇.不确定性系统理论基础概要[A].第二届不确定系统年会大会论文集[C],秦皇岛, 2004, 7:118-130.
[25] Pawlak Z. Rough sets[J]. International of Computer and Information Sciences, 1982, 11(5):341-356.
[26] HERRERA F,MARTINEZ L. A 2-Tuple Fuzzy Linguistic Representation Model for Computing with Words[J]. IEEE Trans on Fuzzy System, 2000, 8(6):746-752.
[27] RANDAL C N. Generating Verbal Descriptions of Colored Objects[J]. Towards Grounding Language in Perception. Proceedings of the Seventh IEEE Workshop on Application of Computer Vision (WACV/MOTION`05)2005, 1-8.
[28]郭云飞、薛安克、彭冬亮等.复杂环境下基于不确定基的信息描述和融合方法[A].第27届中国控制会议[C],杭州, 2008.
[29]马野、王孝通、戴耀.基于UKF的神经网络自适应全局信息融合方法[J].电子学报, 2005(10).
[30]宋清昆、郝敏.基于聚类算法的自适应模糊神经网络研究[A].第五届全国技术过程故障诊断与安全性学术会议[C], 2007(3).
[31]苏杭、刘小河.非线性系统的神经网络自适应控制[A].第十二届全国自动化应用技术学术交流会[C], 2006(7).
[32]原泉、董朝阳、王青.基于小波神经网络的多传感器自适应融合算法[J].北京航空航天大学学报,2008(11).
[33]魏东、马瑞平、张明廉等.基于神经网络的自适应非线性控制及仿真研究[J].北京航天航空大学学报, 2004, 30(3):217-221.
[34]徐湘元.自适应控制理论与应用[M].北京:机械工业出版社, 2005:22-30.
[35] Chiang-Ju Chien, King-Chuan Sun, A-Chen Wu, Li-Chen Fu. Arobust MRAC usingvariable structure design for multivariable plants[J]. Automatica, Volume 32, Issue 6, June 1996, Page 833-848.
[36] Sergio Jimenez-Garcia, Mario E. Magana. Modellingsimulation and gain scheduling control of large radiotelescopes[J]. Simulation Practice and Theory, Volume 8, Issues 3-4, 15 September 2000, Pages 141-160
[37] T.Knohl, W.M.Xu, H.Unbhauen. Indirect adaptive dual control for Hammerstein systems using ANN[J]. Control Engineering Practice, Volume11, Issue 4, April 2003, Pages 377-385.
[38]徐湘元.自适应控制理论与应用[M].北京:机械工业出版社, 2005.
[39] D.L.Hall and J.Linas. Handbook of Multisensor Data Fusion[M]. The Electrical Engineering and Applied Signal Processing Series, CRC Press, 2001.
[40]赵宗贵.信息融合技术现状、概念与结构模型[J].中国电子科学研究院学报. 2006(4).
[41]喻卫斌、晓勇.现代不确定性理论的比较研究[J].经济问题, 2008(3).
[42]蔡星.基于不确定性理论的多目标决策研究[D].浙江:浙江工商大学, 2008.
[43]杨星.粗糙集理论与相关不确定性理论的辨证研究[J].微电子学与计算机, 2006(3).
[44]刘后胜.基于不确定理论和机器学习的知识发现研究[D].合肥:中国科学技术大学, 2008.
[45]张建国.不确定结构(机构)分析和可靠性专题研究[D].西安:西安电子科技大学: 2006.
[46] K.G. Klir, Uncertainty and information: foundations of generalized informationtheory[M]. Hoboken: John Wiley & Sons, Inc., 2006.
[47] K. G. Klir, Uncertainty and information: foundations of generalized information theory[M]. Hoboken: John Wiley & Sons, Inc., 2006.
[48]林洪桦.测量误差与不确定度评估[M].北京:机械工业出版社, 2010.
[49] BIPM-IEC-IFFC-ISO_IUPAC-IUPAP-OIML. Guide to the Expression of uncertainty in measurement[S]. ISO, Geneva, Switzerland, 1993.
[50]张晶、何友、熊伟.集中交互式多传感器模糊联合概率数据互联算法[J].电子学报, 2008, 36(8):1655-1659.
[51] D.E.Maurer. Information Handover for Track-to-track Correlation[J]. Information Fusion, 2003, 4(2):281-295.
[52]石玥、王钺、王树刚等.基于目标参照拓扑的模糊航迹关联方法[J].国防科技大学学报, 2006, 28(4) : 105-109.
[53]杨哲、韩崇昭、李晨等.基于目标之间拓扑信息的数据关联方法[J].系统仿真学报, 2008, 20(9):2357-2360.
[54] Y.Bar-Shalom and H.Chen. Multi-sensor track-to-track association for tracks with dependent errors[A]. 43th IEEE Conference on Decision and Control[C], Paradise Island,Bahamas, 2004:2674-2679.
[55]孙俊生、王建民、王维锋.多传感器多目标联合概率数据关联研究[J].微型机与应用, 2009(17).
[56]司锡才、陈玉坤、李志刚.数据关联算法的研究[J].哈尔滨工程大学学报, 2007(7).
[57]吴雯漫、周锐.多平台协同控制与协同目标分配方法[J].系统仿真学报,2008, 20:503-507.
[58]侯忠生、许建新.数据驱动控制理论及方法的回顾和展望[J].自动化学报, 2009, 35(6):650-667.
[59]赵晋、张建秋、高羽.迭代异方差估计及其在多传感器数据融合中的应用[J].电子学报,2008,36(10):1938-1943.
[60] JIN Hong-bin, PENG Yan, MA Jian-chao. Algorithm of Radar Registration Based on Unbiased Transformation[J]. Journal of System Simulation, 2008, 20(19):5399-5402.
[61]王红卫、祁超、魏永长等.基于数据的决策方法综述[J].自动化学报, 2009, 35(6):820-833.
[62] WANG Hong, CHAI Tian-You, DING Jin-Liang BROWN Martin. Data Driven Fault Diagnosis and Fault Tolerant Control:Some Advangces and Possible New Directions[J]. ACTA AUTOMATICA SINICA, 2009, 35(6):739-747.
[2]李金海.误差理论与测量不确定度评定[M].北京:中国计量出版社, 2003:108-110.
[3]韩崇昭、朱洪艳.多源信息融合[M].北京:清华大学出版社, 2007: 87-99.
[4]韩崇昭、周彬、元向辉等.共同杂波环境下多目标量测数据的误差传递与校正[J].自动化学报, 2007, 33(7):771-774.
[5]陈世友、李燕菲.航迹关联不确定度评定方法[J].华中科技大学学报, 2010(1):61-65.
[6]马琦、张记龙等.无线传感器网络簇内自适应融合算法研究[J].计算机应用研究, 2009, 26(7):2502-2507.
[7]陈亮、吴小俊等.基于Jerk的常增益目标跟踪及其自适应算法[J].计算机工程与应用, 2008, 44(31):72-77.
[8]赵智勇、吴小俊等.基于遗传算法的自适应机动多目标跟踪算法[J].弹箭与制导学报. 2009, 29(2):209-211.
[9]高世伟、郭雷等.一种新的粒子滤波目标跟踪算法[J].上海交通大学学报. 2009, 43(3):485-489.
[10]肖龙远、曾超.一种自适应二次数据融合算法[J].传感器与微系统, 2007(4).
[11]刘以安、陈松灿、杨华明.灰色优势分析在多雷达低空小目标跟踪中的应用[J].南京航空航天大学学报, 2002, 34(4):354-357.
[12] Zhou B Y, Leung H. Minimum entropy approach for multisensor data fusion[A]. In:Larson Ped. IEEE Signal Processing Workshop on High-Order Statistics(SPW-HOS`97)[C]. NY:Prentice Hall, 1997, 336-339.
[13] Jansen M, Roose D. Bayesian correction of wavelet threshold procedures for images denoising[J]. Bayesian Statistical Science, 1998, 35:245-251.
[14] Couso I, Montes S. Measurec fo fuzziness and information theory[A]. In:Soheil S ed. Information Processing and Management of Uncertainty in Knowledge[C]. NY: Prentice Hall, 1996.501-505.
[15] Fioretto A, Sgarro A. A second step information measure and the uncertainty of bodies of evidence[A]. In:Soheil S ed. Information Processing and Management of Uncertainty in Knowledge[C]. NY:Prentice Hall, 1996.687-691.
[16]尹国成、张德干等.基于熵模型的自适应信息融合方法[J].东北大学学报, 2002(3).
[17] Eseamilla-Ambrosio P J, Mort N. Hybrid Kalman filter-fuzzy logic adaptive muhisensor data fusion architectures[A]. Proceedings of the 42nd IEEE Conference on Decision and Control[C]. Maui, 2003:5215-5220.
[18] Tafti A D, Sadati N. Novel adaptive Kalman filtering and fuzzy track fusion approach for real time applications[A]. Proceedings of the 3rd IEEE Conference on Industrial Electronics and Application[C]. Singapore, 2008:120-125.
[19] Smith D, Singh S. Approaches to muhisensor data fusion in target tracking:A survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(2):1696-1710.
[20]贾彩娟、许晖.多模自适应滤波算法的性能改进方法[J].火力与指挥控制, 2009, 34(5).
[21] Yager R R. Uncertainty representation using fuzzy measures[J]. Fuzzy System, Man and Cybernetics, Part B, IEEE Transactions on, 2002, 32(1):13-20.
[22]陈湘晖等.扩展的粗糙集模型及其不确定性量度[J].清华大学学报(自软科学版), 2002, 42(1):128-131.
[23] Klir G J, Smith R M. On measuring uncertainty and uncertainty-based information: Recent developments[J]. Annals of Mathematecs and Artificial Intelligence, 2001, 32(1-4):5-33.
[24]王清印、刘志勇.不确定性系统理论基础概要[A].第二届不确定系统年会大会论文集[C],秦皇岛, 2004, 7:118-130.
[25] Pawlak Z. Rough sets[J]. International of Computer and Information Sciences, 1982, 11(5):341-356.
[26] HERRERA F,MARTINEZ L. A 2-Tuple Fuzzy Linguistic Representation Model for Computing with Words[J]. IEEE Trans on Fuzzy System, 2000, 8(6):746-752.
[27] RANDAL C N. Generating Verbal Descriptions of Colored Objects[J]. Towards Grounding Language in Perception. Proceedings of the Seventh IEEE Workshop on Application of Computer Vision (WACV/MOTION`05)2005, 1-8.
[28]郭云飞、薛安克、彭冬亮等.复杂环境下基于不确定基的信息描述和融合方法[A].第27届中国控制会议[C],杭州, 2008.
[29]马野、王孝通、戴耀.基于UKF的神经网络自适应全局信息融合方法[J].电子学报, 2005(10).
[30]宋清昆、郝敏.基于聚类算法的自适应模糊神经网络研究[A].第五届全国技术过程故障诊断与安全性学术会议[C], 2007(3).
[31]苏杭、刘小河.非线性系统的神经网络自适应控制[A].第十二届全国自动化应用技术学术交流会[C], 2006(7).
[32]原泉、董朝阳、王青.基于小波神经网络的多传感器自适应融合算法[J].北京航空航天大学学报,2008(11).
[33]魏东、马瑞平、张明廉等.基于神经网络的自适应非线性控制及仿真研究[J].北京航天航空大学学报, 2004, 30(3):217-221.
[34]徐湘元.自适应控制理论与应用[M].北京:机械工业出版社, 2005:22-30.
[35] Chiang-Ju Chien, King-Chuan Sun, A-Chen Wu, Li-Chen Fu. Arobust MRAC usingvariable structure design for multivariable plants[J]. Automatica, Volume 32, Issue 6, June 1996, Page 833-848.
[36] Sergio Jimenez-Garcia, Mario E. Magana. Modellingsimulation and gain scheduling control of large radiotelescopes[J]. Simulation Practice and Theory, Volume 8, Issues 3-4, 15 September 2000, Pages 141-160
[37] T.Knohl, W.M.Xu, H.Unbhauen. Indirect adaptive dual control for Hammerstein systems using ANN[J]. Control Engineering Practice, Volume11, Issue 4, April 2003, Pages 377-385.
[38]徐湘元.自适应控制理论与应用[M].北京:机械工业出版社, 2005.
[39] D.L.Hall and J.Linas. Handbook of Multisensor Data Fusion[M]. The Electrical Engineering and Applied Signal Processing Series, CRC Press, 2001.
[40]赵宗贵.信息融合技术现状、概念与结构模型[J].中国电子科学研究院学报. 2006(4).
[41]喻卫斌、晓勇.现代不确定性理论的比较研究[J].经济问题, 2008(3).
[42]蔡星.基于不确定性理论的多目标决策研究[D].浙江:浙江工商大学, 2008.
[43]杨星.粗糙集理论与相关不确定性理论的辨证研究[J].微电子学与计算机, 2006(3).
[44]刘后胜.基于不确定理论和机器学习的知识发现研究[D].合肥:中国科学技术大学, 2008.
[45]张建国.不确定结构(机构)分析和可靠性专题研究[D].西安:西安电子科技大学: 2006.
[46] K.G. Klir, Uncertainty and information: foundations of generalized informationtheory[M]. Hoboken: John Wiley & Sons, Inc., 2006.
[47] K. G. Klir, Uncertainty and information: foundations of generalized information theory[M]. Hoboken: John Wiley & Sons, Inc., 2006.
[48]林洪桦.测量误差与不确定度评估[M].北京:机械工业出版社, 2010.
[49] BIPM-IEC-IFFC-ISO_IUPAC-IUPAP-OIML. Guide to the Expression of uncertainty in measurement[S]. ISO, Geneva, Switzerland, 1993.
[50]张晶、何友、熊伟.集中交互式多传感器模糊联合概率数据互联算法[J].电子学报, 2008, 36(8):1655-1659.
[51] D.E.Maurer. Information Handover for Track-to-track Correlation[J]. Information Fusion, 2003, 4(2):281-295.
[52]石玥、王钺、王树刚等.基于目标参照拓扑的模糊航迹关联方法[J].国防科技大学学报, 2006, 28(4) : 105-109.
[53]杨哲、韩崇昭、李晨等.基于目标之间拓扑信息的数据关联方法[J].系统仿真学报, 2008, 20(9):2357-2360.
[54] Y.Bar-Shalom and H.Chen. Multi-sensor track-to-track association for tracks with dependent errors[A]. 43th IEEE Conference on Decision and Control[C], Paradise Island,Bahamas, 2004:2674-2679.
[55]孙俊生、王建民、王维锋.多传感器多目标联合概率数据关联研究[J].微型机与应用, 2009(17).
[56]司锡才、陈玉坤、李志刚.数据关联算法的研究[J].哈尔滨工程大学学报, 2007(7).
[57]吴雯漫、周锐.多平台协同控制与协同目标分配方法[J].系统仿真学报,2008, 20:503-507.
[58]侯忠生、许建新.数据驱动控制理论及方法的回顾和展望[J].自动化学报, 2009, 35(6):650-667.
[59]赵晋、张建秋、高羽.迭代异方差估计及其在多传感器数据融合中的应用[J].电子学报,2008,36(10):1938-1943.
[60] JIN Hong-bin, PENG Yan, MA Jian-chao. Algorithm of Radar Registration Based on Unbiased Transformation[J]. Journal of System Simulation, 2008, 20(19):5399-5402.
[61]王红卫、祁超、魏永长等.基于数据的决策方法综述[J].自动化学报, 2009, 35(6):820-833.
[62] WANG Hong, CHAI Tian-You, DING Jin-Liang BROWN Martin. Data Driven Fault Diagnosis and Fault Tolerant Control:Some Advangces and Possible New Directions[J]. ACTA AUTOMATICA SINICA, 2009, 35(6):739-747.