弱小目标检测与多传感器数据融合跟踪技术研究
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摘要
随着多传感器复杂大系统的不断出现,多传感器数据融合技术已经受到广泛关注。目前,各国都在竞相投入大量的人力、财力进行研究,使得数据融合技术已经成为有效处理多源信息的一个非常活跃的领域。西方发达国家已经研制和开发了数个军用数据融合系统。我国数据融合的研究水平和国外发达国家相比还较为落后,尤其在复杂环境下,数据融合的一些相关技术还缺乏准确性和自适应性。本文主要研究了数据融合中三个最基本又最重要的问题:目标的检测、跟踪和识别关键技术的实现问题。对于多传感器多目标的检测识别环境,往往是复杂背景弱小目标的情况,多信息的关联和融合、跟踪都是以低信噪比、弱小目标的检测为前提的。系统的每个传感器对每个弱小目标的检测效果越好,其融合、跟踪能力越理想。因而本文在研究多传感器对多目标的融合跟踪之前,首先讨论了以红外弱小目标的检测为对象的复杂背景下低信噪比、弱小目标的检测方法。主要研究内容及创新工作如下:
本文首先研究了红外图像的目标检测问题,针对低信噪比,海空背景的红外图像中的弱小目标检测问题,提出一种基于高斯内核的自适应滤波方法,并将它应用于红外图像的小波变换后的子图中抑制噪声,然后根据小波尺度分解的特点,把低频子图和水平子图以及垂直子图能量交叉加权,并进行小波重构。在此基础上,根据重构图像的信噪比,自适应的设定分割门限,分割红外图像中存在的可疑目标。实验表明,该算法易于工程实现,也能够满足实时性的要求。
其次,对于多传感器多目标跟踪问题,数据关联是其中一项重要问题,也是实现多传感器数据融合的前提。研究了两种情况的数据关联-同类传感器和异类传感器。在同类传感器数据关联研究时,针对多维分配算法S≥3时求解复杂度随着问题规模的增大呈指数规模增大,提出把它转化为用粒子群优化算法求解的组合优化问题,并通过量测约束,更快更有效找到最优解,实现数据关联;在异类传感器数据关联时,提出同时利用雷达和ESM不同类型的信息的数据关联方法。该方法基于模糊C-均值算法,把动力学信息和来自ESM的特征信息有效结合,应用到聚类过程中,提高数据关联精度。
再次,研究了目标航迹融合问题。采用一种利用模糊理论的自适应数据融合算法。对双传感器的滤波数据进行特征提取,并在一定的隶属函数和模糊规则下对其进行模糊推理,得到随目标机动情况自由调节加速度方差的系数调节值,使之保持对目标机动的快速响应,提高了跟踪精度。
最后,在多传感器目标识别决策融合模型的基础上研究了基于模糊积分的目标识别决策融合的实现问题。对模糊积分应用于决策层目标识别的决策层融合的核心问题—模糊密度赋值,针对仅利用训练样本先验静态信息的获取模糊密度算法的不足,提出一种利用训练样本先验静态信息结合传感器识别信息集之间的支持可信度对模糊密度进行自适应赋值的方法。仿真实验结果表明,自适应动态模糊密度赋值方法通过传感器信息的支持度对传感器模糊密度进行实时的更新,减少了错误的信息对融合的影响,提高了多传感器系统的可靠性和鲁棒性。
With the continuous appearances of complex multisensor large-scale systems, multisensor data fusion technique has attracted comprehensive attention. Studied with quantities of manpower and material resources in many countries, multisensor data fusion technique has been a very active field of processing multiple source information effectively. In western developed countries, some military data fusion systems have been researched and exploited. The study of data fusion in our country is still at a relatively low level, especially in complex environment, some correlative techniques in data fusion lack veracity and adaptiveness. Three important problems in date fusion, multisensor tagert detection, target tracking and recognition, are discussed in this dissertation. For multiple sensors multiple targets detection and identification environment, dim small targets are always in complex background, and the multiple information association, fusion, and tracking is on the premise of low SNR, dim small targets detection. The better results of dim small targets detection for each sensor of system, the better ability of fusion and tracking. Thus before multiple sensors multiple targets tracking and fusion, the detection methods for low SNR, dim small target in the complex background is researched first in this paper.The main research and innovation contents are as following:
Firstly, target detection in infrared image is reseached. With the problem of the small target detection in low SNR and sky-sea background conditions, the paper proposed a method which based on the adaptive filter with Gauss function.This method is used in the sub-image which was obtained by wavelet analysis in the infrared image in order to restrain the noise. According to the trait of the wavelet analysis, we multiply the coefficient of the LL sub-image、HL sub-image and LH sub-image as the new LL sub-image, then use this coefficient by conversed wavelet transform. The results in the aspects of the mean, the standard deviation and the SNR of the image were better. Then, we found the threshold adaptively to segment the target in the image according to the SNR value. The simulation experiment results show the method is prone to realization in engineering and satisfies the meet of real time.
Secondly, for multi-sensor multi-target tracking, data association is one of important problems, and is the precondition of data fusion. Two kinds of data association conditions - homogeneous sensors and heterogeneous sensors are reseaerched in this thesis. Consindered of data association for homogeneous sensors, the multiple dimensions assignment algorithm is used. This algorithm is NP-hard for S≥3, so it is transformed to a combination optimization problem and the Particle Swarm Optimization algorithm is used to solve this. Moreover, the measurements are restranited, then the solution can be found faster and more effective, and then data association problem is solved; Consindered of data association for heterogeneous sensors, a data association method of using two kinds of information from radar and ESM is proposed. It is based on the Fuzzy C-means algorithm, and dymatice information and characteristic information are used at the same time to cluster, so that the data association is enhanced.
Thirdly, the problem of target track fusion was researched. A fuzzy theory based adaptive data fusion algorithm is used. The feature extraction was made for the filter data from two sensors and then the fuzzy illation was done under certain subordinate functions and rules, thereby a coefficient was got respinse to the practical conditions, and fusion results enhance the precision.
Finally, the core problem in using fuzzy integral for decision fusion is to determine the fuzzy densities. According to shortage of the algorithm through that the fuzzy densities are determined only by aprior static information, a method of determining fuzzy densities adaptively is presented, which uses the apriori static information of the training samples and supporting reliability of each sensor's information. The simulation experiment results show that due to real-time update of fuzzy densities through the method of determining fuzzy densities adaptively using supporting reliability of each sensor's information, the effect of inaccurate information on fusion is depressed, and the reliability and robustness of multi-sensor system are enhanced.
本文首先研究了红外图像的目标检测问题,针对低信噪比,海空背景的红外图像中的弱小目标检测问题,提出一种基于高斯内核的自适应滤波方法,并将它应用于红外图像的小波变换后的子图中抑制噪声,然后根据小波尺度分解的特点,把低频子图和水平子图以及垂直子图能量交叉加权,并进行小波重构。在此基础上,根据重构图像的信噪比,自适应的设定分割门限,分割红外图像中存在的可疑目标。实验表明,该算法易于工程实现,也能够满足实时性的要求。
其次,对于多传感器多目标跟踪问题,数据关联是其中一项重要问题,也是实现多传感器数据融合的前提。研究了两种情况的数据关联-同类传感器和异类传感器。在同类传感器数据关联研究时,针对多维分配算法S≥3时求解复杂度随着问题规模的增大呈指数规模增大,提出把它转化为用粒子群优化算法求解的组合优化问题,并通过量测约束,更快更有效找到最优解,实现数据关联;在异类传感器数据关联时,提出同时利用雷达和ESM不同类型的信息的数据关联方法。该方法基于模糊C-均值算法,把动力学信息和来自ESM的特征信息有效结合,应用到聚类过程中,提高数据关联精度。
再次,研究了目标航迹融合问题。采用一种利用模糊理论的自适应数据融合算法。对双传感器的滤波数据进行特征提取,并在一定的隶属函数和模糊规则下对其进行模糊推理,得到随目标机动情况自由调节加速度方差的系数调节值,使之保持对目标机动的快速响应,提高了跟踪精度。
最后,在多传感器目标识别决策融合模型的基础上研究了基于模糊积分的目标识别决策融合的实现问题。对模糊积分应用于决策层目标识别的决策层融合的核心问题—模糊密度赋值,针对仅利用训练样本先验静态信息的获取模糊密度算法的不足,提出一种利用训练样本先验静态信息结合传感器识别信息集之间的支持可信度对模糊密度进行自适应赋值的方法。仿真实验结果表明,自适应动态模糊密度赋值方法通过传感器信息的支持度对传感器模糊密度进行实时的更新,减少了错误的信息对融合的影响,提高了多传感器系统的可靠性和鲁棒性。
With the continuous appearances of complex multisensor large-scale systems, multisensor data fusion technique has attracted comprehensive attention. Studied with quantities of manpower and material resources in many countries, multisensor data fusion technique has been a very active field of processing multiple source information effectively. In western developed countries, some military data fusion systems have been researched and exploited. The study of data fusion in our country is still at a relatively low level, especially in complex environment, some correlative techniques in data fusion lack veracity and adaptiveness. Three important problems in date fusion, multisensor tagert detection, target tracking and recognition, are discussed in this dissertation. For multiple sensors multiple targets detection and identification environment, dim small targets are always in complex background, and the multiple information association, fusion, and tracking is on the premise of low SNR, dim small targets detection. The better results of dim small targets detection for each sensor of system, the better ability of fusion and tracking. Thus before multiple sensors multiple targets tracking and fusion, the detection methods for low SNR, dim small target in the complex background is researched first in this paper.The main research and innovation contents are as following:
Firstly, target detection in infrared image is reseached. With the problem of the small target detection in low SNR and sky-sea background conditions, the paper proposed a method which based on the adaptive filter with Gauss function.This method is used in the sub-image which was obtained by wavelet analysis in the infrared image in order to restrain the noise. According to the trait of the wavelet analysis, we multiply the coefficient of the LL sub-image、HL sub-image and LH sub-image as the new LL sub-image, then use this coefficient by conversed wavelet transform. The results in the aspects of the mean, the standard deviation and the SNR of the image were better. Then, we found the threshold adaptively to segment the target in the image according to the SNR value. The simulation experiment results show the method is prone to realization in engineering and satisfies the meet of real time.
Secondly, for multi-sensor multi-target tracking, data association is one of important problems, and is the precondition of data fusion. Two kinds of data association conditions - homogeneous sensors and heterogeneous sensors are reseaerched in this thesis. Consindered of data association for homogeneous sensors, the multiple dimensions assignment algorithm is used. This algorithm is NP-hard for S≥3, so it is transformed to a combination optimization problem and the Particle Swarm Optimization algorithm is used to solve this. Moreover, the measurements are restranited, then the solution can be found faster and more effective, and then data association problem is solved; Consindered of data association for heterogeneous sensors, a data association method of using two kinds of information from radar and ESM is proposed. It is based on the Fuzzy C-means algorithm, and dymatice information and characteristic information are used at the same time to cluster, so that the data association is enhanced.
Thirdly, the problem of target track fusion was researched. A fuzzy theory based adaptive data fusion algorithm is used. The feature extraction was made for the filter data from two sensors and then the fuzzy illation was done under certain subordinate functions and rules, thereby a coefficient was got respinse to the practical conditions, and fusion results enhance the precision.
Finally, the core problem in using fuzzy integral for decision fusion is to determine the fuzzy densities. According to shortage of the algorithm through that the fuzzy densities are determined only by aprior static information, a method of determining fuzzy densities adaptively is presented, which uses the apriori static information of the training samples and supporting reliability of each sensor's information. The simulation experiment results show that due to real-time update of fuzzy densities through the method of determining fuzzy densities adaptively using supporting reliability of each sensor's information, the effect of inaccurate information on fusion is depressed, and the reliability and robustness of multi-sensor system are enhanced.
引文
[1]刘同明,夏祖勋,解洪成.数据融合技术及其应用.北京:国防工业出版社,1998:1-12页,191-198页
[2]何友,王国红,彭应宁.多传感器信息融合及应用.北京:电子工业出版社,2001:36-47页,12-23页
[3]D.L.Hall,R.J.Linn.A.Taxonomy of Algorithms for Multisensor Data Fusion.In Proc.1990 Tri-Serice Data Fusion Stmp.Apr.1991:13-29P
[4]N.Nabaa,R.H.Bishop.Solution to a Multisensor Tracking Problem with Sensor Registration Errors.IEEE Trans.On AES,1999,35(1):354-363P
[5]R.P.Simgh,W.H.Baily.Fuzzy Logic Application to Multisensor-Multitarget Correlation.IEEE Trans.On AES,1997,3086:129-138P
[6]梁彦,贾宇岗,潘泉,张洪才.具有参数自适应的交互式多模型算法.控制理论与应用,2001,18(5):653-656页
[7]苏惠敏,张明廉.多传感器监测系统的分布式数据融合.北京航空航天大学学报.1999,25(3):347-350页
[8]王青,毕靖.基于雷达/红外神经网络融合目标跟踪算法.系统仿真学报.2003,15(4):486-487,491页
[9]衣晓,何友,关欣.多模型算法在协同定位中的应用.武汉大学学报:信息科学版.2004,29(8):732-735页
[10]黄晓冬,何友.分布信息融合系统中的结构和对象.舰船电子工程.2001(2):26-30页
[11]黄涛,夏佩伦.潜艇多信息与结案目标融合识别模型.兵工自动化.2004,23(3):3-5页
[12]杨凡,常国岑.C~3I战术态势评估系统结构和实现方法.空军工程大学学报:自然科学版.2002,2(4):16-18页
[13]Hall D L.Mathemeticial techniques in multisensor data fusion,Artech Housem,1992
[14]Hall D L,James Llinas.An introduction to multisensor data fusion.Proceeding of IEEE.1997,85(1):6-23P
[15]张弘等.复杂背景下红外点目标的检测.红外与激光工程.2001,30(2):96-98页
[16]叶斌.基于顺序形态滤波目标检测.红外与激光工程,2001,30(1):25-29页
[17]雷选华等.海空背景下红外点目标检测算法.激光与红外,2001,31(1):32-34页
[18]史彩成等.基于模糊理论的红外图像滤波.激光与红外,2001,31(1):113-114页
[19]叶增军.强杂波背景中红外弱点目标检测新算法.激光与红外,2001,30(3):175-177页
[20]张贤达著.现代信号处理(第二版).北京:清华大学出版社,2002:263-344页
[21]朱红,赵亦工.基于背景自适应预测的红外弱小目标运动检测.红外与毫米波学报.1999,18(8):305-309页
[22]徐军.红外图像中弱小目标检测技术研究.西安电子科技大学博士学位论文.2001,10
[23]张弘,赵保军,毛二可等.复杂背景下红外点目标的检测.红外与激光工程.2001,30(4):96-98页
[24]张孝霖等.在海域红外复杂背景中小目标的检测的空间匹配滤波技术.红外与毫米波学报.1994,13(2):68-69页
[25]Henry Leung.Nonlinear Clutter Cancellation and Detection Using a Memory-Based Predictor.IEEE Trans on AES.1996,32(4)
[26]Wang D.Adaptive spatial filters for background clutter suppression and target detection.Opt.Eng.1982,21
[27]何焰兰,苏勇,高永眉.一种自适应小波去噪算法.电子学报2000,28(10):138-140页
[28]兰涛.外成像制导信息处理系统研究.科技大学博士学位论文 1996,6
[29]杨卫平,沈振康.起伏背景下的自适应门限检测方法.红外与毫米波学报.1999,18(4):120-124页
[30]崔屹.图像处理与分析-数学形态学方法及应用.第一版.北京:科学出版社,2000
[31]钱琼芬,徐毓.杂波环境中的单目标跟踪方法仿真研究.空军雷达学院学 报.2004,18(1):12-15页
[32]毛一凡,绕世钧.对修正的K近邻域关联算法的仿真与可信性评估.计算机仿真,2004,21(7):11-13页
[33]陈利斌,佟明安.密集杂波环境下的机动目标跟踪算法.电光与控制,2000,77(1):35-39页
[34]潘小瑾,徐毓,刘诗华.基于不同虚假量测分布的PDA算法.空军雷达学院学报,2004,18(1):22-24页
[35]徐毓,杨瑞娟,周焰.一种实现机动目标跟踪的STF动态模型PDA算法.电子学报,2003,31(7):981-984页
[36]刘城霞,王宝树.一种适用于工程应用的近似MSJPDA算法.上海航天,2002,12(4):8-13页
[37]巴宏欣,赵宗贵,杨飞,曹雷.多传感器多目标跟踪的JPDA算法.系统仿真学报,2004,16(7):1563-1566页
[38]尹成友,杨斌.JPDA中联合事件概率的计算.信号处理,2002,18(6):567-570页
[39]潘泉,张洪才,向阳朝,王培德.组合式快速JPDA算法.航空学报,1994,15(5):558-563页
[40]张世祥,秦卫华.一种目标跟踪的快速算法.探测与定位,2005,15(1):53-62页
[41]王家兵,程咏梅,梁彦.一种机动目标的PMHT跟踪算法.计算机量测与控制,2005,13(9):981-984页
[42]郭汝江,袁业术,权太范.高频地波雷达飞行小目标跟踪方法研究。电子学报,2005,33(9):1586-1589页
[43]王继阳,陆军,栗毅.一种基于目标属性特征的多假设关联算法.计算机仿真,2005,22(10):76-79页
[44]朱洪艳,韩崇昭,韩红,左东广.航迹起始算法研究.航空学报,2004,25(3):284-288页
[45]胡文龙,毛士艺.基于组合优化分配的多传感器数据关联.系统工程与电子技术,1997,5(1):1-10页
[46]胡文龙,毛士艺.基于组合优化的多传感器多目标数据关联.电子科学学刊,1996,18(6):561-566页
[47]雄伟,张晶炜,何友.基于S-D分配的多传感器联合概率数据互联算法. 清华大学学报(自然科学版),2005,45(4):452-455页
[48]何友,衣晓,关欣.基于串行的动态多维分配算法.西安电子科技大学学报,2005,32(3):489-493页
[49]Carine Hue,Jean-Pierre Le Cadre.Sequential monte carlo methods for multiple target tracking and data fusion.IEEE Transaction on Signal Processing,2002,50(2):309-325P
[50]Ba-ngu Vo,Sumeetpal Singh,Amaud Doucet.Sequential monte carlo methods for multi-target filtering with random finite sets.IEEE Transaction and Electronic Systems,2005,41(4):1224-1245P
[51]Jaco Vermaak,Simon J.Godsill,Patrick Perez.Monte carlo filtering for multi-target tracking data association,IEEE Transcation on Aerospace and Eletronic Systems,2005,41(4):309-332P
[52]辛云宏,杨万海,曹正文.红外单站多目标跟踪算法研究.光子学报,2005,34(10):1451-1455页
[53]戴耀,汪德虎.使用IMM/JPDA和固定延迟平滑滤波方法进行杂波环境下多机动目标跟踪.情报指挥控制系统与仿真技术,2003,12(4):30-39页
[54]梁彦,程咏梅.交互式多模型算法性能分析.控制理论与应用,2001,18(4):487-492页
[55]郝志成,朱明,刘微.复杂背景下目标的快速提取与跟踪.吉林大学学报:工学版,2006,36(2):259-263页
[56]朱志宇,姜长生,张冰.多传感器多机动目标跟踪方法研究进展.现代防御技术,2005,33(5):60-66页
[57]白东炜.一种适用工程应用的杂波环境中机动多目标的跟踪方法.雷达与对抗,2005,25(3):49-53页
[58]Khawsuk W.,Pao,L.Y.,Distributed multi-sensor multi-target tracking with feedback,Proc.Of American Control,2004,6(30):5356-5362P
[59]Brehard T.,Le Cadre,J.P.,Distributed target tracking for monlinear systems:Application to bearings-only tracking,7~(th) international Conference on Information Fusion,2005,1(25-28):77-84P
[60]Megherbi N.,Ambellouis S,Colot O,Cabestaning F,Multimodal data association based on the using of belief functions for multiple target tracking,7~(th) international Conference on Information Fusion,2005, 2(25-28):900-906P
[61]窦丽华,杨国胜.多目标数据关联的神经网络方法.火力与指挥控制,2002,27(4):28-30页
[62]Jianhua Wang,Weiting Liu,Min Wang,Multiple maneuvering target data association based on genetic algorithms,ICACT 2005,the 7~(th) International Conference on Advanced Communication Technology,2005,2(21-23):1011-1014P
[63]衣晓,关欣,何友,分布式多目标跟踪系统的灰色航迹关联模型,信号处理,2005,28(6):653-662页
[64]李辉,张安,赵敏,庞勤,沈莹,基于两点式数据关联算法的多机动目标跟踪,电子学报,2005,33(12):2125-2129页
[65]陈明辉,于玉鹏,钱晓俊,王雪松,基于仿真的多假目标干扰对相控阵雷达干扰效果研究,雷达与对抗,2005,24(3):1-5页
[66]Zhangsheng Duan,Chongzhao Han,Tangfei Tao,Optimal multi-sensor fusion target tracking with correlated measurement noises,2004 IEEE Internaltional Conference on Systems,Man and Cybenrntics,2004,2(10-13):1272-1278P
[67]郑家春,陈宗恒,基于模糊神经网络的AIS与雷达目标数据融合,集美大学学报(自然科学版),2005,10(3):216-220页
[68]胡士强,敬忠良,田宏伟,胡洪涛,异类传感器融合跟踪系统配准偏差的在线补偿,控制理论与应用,2005,22(5):748-752页
[69]Rizvi S A,Nasrabadi N M.Fusion techniques for automatic target recognition.IEEE Proceedings of the 32nd Applied Imagery Pattern Recognition Workshop,2003:201-207P
[70]Jimenez L O,Morell A M.Classification of hyperdimensional data based on feature and decision fusion approaches using Projection Pursuit,Majority Voting,and Neural Networks.IEEE Trans On Geoscience And Remote Sensing,1999,37(3):1360-1366P
[71]Y.S.Huang,C.Y.Suen,Combination of multiple experts for the recognition of unconstrained handwritten numerals.IEEE Transaction on Pattern Analysis and Machine Intelligence,1995,17(1):90-94P
[72]李宏,刘江涛,安玮.主观Bayes方法与神经网络相结合的多传感器数 据融合空间点目标识别方法.红外与毫米波学报.1997(16):448-454页
[73]Buede D.M,Girardi P.A target identification comparison of Bayesian and Demper-Shafer multisensor fusion,IEEE Trans.System,Man and Cybernetics,1997,27:569-577P
[74]Yang J B,Singh M G..An evidential reasoning approach for multiple-attributed decision making with uncertainty.IEEE Trans.Sys.Man.and Cybern.,1994,24(1):1-18P
[75]Denceur T.Analysis of evidence theoretic decision rules for pattern classification.Pattern Recongnition,1997,30(7):1095-1108P
[76]蓝金辉,蓝天,马宝华.改进的D-S数据融合方法在车辆目标识别中的应用研究.仪器仪表学报.2001.22(3):321-324页
[77]Yager R R.General approach to the fusion of imprecise information.International Journal of Intelligent Systems,1997,12(1):1-29P
[78]杜峰,施文康,邓勇.证据特征提取及其在证据理论改进中的应用.上海交通大学学报.2004.38(10):164-168页
[79]E Lefvre,O Colot,et al.A genenic framework for resolving the conflict in the combination belief structures.Proceedings of the Third International Conference on Information Fusion,2000(1):11-18P
[80]H.Tanhani,J.M.Keller.Information fusion incomputer vision using the fuzzy integral.IEEE Transaction on Systems Man and Cyberntics,1990,20(3):733-741P
[81]S.B.Cho,J.H,Kim.Muliple network fusion using fuzzy logic.IEEE Transaction on Neual Networks,1995,6(2):497-501P
[82]T.D.Pham,H.Yan.Fusion of handwritten numeral classifier based on fuzzy and genetic algorithms.Fuzzy Information Processing Society,NAFIPS'97,1997 Annual Meeting of the North American,1997:257-262P
[83]D.Wang,X.Wang and J.M.Keller.Determining Fuzzy Intergtal Densities Using a genetic algorithm for pattern recognition.Fuzzy Information Processing Society,NAFIPS'97,1997 Annual Meeting of the North American,1997:263-267P
[84]J.M.Keller,P.Gader,H.Tahani and J.H.Chiang.Advances in fuzzy intergration for pattern recognition.Fuzzy Sets and Systems,1994, 65:273-283P
[85]李玉榕 乔斌 蒋静坪.基于模糊积分和遗传算法的分类器组合算法.计算机工程与应用.2002.12,119-121
[86]梁继民 杨万海 蔡希尧.决策融合的模糊积分方法.西安电子科技大学校报.1998.25(2):250-253
[87]Xuefei Li,Huimin Feng,Dongmei Huang.Some aspects of classifier fusion based on fuzzy integrals.Proceedings of the Fourth International Conference on Machine Learning and Cybernetics,Guangzhou,18-21August 2005:4437-4441
[88]Benediktsson J A,Sveinsson J R.Optimied Consensus Theoretic.IEEE Trans.Geosci.Remote Sensing,1996:3490-3493P
[89]Benediktsson J A,Sveinsson J R.Hybrid Con-sensus Theoretic Classification.IEEE Trans.Geosci.Remote Sensing,1997,35(7):833-843P
[90]Benediktsson J A,Johannes R.Sveinsson.Parallel Consensual Neural Networks.IEEE Trans On Neural Networks,1997,8(1):54-64P
[91]J.Kittler,M.Hatef,et al.On combining classifiers.IEEE Transaction on Pattern Analysis and Machine Intelligence,1998,20(3):226-239P
[92]L.I.Kuncheva,L.C.Jain,Designing classifier fusion systems by genetic algorithms.IEEE Transaction on Evolutionary Computation,2000,4(4):327-336P
[93]X.Tang.Multiple competitive learning network fusion for object classification IEEE Transaction on Systems Man and Cyberntics-Part B:Cybernetics,1998,28(4):532-543
[94]刘伟,杨万海.基于背景抑制的红外弱小目标检测方法.红外技术,2003,25(3):1-4页
[95]阮秋琦,阮宇智等译.数字图像处理(第二版).北京.电子工业出版社.2003
[96]Askar H.Performance analysis of dim moving point target detection algo2rithms[A].International Conference on Communication,Circuits andSystems and West Sino Expositions.2002 IEEE.2002,1.605-260P
[97]杨福生.小波变换的工程分析与应用.北京:清华大学电机与应用电子技术,1996:3-66页
[98]S.Mallt.A theory of multiresolution signal decomposition:The wavelet transform.IEEE Trans on PAMI-11(7),1989:674-693P
[99]韩腊萍,陈立潮.改进Viterbi算法在机动目标跟踪中的应用.微电子学与计算机,2004,21(8):111-114页
[100]Jie Cai,Abhijit Sinha,T.Kirubarajan.EM-ML algorithm for track initialization using possibly noninformative data.IEEE Transactions on Aerospace and Electronic Systems,2005,41(3):1030-1048P
[101]陈蓓玉,陈小惠.基于改进Kohonen神经网络的多传感器关联融合算法.江苏船舶,2006,23(1):32-35页
[102]刘宗香,谢维信,黄敬雄.模糊观测数据的关联和目标跟踪.信号处理,2005,21(4):335-337页
[103]周开学,李书光.误差与数据处理理论[M].山东东营:石油大学出版社,2002
[104]王进,季薇,郑宝玉.粒子种群优化(PSO)算法的性能研究.南京邮电学院学报:自然科学版,2005,25(4):30-35页
[105]陈国初,俞金寿,郭伟.两群替代微粒群优化算法及其应用.华东理工大学学报:自然科学版,2005,31(6):787-791页
[106]张蕾,杨波.并行粒子群优化算法的设计与设计.通信学报,2005,26(B01):289-292页
[107]高尚,韩斌,吴小俊,杨静宇.求解旅行商问题的混合粒子群优化算法.控制与决策,2004,11(19):1286-1289页
[108]Y Shi,R C Eberhart.A Modified Swarm Optimizer.In:Proc.IEEE International Conference of Evolutionary Computation,Piscataway,NJ:IEEE Press,1998:303-308P
[109]韩红,韩崇昭,朱洪艳,刘允才.基于FCM的多传感器融合多目标跟踪的数据关联.系统仿真学报,2004,16(9):2096-2099页
[110]党宏社,张震强.一种道路条件下车辆跟踪的多目标数据关联方法.武汉理工大学学报:交通科学与工程版,2004,28(6):903-906页
[111]刘英.遗传算法与改进的FCM聚类算法的结合.甘肃科技,2006,22(3):122-124页
[112]J C Bezdek.Pattern Recognition with Fuzzy Objective Algorithms.Plenum Press,New York,1981
[113]Subhash Challa,Graham W.Pulford.Joint target tracking and classification using radar and ESM sensors.IEEE Transactions on Aerospace and Electronic Systrnes,2001,37(3):1039-1055P
[114]徐毓,金以慧,李锋.基于强跟踪滤波器估计的最优融合方法.系统工程与电子技术,2002,24(10):32-33页
[115]张永胜,嵇成新.一种基于当前统计模型的模糊交互多模型算法.火力与指挥控制,2003,28(1):51-55页
[116]L.A.Zadeh.Quantitative fuzzy semantics.Information Sciences.1971:159-176P
[117]范凯,陶然,周思永.模糊数据融合在目标跟踪中的应用.北京理工大学学报,2000,20(3):343-346页
[118]刘增良.模糊专家系统原理与设计.北京:北京航空航天大学出版社,1995
[119]王元斌,夏学知.多传感器综合目标识别技术研究.舰船电子工程.2004(24):8-16页
[120]孙红岩,毛士艺.多传感器目标识别的数据融合.电子学报.1995(23):188-193页
[121]Ren C.Luo,Michael G.Kay.Multisensor Integration and Fusion in Intelligent Systems.IEEE Trans.System,Man and Cybernetics,1989,19(5):901-928P
[122]Dasarathy B.V.Decision Fusion Stratgeies in Multisensor Environments.IEEE Transaction on Systems,Man,and Cybernetics,1991,21(3):1140-1154P
[123]李建勋.决策信息融合研究.西安电子科技大学学报.2000,27(1):83-87页
[124]Libby E W,Maybeck P.S.Sequence comparision technigues for multisensor data fusion and target recognition.IEEE Transaction on Systems,Man,and Cybernetics,1996,32(1):52-65P
[125]Tin Kam Ho,Hull J J,Srihari S.N.Decision combination in multiple classifier systems,ieee transaction on pattern analysis and machine intelligence,1994,16,(1):66-75P
[126]罗志增,蒋静萍.应用模糊信息融合实现目标物体的分类.仪器仪表学 报.1999(20):401-404页
[127]Germain M,Voorons M,Boucher J M.Fuzzy statistical classification method for multiband image fusion.Proc,of the Fifth International Conference on Information Fusion,2002,1:178-184P
[128]D W Ruck,S K Rogers.The multilayer perceptron as an approximation to a bayes optimal discrimination function.IEEE Trans Neural Networks,1990,1:296-298P
[129]George E.Target recognition and classification using neural networks.Proceedings IEEE Military Communications Conference,2002,2:1439-1442P
[130]孙即祥等.现代模式识别.长沙:国防科技大学出版社.2002:278-299页
[131]Ruck D W,Rogers S K.The multilayer perceptron as an approximation to a bayes optimal discrimination function.IEEE Trans on Neural Networks,1990,(1):296-298P
[132]M.Sugeno.Theroy of fuzzy integral and its application.Doct.Thesie,Tokyo Institute of Technology,1974
[133]H.Tanhani,J.M.Keller.Information fusion incomputer vision using the fuzzy integral.IEEE Transaction on Systems Man and Cyberntics,1990,20(3):733-741P
[134]刘普寅,吴孟达.模糊理论及其应用.长沙:国防科技大学出版社,1998
[2]何友,王国红,彭应宁.多传感器信息融合及应用.北京:电子工业出版社,2001:36-47页,12-23页
[3]D.L.Hall,R.J.Linn.A.Taxonomy of Algorithms for Multisensor Data Fusion.In Proc.1990 Tri-Serice Data Fusion Stmp.Apr.1991:13-29P
[4]N.Nabaa,R.H.Bishop.Solution to a Multisensor Tracking Problem with Sensor Registration Errors.IEEE Trans.On AES,1999,35(1):354-363P
[5]R.P.Simgh,W.H.Baily.Fuzzy Logic Application to Multisensor-Multitarget Correlation.IEEE Trans.On AES,1997,3086:129-138P
[6]梁彦,贾宇岗,潘泉,张洪才.具有参数自适应的交互式多模型算法.控制理论与应用,2001,18(5):653-656页
[7]苏惠敏,张明廉.多传感器监测系统的分布式数据融合.北京航空航天大学学报.1999,25(3):347-350页
[8]王青,毕靖.基于雷达/红外神经网络融合目标跟踪算法.系统仿真学报.2003,15(4):486-487,491页
[9]衣晓,何友,关欣.多模型算法在协同定位中的应用.武汉大学学报:信息科学版.2004,29(8):732-735页
[10]黄晓冬,何友.分布信息融合系统中的结构和对象.舰船电子工程.2001(2):26-30页
[11]黄涛,夏佩伦.潜艇多信息与结案目标融合识别模型.兵工自动化.2004,23(3):3-5页
[12]杨凡,常国岑.C~3I战术态势评估系统结构和实现方法.空军工程大学学报:自然科学版.2002,2(4):16-18页
[13]Hall D L.Mathemeticial techniques in multisensor data fusion,Artech Housem,1992
[14]Hall D L,James Llinas.An introduction to multisensor data fusion.Proceeding of IEEE.1997,85(1):6-23P
[15]张弘等.复杂背景下红外点目标的检测.红外与激光工程.2001,30(2):96-98页
[16]叶斌.基于顺序形态滤波目标检测.红外与激光工程,2001,30(1):25-29页
[17]雷选华等.海空背景下红外点目标检测算法.激光与红外,2001,31(1):32-34页
[18]史彩成等.基于模糊理论的红外图像滤波.激光与红外,2001,31(1):113-114页
[19]叶增军.强杂波背景中红外弱点目标检测新算法.激光与红外,2001,30(3):175-177页
[20]张贤达著.现代信号处理(第二版).北京:清华大学出版社,2002:263-344页
[21]朱红,赵亦工.基于背景自适应预测的红外弱小目标运动检测.红外与毫米波学报.1999,18(8):305-309页
[22]徐军.红外图像中弱小目标检测技术研究.西安电子科技大学博士学位论文.2001,10
[23]张弘,赵保军,毛二可等.复杂背景下红外点目标的检测.红外与激光工程.2001,30(4):96-98页
[24]张孝霖等.在海域红外复杂背景中小目标的检测的空间匹配滤波技术.红外与毫米波学报.1994,13(2):68-69页
[25]Henry Leung.Nonlinear Clutter Cancellation and Detection Using a Memory-Based Predictor.IEEE Trans on AES.1996,32(4)
[26]Wang D.Adaptive spatial filters for background clutter suppression and target detection.Opt.Eng.1982,21
[27]何焰兰,苏勇,高永眉.一种自适应小波去噪算法.电子学报2000,28(10):138-140页
[28]兰涛.外成像制导信息处理系统研究.科技大学博士学位论文 1996,6
[29]杨卫平,沈振康.起伏背景下的自适应门限检测方法.红外与毫米波学报.1999,18(4):120-124页
[30]崔屹.图像处理与分析-数学形态学方法及应用.第一版.北京:科学出版社,2000
[31]钱琼芬,徐毓.杂波环境中的单目标跟踪方法仿真研究.空军雷达学院学 报.2004,18(1):12-15页
[32]毛一凡,绕世钧.对修正的K近邻域关联算法的仿真与可信性评估.计算机仿真,2004,21(7):11-13页
[33]陈利斌,佟明安.密集杂波环境下的机动目标跟踪算法.电光与控制,2000,77(1):35-39页
[34]潘小瑾,徐毓,刘诗华.基于不同虚假量测分布的PDA算法.空军雷达学院学报,2004,18(1):22-24页
[35]徐毓,杨瑞娟,周焰.一种实现机动目标跟踪的STF动态模型PDA算法.电子学报,2003,31(7):981-984页
[36]刘城霞,王宝树.一种适用于工程应用的近似MSJPDA算法.上海航天,2002,12(4):8-13页
[37]巴宏欣,赵宗贵,杨飞,曹雷.多传感器多目标跟踪的JPDA算法.系统仿真学报,2004,16(7):1563-1566页
[38]尹成友,杨斌.JPDA中联合事件概率的计算.信号处理,2002,18(6):567-570页
[39]潘泉,张洪才,向阳朝,王培德.组合式快速JPDA算法.航空学报,1994,15(5):558-563页
[40]张世祥,秦卫华.一种目标跟踪的快速算法.探测与定位,2005,15(1):53-62页
[41]王家兵,程咏梅,梁彦.一种机动目标的PMHT跟踪算法.计算机量测与控制,2005,13(9):981-984页
[42]郭汝江,袁业术,权太范.高频地波雷达飞行小目标跟踪方法研究。电子学报,2005,33(9):1586-1589页
[43]王继阳,陆军,栗毅.一种基于目标属性特征的多假设关联算法.计算机仿真,2005,22(10):76-79页
[44]朱洪艳,韩崇昭,韩红,左东广.航迹起始算法研究.航空学报,2004,25(3):284-288页
[45]胡文龙,毛士艺.基于组合优化分配的多传感器数据关联.系统工程与电子技术,1997,5(1):1-10页
[46]胡文龙,毛士艺.基于组合优化的多传感器多目标数据关联.电子科学学刊,1996,18(6):561-566页
[47]雄伟,张晶炜,何友.基于S-D分配的多传感器联合概率数据互联算法. 清华大学学报(自然科学版),2005,45(4):452-455页
[48]何友,衣晓,关欣.基于串行的动态多维分配算法.西安电子科技大学学报,2005,32(3):489-493页
[49]Carine Hue,Jean-Pierre Le Cadre.Sequential monte carlo methods for multiple target tracking and data fusion.IEEE Transaction on Signal Processing,2002,50(2):309-325P
[50]Ba-ngu Vo,Sumeetpal Singh,Amaud Doucet.Sequential monte carlo methods for multi-target filtering with random finite sets.IEEE Transaction and Electronic Systems,2005,41(4):1224-1245P
[51]Jaco Vermaak,Simon J.Godsill,Patrick Perez.Monte carlo filtering for multi-target tracking data association,IEEE Transcation on Aerospace and Eletronic Systems,2005,41(4):309-332P
[52]辛云宏,杨万海,曹正文.红外单站多目标跟踪算法研究.光子学报,2005,34(10):1451-1455页
[53]戴耀,汪德虎.使用IMM/JPDA和固定延迟平滑滤波方法进行杂波环境下多机动目标跟踪.情报指挥控制系统与仿真技术,2003,12(4):30-39页
[54]梁彦,程咏梅.交互式多模型算法性能分析.控制理论与应用,2001,18(4):487-492页
[55]郝志成,朱明,刘微.复杂背景下目标的快速提取与跟踪.吉林大学学报:工学版,2006,36(2):259-263页
[56]朱志宇,姜长生,张冰.多传感器多机动目标跟踪方法研究进展.现代防御技术,2005,33(5):60-66页
[57]白东炜.一种适用工程应用的杂波环境中机动多目标的跟踪方法.雷达与对抗,2005,25(3):49-53页
[58]Khawsuk W.,Pao,L.Y.,Distributed multi-sensor multi-target tracking with feedback,Proc.Of American Control,2004,6(30):5356-5362P
[59]Brehard T.,Le Cadre,J.P.,Distributed target tracking for monlinear systems:Application to bearings-only tracking,7~(th) international Conference on Information Fusion,2005,1(25-28):77-84P
[60]Megherbi N.,Ambellouis S,Colot O,Cabestaning F,Multimodal data association based on the using of belief functions for multiple target tracking,7~(th) international Conference on Information Fusion,2005, 2(25-28):900-906P
[61]窦丽华,杨国胜.多目标数据关联的神经网络方法.火力与指挥控制,2002,27(4):28-30页
[62]Jianhua Wang,Weiting Liu,Min Wang,Multiple maneuvering target data association based on genetic algorithms,ICACT 2005,the 7~(th) International Conference on Advanced Communication Technology,2005,2(21-23):1011-1014P
[63]衣晓,关欣,何友,分布式多目标跟踪系统的灰色航迹关联模型,信号处理,2005,28(6):653-662页
[64]李辉,张安,赵敏,庞勤,沈莹,基于两点式数据关联算法的多机动目标跟踪,电子学报,2005,33(12):2125-2129页
[65]陈明辉,于玉鹏,钱晓俊,王雪松,基于仿真的多假目标干扰对相控阵雷达干扰效果研究,雷达与对抗,2005,24(3):1-5页
[66]Zhangsheng Duan,Chongzhao Han,Tangfei Tao,Optimal multi-sensor fusion target tracking with correlated measurement noises,2004 IEEE Internaltional Conference on Systems,Man and Cybenrntics,2004,2(10-13):1272-1278P
[67]郑家春,陈宗恒,基于模糊神经网络的AIS与雷达目标数据融合,集美大学学报(自然科学版),2005,10(3):216-220页
[68]胡士强,敬忠良,田宏伟,胡洪涛,异类传感器融合跟踪系统配准偏差的在线补偿,控制理论与应用,2005,22(5):748-752页
[69]Rizvi S A,Nasrabadi N M.Fusion techniques for automatic target recognition.IEEE Proceedings of the 32nd Applied Imagery Pattern Recognition Workshop,2003:201-207P
[70]Jimenez L O,Morell A M.Classification of hyperdimensional data based on feature and decision fusion approaches using Projection Pursuit,Majority Voting,and Neural Networks.IEEE Trans On Geoscience And Remote Sensing,1999,37(3):1360-1366P
[71]Y.S.Huang,C.Y.Suen,Combination of multiple experts for the recognition of unconstrained handwritten numerals.IEEE Transaction on Pattern Analysis and Machine Intelligence,1995,17(1):90-94P
[72]李宏,刘江涛,安玮.主观Bayes方法与神经网络相结合的多传感器数 据融合空间点目标识别方法.红外与毫米波学报.1997(16):448-454页
[73]Buede D.M,Girardi P.A target identification comparison of Bayesian and Demper-Shafer multisensor fusion,IEEE Trans.System,Man and Cybernetics,1997,27:569-577P
[74]Yang J B,Singh M G..An evidential reasoning approach for multiple-attributed decision making with uncertainty.IEEE Trans.Sys.Man.and Cybern.,1994,24(1):1-18P
[75]Denceur T.Analysis of evidence theoretic decision rules for pattern classification.Pattern Recongnition,1997,30(7):1095-1108P
[76]蓝金辉,蓝天,马宝华.改进的D-S数据融合方法在车辆目标识别中的应用研究.仪器仪表学报.2001.22(3):321-324页
[77]Yager R R.General approach to the fusion of imprecise information.International Journal of Intelligent Systems,1997,12(1):1-29P
[78]杜峰,施文康,邓勇.证据特征提取及其在证据理论改进中的应用.上海交通大学学报.2004.38(10):164-168页
[79]E Lefvre,O Colot,et al.A genenic framework for resolving the conflict in the combination belief structures.Proceedings of the Third International Conference on Information Fusion,2000(1):11-18P
[80]H.Tanhani,J.M.Keller.Information fusion incomputer vision using the fuzzy integral.IEEE Transaction on Systems Man and Cyberntics,1990,20(3):733-741P
[81]S.B.Cho,J.H,Kim.Muliple network fusion using fuzzy logic.IEEE Transaction on Neual Networks,1995,6(2):497-501P
[82]T.D.Pham,H.Yan.Fusion of handwritten numeral classifier based on fuzzy and genetic algorithms.Fuzzy Information Processing Society,NAFIPS'97,1997 Annual Meeting of the North American,1997:257-262P
[83]D.Wang,X.Wang and J.M.Keller.Determining Fuzzy Intergtal Densities Using a genetic algorithm for pattern recognition.Fuzzy Information Processing Society,NAFIPS'97,1997 Annual Meeting of the North American,1997:263-267P
[84]J.M.Keller,P.Gader,H.Tahani and J.H.Chiang.Advances in fuzzy intergration for pattern recognition.Fuzzy Sets and Systems,1994, 65:273-283P
[85]李玉榕 乔斌 蒋静坪.基于模糊积分和遗传算法的分类器组合算法.计算机工程与应用.2002.12,119-121
[86]梁继民 杨万海 蔡希尧.决策融合的模糊积分方法.西安电子科技大学校报.1998.25(2):250-253
[87]Xuefei Li,Huimin Feng,Dongmei Huang.Some aspects of classifier fusion based on fuzzy integrals.Proceedings of the Fourth International Conference on Machine Learning and Cybernetics,Guangzhou,18-21August 2005:4437-4441
[88]Benediktsson J A,Sveinsson J R.Optimied Consensus Theoretic.IEEE Trans.Geosci.Remote Sensing,1996:3490-3493P
[89]Benediktsson J A,Sveinsson J R.Hybrid Con-sensus Theoretic Classification.IEEE Trans.Geosci.Remote Sensing,1997,35(7):833-843P
[90]Benediktsson J A,Johannes R.Sveinsson.Parallel Consensual Neural Networks.IEEE Trans On Neural Networks,1997,8(1):54-64P
[91]J.Kittler,M.Hatef,et al.On combining classifiers.IEEE Transaction on Pattern Analysis and Machine Intelligence,1998,20(3):226-239P
[92]L.I.Kuncheva,L.C.Jain,Designing classifier fusion systems by genetic algorithms.IEEE Transaction on Evolutionary Computation,2000,4(4):327-336P
[93]X.Tang.Multiple competitive learning network fusion for object classification IEEE Transaction on Systems Man and Cyberntics-Part B:Cybernetics,1998,28(4):532-543
[94]刘伟,杨万海.基于背景抑制的红外弱小目标检测方法.红外技术,2003,25(3):1-4页
[95]阮秋琦,阮宇智等译.数字图像处理(第二版).北京.电子工业出版社.2003
[96]Askar H.Performance analysis of dim moving point target detection algo2rithms[A].International Conference on Communication,Circuits andSystems and West Sino Expositions.2002 IEEE.2002,1.605-260P
[97]杨福生.小波变换的工程分析与应用.北京:清华大学电机与应用电子技术,1996:3-66页
[98]S.Mallt.A theory of multiresolution signal decomposition:The wavelet transform.IEEE Trans on PAMI-11(7),1989:674-693P
[99]韩腊萍,陈立潮.改进Viterbi算法在机动目标跟踪中的应用.微电子学与计算机,2004,21(8):111-114页
[100]Jie Cai,Abhijit Sinha,T.Kirubarajan.EM-ML algorithm for track initialization using possibly noninformative data.IEEE Transactions on Aerospace and Electronic Systems,2005,41(3):1030-1048P
[101]陈蓓玉,陈小惠.基于改进Kohonen神经网络的多传感器关联融合算法.江苏船舶,2006,23(1):32-35页
[102]刘宗香,谢维信,黄敬雄.模糊观测数据的关联和目标跟踪.信号处理,2005,21(4):335-337页
[103]周开学,李书光.误差与数据处理理论[M].山东东营:石油大学出版社,2002
[104]王进,季薇,郑宝玉.粒子种群优化(PSO)算法的性能研究.南京邮电学院学报:自然科学版,2005,25(4):30-35页
[105]陈国初,俞金寿,郭伟.两群替代微粒群优化算法及其应用.华东理工大学学报:自然科学版,2005,31(6):787-791页
[106]张蕾,杨波.并行粒子群优化算法的设计与设计.通信学报,2005,26(B01):289-292页
[107]高尚,韩斌,吴小俊,杨静宇.求解旅行商问题的混合粒子群优化算法.控制与决策,2004,11(19):1286-1289页
[108]Y Shi,R C Eberhart.A Modified Swarm Optimizer.In:Proc.IEEE International Conference of Evolutionary Computation,Piscataway,NJ:IEEE Press,1998:303-308P
[109]韩红,韩崇昭,朱洪艳,刘允才.基于FCM的多传感器融合多目标跟踪的数据关联.系统仿真学报,2004,16(9):2096-2099页
[110]党宏社,张震强.一种道路条件下车辆跟踪的多目标数据关联方法.武汉理工大学学报:交通科学与工程版,2004,28(6):903-906页
[111]刘英.遗传算法与改进的FCM聚类算法的结合.甘肃科技,2006,22(3):122-124页
[112]J C Bezdek.Pattern Recognition with Fuzzy Objective Algorithms.Plenum Press,New York,1981
[113]Subhash Challa,Graham W.Pulford.Joint target tracking and classification using radar and ESM sensors.IEEE Transactions on Aerospace and Electronic Systrnes,2001,37(3):1039-1055P
[114]徐毓,金以慧,李锋.基于强跟踪滤波器估计的最优融合方法.系统工程与电子技术,2002,24(10):32-33页
[115]张永胜,嵇成新.一种基于当前统计模型的模糊交互多模型算法.火力与指挥控制,2003,28(1):51-55页
[116]L.A.Zadeh.Quantitative fuzzy semantics.Information Sciences.1971:159-176P
[117]范凯,陶然,周思永.模糊数据融合在目标跟踪中的应用.北京理工大学学报,2000,20(3):343-346页
[118]刘增良.模糊专家系统原理与设计.北京:北京航空航天大学出版社,1995
[119]王元斌,夏学知.多传感器综合目标识别技术研究.舰船电子工程.2004(24):8-16页
[120]孙红岩,毛士艺.多传感器目标识别的数据融合.电子学报.1995(23):188-193页
[121]Ren C.Luo,Michael G.Kay.Multisensor Integration and Fusion in Intelligent Systems.IEEE Trans.System,Man and Cybernetics,1989,19(5):901-928P
[122]Dasarathy B.V.Decision Fusion Stratgeies in Multisensor Environments.IEEE Transaction on Systems,Man,and Cybernetics,1991,21(3):1140-1154P
[123]李建勋.决策信息融合研究.西安电子科技大学学报.2000,27(1):83-87页
[124]Libby E W,Maybeck P.S.Sequence comparision technigues for multisensor data fusion and target recognition.IEEE Transaction on Systems,Man,and Cybernetics,1996,32(1):52-65P
[125]Tin Kam Ho,Hull J J,Srihari S.N.Decision combination in multiple classifier systems,ieee transaction on pattern analysis and machine intelligence,1994,16,(1):66-75P
[126]罗志增,蒋静萍.应用模糊信息融合实现目标物体的分类.仪器仪表学 报.1999(20):401-404页
[127]Germain M,Voorons M,Boucher J M.Fuzzy statistical classification method for multiband image fusion.Proc,of the Fifth International Conference on Information Fusion,2002,1:178-184P
[128]D W Ruck,S K Rogers.The multilayer perceptron as an approximation to a bayes optimal discrimination function.IEEE Trans Neural Networks,1990,1:296-298P
[129]George E.Target recognition and classification using neural networks.Proceedings IEEE Military Communications Conference,2002,2:1439-1442P
[130]孙即祥等.现代模式识别.长沙:国防科技大学出版社.2002:278-299页
[131]Ruck D W,Rogers S K.The multilayer perceptron as an approximation to a bayes optimal discrimination function.IEEE Trans on Neural Networks,1990,(1):296-298P
[132]M.Sugeno.Theroy of fuzzy integral and its application.Doct.Thesie,Tokyo Institute of Technology,1974
[133]H.Tanhani,J.M.Keller.Information fusion incomputer vision using the fuzzy integral.IEEE Transaction on Systems Man and Cyberntics,1990,20(3):733-741P
[134]刘普寅,吴孟达.模糊理论及其应用.长沙:国防科技大学出版社,1998