机场场面移动目标监视系统关键技术研究
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摘要
民航机场是民用航空运输系统的主要组成部分，是空中交通的重要基础设施。它是保证航空器安全起降的首要条件。随着民航运输业的快速发展，机场交通流量持续增长，在保障安全的条件下，如何提高机场场面的运行效率是民航业面临的重要问题。以A-SMGCS为代表的新一代高级场面运动引导与控制技术成为该领域研究的热点。本文围绕A-SMGCS技术，对机场场场面监视技术及组合监视原型系统展开理论算法和工程应用的研究。
主要工作及取得的研究成果如下：
1、根据SMR、ADS、D-GPS和MDS的目标定位原理、精度、更新率及覆盖范围等特点，确定了采用SMR、D-GPS和ADS-C三种监视方式，以及它们之间的某种组合，作为本文场面监视的关键技术，为后文研究奠定了基础。
2、实现SMR场面监视的目标定位与跟踪研究。采用多模型算法（IMM）实现单SMR对场面移动目标的定位与跟踪，分别研究了固定模型集IMM算法（FS-IMM）和变结构IMM算法（VS-IMM），经过Monte Carlo仿真，验证了VS-IMM算法比FS-IMM算法的跟踪精度高；研究了分布式多SMR场面监视的目标航迹融合，对其中的时空对准、误差配准等进行了研究，在此基础上，提出采用层次聚类的方式实现航迹关联，和采用加权融合的方法实现了航迹状态估计融合。
3、提出基于ADS-C的场面飞机辅助监视系统方案。根据我国ACARS地空数据链的建设与成功应用，提出将其应用于机场场面上飞机辅助监视，设计了基于ACARS的ADS-C场面飞机辅助监视系统；提出了面向ARINC618协议的ACARS报文的快速译码；对基于ADS-C场面飞机辅助监视系统进行了航迹管理，提出采用基于“当前统计模型”对其航迹进行滤波。
4、提出基于D-GPS的场面移动车辆监视方案。在分析对比三种不同D-GPS特性的基础上，提出采用单基准站局域D-GPS实现场面移动车辆监视方案；首先，构建单基站局域D-GPS，标定D-GPS基准站的安装位置，并通过GPRS实现基准站与流动站之间的通信。然后，构建基于D-GPS的场面移动车辆监视系统。
5、研究异类监视设备组合监视的目标航迹融合。介绍从WGS-84大地坐标系到直角坐标系的变换，和以高精度的D-GPS为基准异类多传感器的误差配准；提出采用FCM进行SMR航迹与ADS-C航迹的关联，并将AVQ神经网络引入到FCM中，提高了运算速度；将基于航迹隶属度的动态加权算法，运用到SMR和ADS-C已关联航迹的融合中，仿真验证了算法的有效性。
6、基于D-GPS/ADS-C场面移动目标组合监视原型系统的软硬件设计与实现。以A-SMGCS的监视需求为前提，提出D-GPS/ADS-C组合监视原型系统的总体构架图；构建了基于C8051F单片机和CMX469A（MSK集成芯片）的飞机目标数据接收设备；采用ARM为主芯片，辅以外围芯片及I/O接口，连接D-GPS模块和WLAN模块，构建了车载终端的硬件；配置了监视中心的联网计算机，以及它和车载终端通信的接口硬件。提出用于车载终端的导航地图数据结构，利用JSCORS完成了电子地图的测绘；实现了线程化的UDP服务器模块，利用多线程和ICP技术实现了车载系统的软件设计，完成了GPS数据解析、WLAN数据通信与解析等主要功能；利用Python和PyQt的跨平台性，实现了组合监视原型系统跨平台的监视功能。
7、基于D-GPS/ADS-C场面移动目标组合监视原型系统功能验证实验。把实验室现有的PATS平台配置为“PATS-EG环境”，对本文设计的飞机目标数据接收设备和飞机辅助监视系统，进行接收ACARS报文验证实验，验证了它接收机载ACARS报文的实时性与正确率等需求；在本校江宁校区测绘得到的电子地图基础上，进行跑车实验，验证了本文设计的单基准站局域D-GPS、车载终端和监视中心的基本功能，以及它们之间的通信功能。
通过“基于D-GPS/ADS-C组合监视原型系统”功能验证实验，验证了：本文的机场场面移动目标监视关键技术研究的结果，是有效、有价值的；本文构建的“基于D-GPS/ADS-C组合监视原型系统”，对新构建场面移动目标监视系统可提供有用的基础支持。
As the main component of the civil aviation transport system, airport is an importantinfrastructure of the air traffic. It is the first condition to ensure airplane safety take-off andlanding. With the development of civil aviation industry, the operations on the airport surface havebeen increasingly busy and crowded. Therefore, how to improve the operation efficiency ofairport surface is an important problem of civil aviation. In this thesis, the Airport SurfaceMovement Guidance and Control System is the study popular issuses. Focused on A-SMGCStechnology, deep researches on theory and engineering application algorithm of airport surfacesurveillance technology and combination surveillance prototype system are carried out.
The main content and innovation in this thesis may be summarized as follows:
1、The paper introduces the positioning principle, the precision, the update rate and coverageof SMR, ADS, D-GPS and MDS. According them, the paper identifies the SMR, D-GPS andADS-C as the key surveillance technology of the moving target tracking on the airport surface, aswell as some combination between them. It provides the research basis for later.
2、Research on SMR locating and tracking moving target on airport surface. The IMMalgorithm is adopted to achive single SMR locating and tracking moving target on airport surface.To overcome the defect of EKF, the UKF is introduced to the IMM. Then UKF-IMM algorithm isused to tracking the target. Combined with the airport terrain information, VS-IMM algorithm isadopted to track the target on airport surface.The simulation results show VS-IMM trackingalgorithm is more accurate than FS-IMM by Monte Carlo simulation. The frame of the multi-SMRmulti-target data fusion of the distributed structure is proposed. Research is carried on the problemof time and space alignment. And multi-SMR error registration is studied. Then, the hierarchicalclustering is used to achive track-to-track association. Finally, weighted fusion method is used torealize track fusion.
3、Based on ADS-C aircraft auxiliary monitoring system on airport surface is proposed.According to ACARS air-ground data chain construction and successful application in our country,the application of using ACARS as an auxiliary surveillance means is proposed. A report decodingmethod based on ARINC-618protocol is put forward.. The track’s management of the ADS-Cauxiliary surveillance system is studied. The track is filterd by using current statical model as theaircraft’s maneuvering model.
4、D-GPS is applied to locate and track the vehicles on airport surface. Based on thecomparision of three different D-GPS characteristics, to meet the positioning accuracy andmonitoring range, a single base station local D-GPS is selected to realize the vehiclesurveillance.According the JSCORS to set the location of D-GPS base station. The information between the base station and mobile station is transmitted via GPRS. Based on these, the systemblock diagram of moving vehicle surveillance system based on D-GPS is designed.
5、 The target’s tracking fusion of heterogeneous sensor consisting of combinationsurveillance system is studied. Transformaiton from the WGS-84the earth coordinate system tothe rectangular coordinate system is studied. With high precision D-GPS as a benchmark, theSMR and ADS-C error registration is realized. Reasearch on SMR track to ADS-C trackassociation by FCM is studied. In order to improve the operation speed, the AVQ neural networkis introduced to realize the FCM. Combing with the idea of weighted fusion algorithm,and trackfuzzy membership matrix, real time and synamic weighting factors of local tracks in track fusionare allotted. Montel Carlo simulaition shows the effective of the algorithm.
6、Research on hardware and softwareware design of A-SMGCS prototype system. Based onthe premise of A-SMGCS surveillance requirements, the main frame of combination prototypesystem based on D-GPS/ADS-C is proposed. The paper presents a design and realization ofaircraft data receiving equipment by using C8051F based on CMX469A. Using ARM as the mainchip, combined with external chip and I/O interface for connection to D-GPS and WLAN modulesto realize the hardware platform. The hardware of communication interface between vehicle andmonitoring center is designed. A specific digital map format is proposed which is dedicated foron-board navigation. The electronic map surveying and mapping is realized using JSCORS, Theefficient UDP server module is realized. UDP multicast and multi-thread technology is used torealize Using cross-platform-ability of Python language and PyQt library, the cross-platformmonitoring center is realized, and the main functions, such as network transceiver and dynamicinformation display are completed.
7、The verification experiment of the of D-GPS/ADS-C combination prototype system iscarried out. Using the PATS-EG environment of PATS platform to verify the aircraft data revevingeuipment and aircraft auxiliary monitoring system’s ability to receive the ACARS message. Theresults show that it can receive the ACARS message and it meets the requirement of surface targetsurveillance on real-time and accuracy rate. Based on college digital map, the real test about thecombination prototype system is finished. The performance of the D-GPS, on-board terminaland the monitoring is verified.
Through the verification experiment of the combination prototype system, we draw thefollowing conclusion. First, the results of research on the key techniques of moving targetsurveillance on airport surface are effective and valuable. Second, the combination prototypesystem can provide useful basis to build new moving target surveillance system on airport surface.
主要工作及取得的研究成果如下：
1、根据SMR、ADS、D-GPS和MDS的目标定位原理、精度、更新率及覆盖范围等特点，确定了采用SMR、D-GPS和ADS-C三种监视方式，以及它们之间的某种组合，作为本文场面监视的关键技术，为后文研究奠定了基础。
2、实现SMR场面监视的目标定位与跟踪研究。采用多模型算法（IMM）实现单SMR对场面移动目标的定位与跟踪，分别研究了固定模型集IMM算法（FS-IMM）和变结构IMM算法（VS-IMM），经过Monte Carlo仿真，验证了VS-IMM算法比FS-IMM算法的跟踪精度高；研究了分布式多SMR场面监视的目标航迹融合，对其中的时空对准、误差配准等进行了研究，在此基础上，提出采用层次聚类的方式实现航迹关联，和采用加权融合的方法实现了航迹状态估计融合。
3、提出基于ADS-C的场面飞机辅助监视系统方案。根据我国ACARS地空数据链的建设与成功应用，提出将其应用于机场场面上飞机辅助监视，设计了基于ACARS的ADS-C场面飞机辅助监视系统；提出了面向ARINC618协议的ACARS报文的快速译码；对基于ADS-C场面飞机辅助监视系统进行了航迹管理，提出采用基于“当前统计模型”对其航迹进行滤波。
4、提出基于D-GPS的场面移动车辆监视方案。在分析对比三种不同D-GPS特性的基础上，提出采用单基准站局域D-GPS实现场面移动车辆监视方案；首先，构建单基站局域D-GPS，标定D-GPS基准站的安装位置，并通过GPRS实现基准站与流动站之间的通信。然后，构建基于D-GPS的场面移动车辆监视系统。
5、研究异类监视设备组合监视的目标航迹融合。介绍从WGS-84大地坐标系到直角坐标系的变换，和以高精度的D-GPS为基准异类多传感器的误差配准；提出采用FCM进行SMR航迹与ADS-C航迹的关联，并将AVQ神经网络引入到FCM中，提高了运算速度；将基于航迹隶属度的动态加权算法，运用到SMR和ADS-C已关联航迹的融合中，仿真验证了算法的有效性。
6、基于D-GPS/ADS-C场面移动目标组合监视原型系统的软硬件设计与实现。以A-SMGCS的监视需求为前提，提出D-GPS/ADS-C组合监视原型系统的总体构架图；构建了基于C8051F单片机和CMX469A（MSK集成芯片）的飞机目标数据接收设备；采用ARM为主芯片，辅以外围芯片及I/O接口，连接D-GPS模块和WLAN模块，构建了车载终端的硬件；配置了监视中心的联网计算机，以及它和车载终端通信的接口硬件。提出用于车载终端的导航地图数据结构，利用JSCORS完成了电子地图的测绘；实现了线程化的UDP服务器模块，利用多线程和ICP技术实现了车载系统的软件设计，完成了GPS数据解析、WLAN数据通信与解析等主要功能；利用Python和PyQt的跨平台性，实现了组合监视原型系统跨平台的监视功能。
7、基于D-GPS/ADS-C场面移动目标组合监视原型系统功能验证实验。把实验室现有的PATS平台配置为“PATS-EG环境”，对本文设计的飞机目标数据接收设备和飞机辅助监视系统，进行接收ACARS报文验证实验，验证了它接收机载ACARS报文的实时性与正确率等需求；在本校江宁校区测绘得到的电子地图基础上，进行跑车实验，验证了本文设计的单基准站局域D-GPS、车载终端和监视中心的基本功能，以及它们之间的通信功能。
通过“基于D-GPS/ADS-C组合监视原型系统”功能验证实验，验证了：本文的机场场面移动目标监视关键技术研究的结果，是有效、有价值的；本文构建的“基于D-GPS/ADS-C组合监视原型系统”，对新构建场面移动目标监视系统可提供有用的基础支持。
As the main component of the civil aviation transport system, airport is an importantinfrastructure of the air traffic. It is the first condition to ensure airplane safety take-off andlanding. With the development of civil aviation industry, the operations on the airport surface havebeen increasingly busy and crowded. Therefore, how to improve the operation efficiency ofairport surface is an important problem of civil aviation. In this thesis, the Airport SurfaceMovement Guidance and Control System is the study popular issuses. Focused on A-SMGCStechnology, deep researches on theory and engineering application algorithm of airport surfacesurveillance technology and combination surveillance prototype system are carried out.
The main content and innovation in this thesis may be summarized as follows:
1、The paper introduces the positioning principle, the precision, the update rate and coverageof SMR, ADS, D-GPS and MDS. According them, the paper identifies the SMR, D-GPS andADS-C as the key surveillance technology of the moving target tracking on the airport surface, aswell as some combination between them. It provides the research basis for later.
2、Research on SMR locating and tracking moving target on airport surface. The IMMalgorithm is adopted to achive single SMR locating and tracking moving target on airport surface.To overcome the defect of EKF, the UKF is introduced to the IMM. Then UKF-IMM algorithm isused to tracking the target. Combined with the airport terrain information, VS-IMM algorithm isadopted to track the target on airport surface.The simulation results show VS-IMM trackingalgorithm is more accurate than FS-IMM by Monte Carlo simulation. The frame of the multi-SMRmulti-target data fusion of the distributed structure is proposed. Research is carried on the problemof time and space alignment. And multi-SMR error registration is studied. Then, the hierarchicalclustering is used to achive track-to-track association. Finally, weighted fusion method is used torealize track fusion.
3、Based on ADS-C aircraft auxiliary monitoring system on airport surface is proposed.According to ACARS air-ground data chain construction and successful application in our country,the application of using ACARS as an auxiliary surveillance means is proposed. A report decodingmethod based on ARINC-618protocol is put forward.. The track’s management of the ADS-Cauxiliary surveillance system is studied. The track is filterd by using current statical model as theaircraft’s maneuvering model.
4、D-GPS is applied to locate and track the vehicles on airport surface. Based on thecomparision of three different D-GPS characteristics, to meet the positioning accuracy andmonitoring range, a single base station local D-GPS is selected to realize the vehiclesurveillance.According the JSCORS to set the location of D-GPS base station. The information between the base station and mobile station is transmitted via GPRS. Based on these, the systemblock diagram of moving vehicle surveillance system based on D-GPS is designed.
5、 The target’s tracking fusion of heterogeneous sensor consisting of combinationsurveillance system is studied. Transformaiton from the WGS-84the earth coordinate system tothe rectangular coordinate system is studied. With high precision D-GPS as a benchmark, theSMR and ADS-C error registration is realized. Reasearch on SMR track to ADS-C trackassociation by FCM is studied. In order to improve the operation speed, the AVQ neural networkis introduced to realize the FCM. Combing with the idea of weighted fusion algorithm,and trackfuzzy membership matrix, real time and synamic weighting factors of local tracks in track fusionare allotted. Montel Carlo simulaition shows the effective of the algorithm.
6、Research on hardware and softwareware design of A-SMGCS prototype system. Based onthe premise of A-SMGCS surveillance requirements, the main frame of combination prototypesystem based on D-GPS/ADS-C is proposed. The paper presents a design and realization ofaircraft data receiving equipment by using C8051F based on CMX469A. Using ARM as the mainchip, combined with external chip and I/O interface for connection to D-GPS and WLAN modulesto realize the hardware platform. The hardware of communication interface between vehicle andmonitoring center is designed. A specific digital map format is proposed which is dedicated foron-board navigation. The electronic map surveying and mapping is realized using JSCORS, Theefficient UDP server module is realized. UDP multicast and multi-thread technology is used torealize Using cross-platform-ability of Python language and PyQt library, the cross-platformmonitoring center is realized, and the main functions, such as network transceiver and dynamicinformation display are completed.
7、The verification experiment of the of D-GPS/ADS-C combination prototype system iscarried out. Using the PATS-EG environment of PATS platform to verify the aircraft data revevingeuipment and aircraft auxiliary monitoring system’s ability to receive the ACARS message. Theresults show that it can receive the ACARS message and it meets the requirement of surface targetsurveillance on real-time and accuracy rate. Based on college digital map, the real test about thecombination prototype system is finished. The performance of the D-GPS, on-board terminaland the monitoring is verified.
Through the verification experiment of the combination prototype system, we draw thefollowing conclusion. First, the results of research on the key techniques of moving targetsurveillance on airport surface are effective and valuable. Second, the combination prototypesystem can provide useful basis to build new moving target surveillance system on airport surface.
引文
[1]中国中国民用航空总局规划发展司.从统计看民航[M].北京:中国民航出版社，2009.
[2]吕小平.空中交通管理文集[M].航空工业出版社，2009.
[3] Molina J M, García J, Casar J R. Analysis of data fusion architectures and techniques in thedevelopment of an A-SMGCS Surveillance prototype[C]//Proceedings of the SeventhInternational Conference on Information Fusion, Stockholm, Sweden.2004:107–114.
[4] ICAO. Advanced surface movement guidance and control systems manual[R]. US. ICAO-9830,2004.
[5]陶诚.高级场面运动引导控制及仿真平台研究102870711-0001[D].南京：南京航空航天大学，2011.
[6] Nijhuis H B, Dehn D M, Schuver-Van Blanken M, et al. BETA[J].2003.
[7] Maycroft H. ATOPS Final Report for Publication, DERA, September2000[R]. ATOPS/P/DERA/2000/025.
[8] Pina P, de Pablo J M, Mas M. Linking Existing ON ground, ARrival and Departure Operations
[C]//Proceedings of the6th USA/Europe Air Traffic Management R&D Seminar, Baltimore,Maryland, USA.2005.
[9] EUROCONTROL. Airport Surface Management&Safety[EB/OL].[2010-10-26]. http://www.eurocontrol.int/airports/public/standard_page/asmgcs.html.
[10] A-SMGCS enters operation in Hamburg[EB/OL].[2010-09-26]. http://www.airport.de/en/109_3763.html.
[11] Evers C, Cassell R, Lee D. Analysis of ADS-B, ASDE-3and Multilateration surveillanceperformance–NASA Atlanta demonstration[C]//1998.
[12] Hemm R, Shapiro G, Lee D, et al. Benefit estimates of terminal area productivity programtechnologies[M]. NASA/CR-1999-208989,1999.
[13] Katz E S. Evaluation of a Prototype Advanced Taxiway Guidance System (ATGS)[R].DOT/FAA/AR-TN00/9,2000.
[14] Hawes C C, DiBenedetto M F. The local area augmentation system: an airport surveillanceapplication supporting the FAA Runway Incursion Reduction Program demonstration at theDallas/Fort Worth International Airport[C]//Digital Avionics Systems,2001. DASC. The20thConference.2002,1:2E2.
[15] Capezzuto V, Olster D, Curry M, et al. Runway incursion reduction program (RIRP)surveillance system, NASA/FAA Atlanta demonstration[C]//Digital Avionics SystemsConference,1998. Proceedings.,17th DASC. The AIAA/IEEE/SAE.2002,2: F31.
[16] Johnson M. Runway Awareness&Advisory System (RAAS)[C]//Orlando, USA:2003.
[17]赵海波,董昀.场面监视雷达系统在浦东国际机场的应用[J].民航经济与技术,1999(02).
[18]陈培英. SMR和MDS信息在空中交通管制系统中的应用[J].现代电子工程,2008(001):4-6,37.
[19] Kesseler E, Knapen E. Designing future advanced controller displays[J]. Information DesignJournal,2002,11(1):32–43.
[20] Bussolari, S.(2000). Surveillance Tools for Distributed Air/Ground Traffic Man-agement.http://www.asc.nasa.gov/aatt/wspdfs/Bussolari_SurveillanceTools.pdf
[21] Brown A K. A review of radar as a sensor for advanced surface movement guidance and controlsystems (A-SMGCS)[C]//Proceedings of IEEE international conference on AviationSurveillance Systems.2002:7/1-7/4
[22] Hicok D S，Lee D. Application of ADS-B for airport surface surveillance[C]//DigitalAvionics Systems Conference,1998. Proceedings.,17th DASC. The AIAA/IEEE/SAE.1998,2:F34/1-F34/8.
[23]郭昊.几种机场场面监视技术的比较[J].中国高新技术企业,2007,(15):100,106.
[24]张琳琳,黄圣国,陶诚.基于ARM的嵌入式机场场面车载终端设计[J].哈尔滨商业大学学报（自然科学版）,2010,26(2):173-176.
[25]强成,张学军,吴财军. A-SMGCS中基于QNX平台的车载终端的设计研究[J].电子技术应用,2006(01):8-11.
[26]强成,罗喜伶,袁永琼.高级场面监视系统车载终端的设计与实现[J].计算机工程,2007(01).
[27]祝宏,曾祥进.多传感器信息融合研究综述[J].计算机与数字工程，2007，35（012）：46-48.
[28] HALL D L，LLINAS J．An introduction to multi-sensor data fusion[J]．Proc IEEE，1997，85(1)：6—23．
[29]王会清，韩艳玲.基于多传感器与数据融合技术的研究[J].计算机与现代化，2002，9：64-67.
[30]黄友澎.多传感器多目标航迹相关与数据合成若干关键技术研究[D].哈尔滨工程大学，2009.6
[31] Carrozzo A., Alba P., Capodimonte F., et al. Data Fusion Techniques for a-Smgcs[C]. RadarConference,2004. EURAD. First European:283-287
[32] Bar-Shalom Y, Li X R. Estimation and Tracking: Pfinciples, Techniques, and Software[M].Artech House,Boston,London,1993.
[33] Smith D.,Singh S..Approches to multisensory data fusion in target tracking: a survey[J]. IEEETrans. on Knowledge and data dengineering,2006:18(12)
[34] Chen H, Li X R. Bayesian model selection for multisensor track-to-track association and trackfusion[C]. IEEE/SP13th Workshop on Statistical Signal Processing,2005:515-520
[35] Morelande M,Kestela K.. A Bayesian Approach to Multiple Target Detection and Tracking[J].IEEE Trans. om Signal Processing,2007,55(5)
[36] Blasch E., Lang H. Data association through fusion of target track and identification sets[C].Proceedings of the Third International Conference on Information Fusion,2000. TUD2/17-TUD2/23vol.1
[37] SingerR A, Stein J J·An optimal tracking filter for processing sensor data of impreciselydetermined origin insurveillance system [C]//Proceedings of the tenth IEEE Conference onDecision and Control·USA: institute ofElectrical and Electronics Engineers,1971:171-175.
[38] Singer R A, Sea R G. A new filter for optimal trackingin densemultitarget environment[C]//Proceedings of the ninthAllerton Conference Circuit and System Theory·Urbana-Champaign,USA: Univ·of Illinois,1971:201-211.
[39] Blackman S S,“Association and Fusion of Multiple Sensor Data,” Multitarget MultisensorTracking: Advanced Applications, Y. Bar-Shalom, ed., pp.187-218, Mass.: Artech House,1990.
[40] Bar-Shalom Y, Tse E,“Tracking in a Cluttered Environment with Probabilistic DataAssociation,” Automatica,1975,11:451-460.
[41] Bar-Shalom Y,“Extension of the Probabilistic Data Association Filter to Multitarget tracking,”Proc. Fifth Symp. Nonlinear Estimation, pp.16-21, Sept.1974.
[42] Bloem E A, Blom H A P,“Joint Probabilistic Data Association Methods Avoiding TrackCoalescence,” Proc.34th IEEE Conf. Decision and Control, vol.3, pp.2752-2757,1995.
[43] R.J. Fitzgerald,“Development of Practical PDA Logic for Multitarget Tracking byMicroprocessor,” Multitarget Multisensor Tracking: Advanced Applications, Y. Bar-Shalom,ed.,1989.
[44] Winter M, Favier G,A Neural Network for Data Association, Proc.1999IEEE Int’l Conf.Acoustics, Speech, and SignalProcessing, vol.2, pp.1041-1044, Mar.1999.
[45] Hossam O, Farooq M, Qauch T. Fuzzy logic approach to data association, Proc.SPIE2755,1996:313-321.
[46] Chen Y M, Huang H C. Fuzzy logic approach to multisensor data association [J]. Mathematicsand Computers in Simulation2000，52:399-412.
[47] Mourad Oussalah, Joris De Schutter. Hybrid fuzzy probabilistic data association filter and jointprobabilistic data association filter [J]. Information Sciences2002，142:195-226.
[48] Singer R K. Computer Control of Multiple Site Track Correlation [J]. Automatica,1971,10(7):455-464.
[49] Bar-Shalom Y. On the Track-to-Track Correlation Problem [J]. IEEE Trans on AutomaticControl,1981,26(2):571-572.
[50] Bar-Shalom Y, Camp L. The Effect of Common Process Noise o n the Two-Sensor Fused-Track Covariance [J]. IEEE Trans on Aerospace Electronic Systems,1986,22(6):803-805.
[51] Saha R K. Effect of Common Process Noise on Tw o-Sensor Track Fusion [J]. AIAA Journalof Guience, Control and Dynamics,1996,19(4):829-835.
[52] Saha R K. Track-to-Track Fusion with Dissimilar Sensor s [J]. IEEE Trans on AerospaceElectronic Systems,1996,32(3):1021-1029.
[53] Bar-Shalom Y. Multitarget-Multisensor Tracking: Application and Advances [M]. Dedham,MA:Ar tech Ho use,1990.
[54]刘枫.目标跟踪的算法研究与应用[D].江南大学，2011
[55] Besada J A, Garcia J, De-Miguel G, et al. Design of IMM filter for radar tracking usingevolution strategies[J]. IEEE Trans. on Aerospace and Electronic Systems,2005,41(3):1109-1122.
[56] Zhang M, Knedlik S, Loffeld O. An adaptive road-constrained IMM estimator for ground targettracking in GSM networks[C]//Proc of the11th International Conference on InformationFusion. Cologne, Germany: IEEE Press,2008:1-8.
[57] Li X R. Multiple-Model Estimation with Variable Structure: Some theoretical consideration[C].//Proc. of the33rd IEEE Conference on Decision and Control,1994:1199-1204.
[58] Li X R., Bar-Shalom Y.. Multiple-model estimation with variable structure[J]. IEEETransactions on Automatic Control,1996,41(4):478-493.
[59]何宝林.多雷达航迹关联与融合技术研究[D].北京：北京工业大学，2010（.HE B L. Study onTrack Association&Fusion of Multiple Radar System[D]. Beijing: Beijing University ofTechnology,2010.）
[60] He Y, Zhang J W. New Track Correlation Algorithms in a Multisensor Data Fusion System[J].IEEE Trans. On Aerospace and Electronic Systems,2006,42(4):1359-1371.
[61] Billur B, Birsel A. Fuzzy clustering and enumeration of target type based on sonar returns[J].Pattern Recognition,2004,37:189-199.
[62]丁钱龙.基于FCM的雷达多目标航迹相关[D].大连：大连海事大学，2010.
[63] Hossam O.,Farooq M.,Quach T..Fuzzy logic approach to data association[J].Proceedings ofSPIE,1996,2755:313-321.
[64] Aziz A. M.，Roberto. Fuzzy logic data correlation in multisensory-multitarget trackingsystem[J]. Signal Processing,1999,76(2):195-209.
[65] Xu R, Wunsch D．Survey of clustering algorithm[J].IEEE Transactions on Neural Networks,2005,16(3)：645-678.
[66]诸克军，苏顺华，黎金玲.模糊C-均值中的最优聚类与最佳聚类数[J].系统工程理论与实践，2005，3：52-61.
[67] Jaramillo A S, Portas J A B, de Miguel V. Airport Based Bias Estimation of SurfaceMovement Radars[C]//Proceeding of IEEE International Conference on Radar,2005:916-921.
[68] Besada J A, Soto A, de Miguel V. Design of an A-SMGCS prototype at Barajas airport: airportsurveillance sensors bias estimation[C]//Proceeding of IEEE International Conference onInformation Fusion,2005:1344-1350.
[69] Simon H.. Neural Networks a comprehensive foundation[M].Prentice-Hall,1999.
[70] ICAO.Automatic dependent surveillance.Circular226-AN/135.Montreal,Canada:ICAO,1990
[71]白松浩，朱晓辉，陈志杰.广播式与合约式自动相关监视的信息转换[J].系统工程与电子技术，2005，27（9）：1658-1660.
[72]周其焕.自动相关监视及其数据链的标准.中国民航学院学报,1995.9Vol.13(3):70～85
[73]Donald McCallie,Jonathan Butts, Robert Mills. Security analysis of the ADS-B implementation inthe next generation air transportation system[J]. International Journal of Critical InfrastructureProtection,2011,4:78-87.
[74]曾思弘.多点相关定位技术及其在北京首都机场的实施和应用[J].空中交通管理，2010（11）：13-15，66.，
[75]王广运，郭秉义，李洪涛.差分GPS定位技术与应用[M].北京：电子工业出版社，1999.3.
[76]吴俐民，丁仁军，李凤霞. GPS参考站系统原理与应用[M].成都：西南交通大学出版社，2008.
[77]罗琳玲.航迹融合系统的研究与实现[D].电子科技大学，2009.
[78]Farina A. Application of knowledge-based techniques to tracking function.[EB/OL].
[2011-05-06]. http://www.rto.nato.int.
[79]宫淑丽，王帮峰，吴红兰，黄圣国.基于IMM算法的机场场面运动目标跟踪研究[J].《系统工程与电子技术》2011,33(10)：2322-2326.
[80]彭亮.机动目标跟踪算法的研究[D].西北工业大学，2007.
[81]朱荣花.多假设航迹合成算法的研究[D].哈尔滨工业大学，2007.
[82]Wan E A, Vand-Der M R. The unscented kalman filter for nonlinear estimation[C]//Proceedingsof International Symposium on Adaptive Systems for Signal Processing, Communications andControl. Alberta, Canada,2000:153-158.
[83]Kandepu R, Foss B, Imsland L. Applying the unscented Kalman filter for nonlinear stateestimation [J]. Journal of Process Control,2008,18(7-8):753-768.
[84]Cork L, Walker R. Sensor fault detection for UAVs using a nonlinear dynamic model and theIMM-UKF algorithm[C]//Proc of2007Information, Decision and Control. Adelaide, Australia.IEEE Press,2007:230-235.
[85]Julier S J, Uhlmann J K. Unscented filter and nonlinear estimation [J]. Proceedings of the IEEE,2004,92(3):401-422.
[86]王珊珊，李续武，周晓滨.基于多模型算法的低空机动目标跟踪研究[J].计算机工程与应用,2008,44(1):222-224,228.
[87]周宏仁，敬忠良，王培德.机动目标跟踪[M].国防工业出版社，1991.
[88]Wang W, Hou H L. An improved Current Statistical Model for maneuvering target tracking[C]//Proc of the4th IEEE Conference on Industrial Electronics and Applications. Xi'an, China. IEEEPress,2009:4017-4020.
[89]Gong S. L., Wu H.L, Tao C, et al. Tracking Target on Airport Surface Based on IMM-UKFAlgorithm[C]//International Conferendce on Optoelectronics and Image Processing.2010,2:671-675.
[90]Cai M, Zong C G. An Improved IMM Algorithm with Variable Structure[C].//Proc. Of the2ndconference on Environmental Science and Information Application Technology, Haikou:[sn]2010:507-510.
[91]嵇成新，许江湖，张永胜，等.一种跟踪机动目标的变结构交互多模型滤波算法[J].系统工程与电子技术，2003，25（5）：595-599.
[92]Li X R.Hybrid estimation techniques[J]. In Control and Dynamic Systems: Advances in Theoryand Applications,1996,76:2136-2287.
[93]Li X R. Multiple-Model Estimation with Variable Structure: Some theoretical consideration[C].//Proc. of the33rd IEEE Conference on Decision and Control,1994:1199-1204.
[94]宫淑丽，陶诚，王帮峰，黄圣国.基于VS-IMM的机场场面监视技术研究[J].南京航空航天大学学报（自然科学版）[J].2012，44（1）：118-123.
[95]Herrero J G, Besada Portas J A, Casar Corredera J R. Use of Map Information for TrackingTargets on Airport Surface[J]. IEEE Transactions on Aerospace and Electronic Systems,2003，39(2),675-693.
[96]Shuli Gong，Xuhui Wang，Shengguo Huang. Tracking Moving Target on Airport Suface Based onVariable-Structure IMM Algorithm[C]//International Conferendce on Advanced MaterialResearch,2012,459:603-608.
[97]Kirubarajan T, Bar-shalom Y, Pattipati K R, etl. Ground Target Tracking with Variable StructureIMM Estimator[J]. IEEE Transactions on Aerospace and Electronic Systems,2000,36(1):26-46.
[98]蒋乃欣，张军，罗喜伶，等.ADS与多雷达数据融合中的系统误差配准法[J].北京航空航天大学学报，2005，31（1）：78-81.
[99]余谅,邓宏洋.空中交通管制系统中多雷达数据的配准法[J].四川联合大学学报，1999，3（3）：19-25.
[100]诸克军，苏顺华，黎金玲.模糊C-均值中的最优聚类与最佳聚类数[J].系统工程理论与实践，2005，3：52-61.
[101] Xu R, Wunsch D．Survey of clustering algorithm[J].IEEE Transactions on Neural Networks,2005,16(3)：645-678.
[102]陈黎飞,姜青山,王声瑞.基于层次划分的最佳聚类数确定方法[J].软件学报,2008,19(1):62-72.
[103]宫淑丽，陶诚，黄圣国. A-SMGCS的多场面监视雷达多目标航迹相关[J].计算机测量与控制，2012,20(8):2183-2186.
[104]毕心安.论两种地空数据链的差别和系统过渡[J].民航经济与技术，1999(02):
[105] Component Maintenance Manual. ACARS Management Unit[M]. Honeywell International Inc.,Jul13/07, ECCN:7E994.
[106] AIRLINES ELECTRONIC ENGINEERING COMMITTEE. ARINC SPECIFICATION618-4[S]. AERONAUTICAL RADIO, INC.,1999.
[107]黄永芳.飞机状态监控系统的数据处理和应用技术[D].南京：南京航空航天大学，2002.
[108] ARINC. Technical Presentation for VHF Data Link Implementation [M]. USA, ARINCInc,1995.
[109]周健星.地空数据链实时信息处理和监控仿真系统[D].南京航空航天大学，2007.
[110] AIR/GROUD CHARACTER-ORIENTED PROTOCOL SPECIFICATION618-4[S]，1999.8.
[111]王旭辉.飞机飞行安全实时监控关键技术研究[D].南京：南京航空航天大学，2008.
[112]卢伯英，张军，唐争由.自动相关监视系统的研究与开发[J].北京航空航天大学学报，1997，23（2）：147-151.
[113]张学军，屈剑明，张其善.基于VHF数据链的自动相关监视系统[J].北京航空航天大学学报，1999，25（5）：521-523.
[114]王广运.载波相位差分GPS定位技术[J].测绘工程,1999,8(1):12-17.
[115]GB/T13814-2009全球定位系统（GPS）测量规范[S]，2009.
[116] JSCORS实践与应用[M].江苏：江苏省测绘工程院，JSCORS中心，2008.10.
[117]吴红兰.基于A-SMGCS原型系统样机研制[D].南京航空航天大学，2011
[118]杨启和.地图投影变换原理与方法[M].北京：解放军出版社,1990.2.
[119] Steve Dutch.Converting UTM to Latitude and Longitude(Or Vice Versa)[Z/OL].http://www.uwgb.edu/dutchs/UsefulData/UTMFormulas.htm,2003.9.12/2009.12.14.
[120] Alvarez J. I., Portas J. B., Herrero J. G., et al. Integration of ADS-B Surveillance Data inOperative Multiradar Tracking Processors[C]//International Conferendce on the11thInformation Fusion, Cologne:[sn]2008:1-8.
[121]孙国政，王索建，董扬.基于ADS-B数据的雷达精度测量方法[J].现代雷达，2011，33（2）：18-21.
[122] J A. Besada,J.Garcia, A. Soto, et al. On-line Sensor Calibration for Airport DataFusion[C]//2004IEEE Radar Conference, Philadelpia:[sn],2004:26-29
[123] Zadeh L A. Fuzzy sets[J].Information and Control,1965,8:338-353.
[124] Zadeh L A. Fuzzy Logic[J]. IEEE Trans. On Control System Magazine,1988:83-93.
[125]高新波.模糊聚类分析及其应用[M].西安：西安电子科技大学出版社，2004.
[126] Dunn J C. A graph theoretic analysis of pattern classification via Tamura’s fuzzy relation[J].IEEE Trans. SMC,1974,4(3):310-313.
[127] Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithm[M].PlenumPress,New York,1981.
[128] Grossberg S. Adaptive classification and universal recording: II[J]. Feedback, oscilation, andillusions. Bio. Cyber.1976,23:187-207.
[129] Kohonen T. Self-Organization and Associative Memory. Berlin:Springer-verlag,1984.
[130] Shih E Y. A new art-based neural architecuture for pattern classification and image enhancementwithout prior knowledge[J]. pattern Recognition,1992,25(5):533-542.
[131] Li W F. An adaptive Filter for Multi-sensor Track Fusion[C]. ICSP2008Proceeding,2008:231-235.
[132]冉金和，张玉.基于航迹隶属度的分布式系统数据融合算法[J].信号处理，2011，27（2）：226-229.
[133] Consumer Microcircuits Linmited CO CMX469A FFSK/MSK Modem [EB/OL].
[2001-05-02].http://www.cmlmicro co UK/datasheetcatalog/CMX469A FFSK.pdf.
[134]肖闽进.基于CMX469A的FFSK调制解调器设计与应用[J].常州工学院学报，2008，21（6）：34-37.
[135] The EDA Technology Leader-Mentor Graphics[EB/OL].[2011-06-17]. http://www.mentor.com/.
[136]赵光. PADS2007高速电路板设计[M].北京：人民邮电出版社，2009.
[137] GB20263-2006,导航电子地图安全处理技术基本要求[S].北京：中国标准出版社，2006.
[138] Wikipedia. Geographic Data Files[EB/OL].[2010-06-25]. http://en.wikipedia.org/wiki/Geographic_Data_Files.
[139] GB/T19711-2005,导航地理数据模型与交换格式[M].北京：中国标准出版社，2005.
[2]吕小平.空中交通管理文集[M].航空工业出版社，2009.
[3] Molina J M, García J, Casar J R. Analysis of data fusion architectures and techniques in thedevelopment of an A-SMGCS Surveillance prototype[C]//Proceedings of the SeventhInternational Conference on Information Fusion, Stockholm, Sweden.2004:107–114.
[4] ICAO. Advanced surface movement guidance and control systems manual[R]. US. ICAO-9830,2004.
[5]陶诚.高级场面运动引导控制及仿真平台研究102870711-0001[D].南京：南京航空航天大学，2011.
[6] Nijhuis H B, Dehn D M, Schuver-Van Blanken M, et al. BETA[J].2003.
[7] Maycroft H. ATOPS Final Report for Publication, DERA, September2000[R]. ATOPS/P/DERA/2000/025.
[8] Pina P, de Pablo J M, Mas M. Linking Existing ON ground, ARrival and Departure Operations
[C]//Proceedings of the6th USA/Europe Air Traffic Management R&D Seminar, Baltimore,Maryland, USA.2005.
[9] EUROCONTROL. Airport Surface Management&Safety[EB/OL].[2010-10-26]. http://www.eurocontrol.int/airports/public/standard_page/asmgcs.html.
[10] A-SMGCS enters operation in Hamburg[EB/OL].[2010-09-26]. http://www.airport.de/en/109_3763.html.
[11] Evers C, Cassell R, Lee D. Analysis of ADS-B, ASDE-3and Multilateration surveillanceperformance–NASA Atlanta demonstration[C]//1998.
[12] Hemm R, Shapiro G, Lee D, et al. Benefit estimates of terminal area productivity programtechnologies[M]. NASA/CR-1999-208989,1999.
[13] Katz E S. Evaluation of a Prototype Advanced Taxiway Guidance System (ATGS)[R].DOT/FAA/AR-TN00/9,2000.
[14] Hawes C C, DiBenedetto M F. The local area augmentation system: an airport surveillanceapplication supporting the FAA Runway Incursion Reduction Program demonstration at theDallas/Fort Worth International Airport[C]//Digital Avionics Systems,2001. DASC. The20thConference.2002,1:2E2.
[15] Capezzuto V, Olster D, Curry M, et al. Runway incursion reduction program (RIRP)surveillance system, NASA/FAA Atlanta demonstration[C]//Digital Avionics SystemsConference,1998. Proceedings.,17th DASC. The AIAA/IEEE/SAE.2002,2: F31.
[16] Johnson M. Runway Awareness&Advisory System (RAAS)[C]//Orlando, USA:2003.
[17]赵海波,董昀.场面监视雷达系统在浦东国际机场的应用[J].民航经济与技术,1999(02).
[18]陈培英. SMR和MDS信息在空中交通管制系统中的应用[J].现代电子工程,2008(001):4-6,37.
[19] Kesseler E, Knapen E. Designing future advanced controller displays[J]. Information DesignJournal,2002,11(1):32–43.
[20] Bussolari, S.(2000). Surveillance Tools for Distributed Air/Ground Traffic Man-agement.http://www.asc.nasa.gov/aatt/wspdfs/Bussolari_SurveillanceTools.pdf
[21] Brown A K. A review of radar as a sensor for advanced surface movement guidance and controlsystems (A-SMGCS)[C]//Proceedings of IEEE international conference on AviationSurveillance Systems.2002:7/1-7/4
[22] Hicok D S，Lee D. Application of ADS-B for airport surface surveillance[C]//DigitalAvionics Systems Conference,1998. Proceedings.,17th DASC. The AIAA/IEEE/SAE.1998,2:F34/1-F34/8.
[23]郭昊.几种机场场面监视技术的比较[J].中国高新技术企业,2007,(15):100,106.
[24]张琳琳,黄圣国,陶诚.基于ARM的嵌入式机场场面车载终端设计[J].哈尔滨商业大学学报（自然科学版）,2010,26(2):173-176.
[25]强成,张学军,吴财军. A-SMGCS中基于QNX平台的车载终端的设计研究[J].电子技术应用,2006(01):8-11.
[26]强成,罗喜伶,袁永琼.高级场面监视系统车载终端的设计与实现[J].计算机工程,2007(01).
[27]祝宏,曾祥进.多传感器信息融合研究综述[J].计算机与数字工程，2007，35（012）：46-48.
[28] HALL D L，LLINAS J．An introduction to multi-sensor data fusion[J]．Proc IEEE，1997，85(1)：6—23．
[29]王会清，韩艳玲.基于多传感器与数据融合技术的研究[J].计算机与现代化，2002，9：64-67.
[30]黄友澎.多传感器多目标航迹相关与数据合成若干关键技术研究[D].哈尔滨工程大学，2009.6
[31] Carrozzo A., Alba P., Capodimonte F., et al. Data Fusion Techniques for a-Smgcs[C]. RadarConference,2004. EURAD. First European:283-287
[32] Bar-Shalom Y, Li X R. Estimation and Tracking: Pfinciples, Techniques, and Software[M].Artech House,Boston,London,1993.
[33] Smith D.,Singh S..Approches to multisensory data fusion in target tracking: a survey[J]. IEEETrans. on Knowledge and data dengineering,2006:18(12)
[34] Chen H, Li X R. Bayesian model selection for multisensor track-to-track association and trackfusion[C]. IEEE/SP13th Workshop on Statistical Signal Processing,2005:515-520
[35] Morelande M,Kestela K.. A Bayesian Approach to Multiple Target Detection and Tracking[J].IEEE Trans. om Signal Processing,2007,55(5)
[36] Blasch E., Lang H. Data association through fusion of target track and identification sets[C].Proceedings of the Third International Conference on Information Fusion,2000. TUD2/17-TUD2/23vol.1
[37] SingerR A, Stein J J·An optimal tracking filter for processing sensor data of impreciselydetermined origin insurveillance system [C]//Proceedings of the tenth IEEE Conference onDecision and Control·USA: institute ofElectrical and Electronics Engineers,1971:171-175.
[38] Singer R A, Sea R G. A new filter for optimal trackingin densemultitarget environment[C]//Proceedings of the ninthAllerton Conference Circuit and System Theory·Urbana-Champaign,USA: Univ·of Illinois,1971:201-211.
[39] Blackman S S,“Association and Fusion of Multiple Sensor Data,” Multitarget MultisensorTracking: Advanced Applications, Y. Bar-Shalom, ed., pp.187-218, Mass.: Artech House,1990.
[40] Bar-Shalom Y, Tse E,“Tracking in a Cluttered Environment with Probabilistic DataAssociation,” Automatica,1975,11:451-460.
[41] Bar-Shalom Y,“Extension of the Probabilistic Data Association Filter to Multitarget tracking,”Proc. Fifth Symp. Nonlinear Estimation, pp.16-21, Sept.1974.
[42] Bloem E A, Blom H A P,“Joint Probabilistic Data Association Methods Avoiding TrackCoalescence,” Proc.34th IEEE Conf. Decision and Control, vol.3, pp.2752-2757,1995.
[43] R.J. Fitzgerald,“Development of Practical PDA Logic for Multitarget Tracking byMicroprocessor,” Multitarget Multisensor Tracking: Advanced Applications, Y. Bar-Shalom,ed.,1989.
[44] Winter M, Favier G,A Neural Network for Data Association, Proc.1999IEEE Int’l Conf.Acoustics, Speech, and SignalProcessing, vol.2, pp.1041-1044, Mar.1999.
[45] Hossam O, Farooq M, Qauch T. Fuzzy logic approach to data association, Proc.SPIE2755,1996:313-321.
[46] Chen Y M, Huang H C. Fuzzy logic approach to multisensor data association [J]. Mathematicsand Computers in Simulation2000，52:399-412.
[47] Mourad Oussalah, Joris De Schutter. Hybrid fuzzy probabilistic data association filter and jointprobabilistic data association filter [J]. Information Sciences2002，142:195-226.
[48] Singer R K. Computer Control of Multiple Site Track Correlation [J]. Automatica,1971,10(7):455-464.
[49] Bar-Shalom Y. On the Track-to-Track Correlation Problem [J]. IEEE Trans on AutomaticControl,1981,26(2):571-572.
[50] Bar-Shalom Y, Camp L. The Effect of Common Process Noise o n the Two-Sensor Fused-Track Covariance [J]. IEEE Trans on Aerospace Electronic Systems,1986,22(6):803-805.
[51] Saha R K. Effect of Common Process Noise on Tw o-Sensor Track Fusion [J]. AIAA Journalof Guience, Control and Dynamics,1996,19(4):829-835.
[52] Saha R K. Track-to-Track Fusion with Dissimilar Sensor s [J]. IEEE Trans on AerospaceElectronic Systems,1996,32(3):1021-1029.
[53] Bar-Shalom Y. Multitarget-Multisensor Tracking: Application and Advances [M]. Dedham,MA:Ar tech Ho use,1990.
[54]刘枫.目标跟踪的算法研究与应用[D].江南大学，2011
[55] Besada J A, Garcia J, De-Miguel G, et al. Design of IMM filter for radar tracking usingevolution strategies[J]. IEEE Trans. on Aerospace and Electronic Systems,2005,41(3):1109-1122.
[56] Zhang M, Knedlik S, Loffeld O. An adaptive road-constrained IMM estimator for ground targettracking in GSM networks[C]//Proc of the11th International Conference on InformationFusion. Cologne, Germany: IEEE Press,2008:1-8.
[57] Li X R. Multiple-Model Estimation with Variable Structure: Some theoretical consideration[C].//Proc. of the33rd IEEE Conference on Decision and Control,1994:1199-1204.
[58] Li X R., Bar-Shalom Y.. Multiple-model estimation with variable structure[J]. IEEETransactions on Automatic Control,1996,41(4):478-493.
[59]何宝林.多雷达航迹关联与融合技术研究[D].北京：北京工业大学，2010（.HE B L. Study onTrack Association&Fusion of Multiple Radar System[D]. Beijing: Beijing University ofTechnology,2010.）
[60] He Y, Zhang J W. New Track Correlation Algorithms in a Multisensor Data Fusion System[J].IEEE Trans. On Aerospace and Electronic Systems,2006,42(4):1359-1371.
[61] Billur B, Birsel A. Fuzzy clustering and enumeration of target type based on sonar returns[J].Pattern Recognition,2004,37:189-199.
[62]丁钱龙.基于FCM的雷达多目标航迹相关[D].大连：大连海事大学，2010.
[63] Hossam O.,Farooq M.,Quach T..Fuzzy logic approach to data association[J].Proceedings ofSPIE,1996,2755:313-321.
[64] Aziz A. M.，Roberto. Fuzzy logic data correlation in multisensory-multitarget trackingsystem[J]. Signal Processing,1999,76(2):195-209.
[65] Xu R, Wunsch D．Survey of clustering algorithm[J].IEEE Transactions on Neural Networks,2005,16(3)：645-678.
[66]诸克军，苏顺华，黎金玲.模糊C-均值中的最优聚类与最佳聚类数[J].系统工程理论与实践，2005，3：52-61.
[67] Jaramillo A S, Portas J A B, de Miguel V. Airport Based Bias Estimation of SurfaceMovement Radars[C]//Proceeding of IEEE International Conference on Radar,2005:916-921.
[68] Besada J A, Soto A, de Miguel V. Design of an A-SMGCS prototype at Barajas airport: airportsurveillance sensors bias estimation[C]//Proceeding of IEEE International Conference onInformation Fusion,2005:1344-1350.
[69] Simon H.. Neural Networks a comprehensive foundation[M].Prentice-Hall,1999.
[70] ICAO.Automatic dependent surveillance.Circular226-AN/135.Montreal,Canada:ICAO,1990
[71]白松浩，朱晓辉，陈志杰.广播式与合约式自动相关监视的信息转换[J].系统工程与电子技术，2005，27（9）：1658-1660.
[72]周其焕.自动相关监视及其数据链的标准.中国民航学院学报,1995.9Vol.13(3):70～85
[73]Donald McCallie,Jonathan Butts, Robert Mills. Security analysis of the ADS-B implementation inthe next generation air transportation system[J]. International Journal of Critical InfrastructureProtection,2011,4:78-87.
[74]曾思弘.多点相关定位技术及其在北京首都机场的实施和应用[J].空中交通管理，2010（11）：13-15，66.，
[75]王广运，郭秉义，李洪涛.差分GPS定位技术与应用[M].北京：电子工业出版社，1999.3.
[76]吴俐民，丁仁军，李凤霞. GPS参考站系统原理与应用[M].成都：西南交通大学出版社，2008.
[77]罗琳玲.航迹融合系统的研究与实现[D].电子科技大学，2009.
[78]Farina A. Application of knowledge-based techniques to tracking function.[EB/OL].
[2011-05-06]. http://www.rto.nato.int.
[79]宫淑丽，王帮峰，吴红兰，黄圣国.基于IMM算法的机场场面运动目标跟踪研究[J].《系统工程与电子技术》2011,33(10)：2322-2326.
[80]彭亮.机动目标跟踪算法的研究[D].西北工业大学，2007.
[81]朱荣花.多假设航迹合成算法的研究[D].哈尔滨工业大学，2007.
[82]Wan E A, Vand-Der M R. The unscented kalman filter for nonlinear estimation[C]//Proceedingsof International Symposium on Adaptive Systems for Signal Processing, Communications andControl. Alberta, Canada,2000:153-158.
[83]Kandepu R, Foss B, Imsland L. Applying the unscented Kalman filter for nonlinear stateestimation [J]. Journal of Process Control,2008,18(7-8):753-768.
[84]Cork L, Walker R. Sensor fault detection for UAVs using a nonlinear dynamic model and theIMM-UKF algorithm[C]//Proc of2007Information, Decision and Control. Adelaide, Australia.IEEE Press,2007:230-235.
[85]Julier S J, Uhlmann J K. Unscented filter and nonlinear estimation [J]. Proceedings of the IEEE,2004,92(3):401-422.
[86]王珊珊，李续武，周晓滨.基于多模型算法的低空机动目标跟踪研究[J].计算机工程与应用,2008,44(1):222-224,228.
[87]周宏仁，敬忠良，王培德.机动目标跟踪[M].国防工业出版社，1991.
[88]Wang W, Hou H L. An improved Current Statistical Model for maneuvering target tracking[C]//Proc of the4th IEEE Conference on Industrial Electronics and Applications. Xi'an, China. IEEEPress,2009:4017-4020.
[89]Gong S. L., Wu H.L, Tao C, et al. Tracking Target on Airport Surface Based on IMM-UKFAlgorithm[C]//International Conferendce on Optoelectronics and Image Processing.2010,2:671-675.
[90]Cai M, Zong C G. An Improved IMM Algorithm with Variable Structure[C].//Proc. Of the2ndconference on Environmental Science and Information Application Technology, Haikou:[sn]2010:507-510.
[91]嵇成新，许江湖，张永胜，等.一种跟踪机动目标的变结构交互多模型滤波算法[J].系统工程与电子技术，2003，25（5）：595-599.
[92]Li X R.Hybrid estimation techniques[J]. In Control and Dynamic Systems: Advances in Theoryand Applications,1996,76:2136-2287.
[93]Li X R. Multiple-Model Estimation with Variable Structure: Some theoretical consideration[C].//Proc. of the33rd IEEE Conference on Decision and Control,1994:1199-1204.
[94]宫淑丽，陶诚，王帮峰，黄圣国.基于VS-IMM的机场场面监视技术研究[J].南京航空航天大学学报（自然科学版）[J].2012，44（1）：118-123.
[95]Herrero J G, Besada Portas J A, Casar Corredera J R. Use of Map Information for TrackingTargets on Airport Surface[J]. IEEE Transactions on Aerospace and Electronic Systems,2003，39(2),675-693.
[96]Shuli Gong，Xuhui Wang，Shengguo Huang. Tracking Moving Target on Airport Suface Based onVariable-Structure IMM Algorithm[C]//International Conferendce on Advanced MaterialResearch,2012,459:603-608.
[97]Kirubarajan T, Bar-shalom Y, Pattipati K R, etl. Ground Target Tracking with Variable StructureIMM Estimator[J]. IEEE Transactions on Aerospace and Electronic Systems,2000,36(1):26-46.
[98]蒋乃欣，张军，罗喜伶，等.ADS与多雷达数据融合中的系统误差配准法[J].北京航空航天大学学报，2005，31（1）：78-81.
[99]余谅,邓宏洋.空中交通管制系统中多雷达数据的配准法[J].四川联合大学学报，1999，3（3）：19-25.
[100]诸克军，苏顺华，黎金玲.模糊C-均值中的最优聚类与最佳聚类数[J].系统工程理论与实践，2005，3：52-61.
[101] Xu R, Wunsch D．Survey of clustering algorithm[J].IEEE Transactions on Neural Networks,2005,16(3)：645-678.
[102]陈黎飞,姜青山,王声瑞.基于层次划分的最佳聚类数确定方法[J].软件学报,2008,19(1):62-72.
[103]宫淑丽，陶诚，黄圣国. A-SMGCS的多场面监视雷达多目标航迹相关[J].计算机测量与控制，2012,20(8):2183-2186.
[104]毕心安.论两种地空数据链的差别和系统过渡[J].民航经济与技术，1999(02):
[105] Component Maintenance Manual. ACARS Management Unit[M]. Honeywell International Inc.,Jul13/07, ECCN:7E994.
[106] AIRLINES ELECTRONIC ENGINEERING COMMITTEE. ARINC SPECIFICATION618-4[S]. AERONAUTICAL RADIO, INC.,1999.
[107]黄永芳.飞机状态监控系统的数据处理和应用技术[D].南京：南京航空航天大学，2002.
[108] ARINC. Technical Presentation for VHF Data Link Implementation [M]. USA, ARINCInc,1995.
[109]周健星.地空数据链实时信息处理和监控仿真系统[D].南京航空航天大学，2007.
[110] AIR/GROUD CHARACTER-ORIENTED PROTOCOL SPECIFICATION618-4[S]，1999.8.
[111]王旭辉.飞机飞行安全实时监控关键技术研究[D].南京：南京航空航天大学，2008.
[112]卢伯英，张军，唐争由.自动相关监视系统的研究与开发[J].北京航空航天大学学报，1997，23（2）：147-151.
[113]张学军，屈剑明，张其善.基于VHF数据链的自动相关监视系统[J].北京航空航天大学学报，1999，25（5）：521-523.
[114]王广运.载波相位差分GPS定位技术[J].测绘工程,1999,8(1):12-17.
[115]GB/T13814-2009全球定位系统（GPS）测量规范[S]，2009.
[116] JSCORS实践与应用[M].江苏：江苏省测绘工程院，JSCORS中心，2008.10.
[117]吴红兰.基于A-SMGCS原型系统样机研制[D].南京航空航天大学，2011
[118]杨启和.地图投影变换原理与方法[M].北京：解放军出版社,1990.2.
[119] Steve Dutch.Converting UTM to Latitude and Longitude(Or Vice Versa)[Z/OL].http://www.uwgb.edu/dutchs/UsefulData/UTMFormulas.htm,2003.9.12/2009.12.14.
[120] Alvarez J. I., Portas J. B., Herrero J. G., et al. Integration of ADS-B Surveillance Data inOperative Multiradar Tracking Processors[C]//International Conferendce on the11thInformation Fusion, Cologne:[sn]2008:1-8.
[121]孙国政，王索建，董扬.基于ADS-B数据的雷达精度测量方法[J].现代雷达，2011，33（2）：18-21.
[122] J A. Besada,J.Garcia, A. Soto, et al. On-line Sensor Calibration for Airport DataFusion[C]//2004IEEE Radar Conference, Philadelpia:[sn],2004:26-29
[123] Zadeh L A. Fuzzy sets[J].Information and Control,1965,8:338-353.
[124] Zadeh L A. Fuzzy Logic[J]. IEEE Trans. On Control System Magazine,1988:83-93.
[125]高新波.模糊聚类分析及其应用[M].西安：西安电子科技大学出版社，2004.
[126] Dunn J C. A graph theoretic analysis of pattern classification via Tamura’s fuzzy relation[J].IEEE Trans. SMC,1974,4(3):310-313.
[127] Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithm[M].PlenumPress,New York,1981.
[128] Grossberg S. Adaptive classification and universal recording: II[J]. Feedback, oscilation, andillusions. Bio. Cyber.1976,23:187-207.
[129] Kohonen T. Self-Organization and Associative Memory. Berlin:Springer-verlag,1984.
[130] Shih E Y. A new art-based neural architecuture for pattern classification and image enhancementwithout prior knowledge[J]. pattern Recognition,1992,25(5):533-542.
[131] Li W F. An adaptive Filter for Multi-sensor Track Fusion[C]. ICSP2008Proceeding,2008:231-235.
[132]冉金和，张玉.基于航迹隶属度的分布式系统数据融合算法[J].信号处理，2011，27（2）：226-229.
[133] Consumer Microcircuits Linmited CO CMX469A FFSK/MSK Modem [EB/OL].
[2001-05-02].http://www.cmlmicro co UK/datasheetcatalog/CMX469A FFSK.pdf.
[134]肖闽进.基于CMX469A的FFSK调制解调器设计与应用[J].常州工学院学报，2008，21（6）：34-37.
[135] The EDA Technology Leader-Mentor Graphics[EB/OL].[2011-06-17]. http://www.mentor.com/.
[136]赵光. PADS2007高速电路板设计[M].北京：人民邮电出版社，2009.
[137] GB20263-2006,导航电子地图安全处理技术基本要求[S].北京：中国标准出版社，2006.
[138] Wikipedia. Geographic Data Files[EB/OL].[2010-06-25]. http://en.wikipedia.org/wiki/Geographic_Data_Files.
[139] GB/T19711-2005,导航地理数据模型与交换格式[M].北京：中国标准出版社，2005.