无线传感器网络节点定位技术研究
|
摘要
无线传感器网络集成了传感器技术、微机电技术、现代网络和无线通信技术,已成为当前IT领域研究的热点之一。由于其具有网络自组织、覆盖广以及高容错性等固有优点以及组网成本低、构建灵活、方便等特点,使得无线传感器网络在军事、民用等领域应用广泛。
节点定位技术是无线传感器网络的关键支撑技术之一,节点自身的正确定位是提供监测事件位置信息的前提。目前现有的无线传感器网络节点定位算法普遍存在有受环境影响大、精确度不高、不适用于移动节点定位等问题,随着无线传感器网络技术的不断进步成熟,其应用将会越来越广泛,节点定位技术研究对于传感器网络技术性能提高和实用性保证有重要的理论意义和应用价值。
本文针对无线传感器网络节点定位技术进行了系统研究,提出了一系列解决无线传感器网络节点定位问题的方法,并结合科研及实际需要,设计实现了无线传感器网络定位应用系统。
具体地,论文主要的研究成果和创新有:
(1)无线传感器网络无需测距定位算法的改进研究
对DV-Hop定位算法进行了局部化及定位计算方面的改进。通过对DV-Hop算法进行局部化,提高了定位精度,减少了数据包发送量,并且对于各向异性网络的节点定位具有较强的适应性。针对DV-Hop算法中采用极大似然估计法和三边测量法而引起的运算量大、精度不高、算法可能失效问题,采用模拟方法计算坐标位置,在同样网络参数的条件下,该方法计算量小,并提高了平均定位精度。
(2)基于神经网络的传感器网络节点定位研究
研究将神经网络应用于传感器网络节点定位,提出了基于神经网络的传感器节点定位方法。分析比较了两种适用于定位场景建模的经典前馈神经网络模型:BP网络和RBF网络。从模型、输入信号和误差准则选择角度对传感器网络建模进行了分析,与极大似然估计法相比,神经网络方法由于综合了全局性的定位信息,具有更高的定位精度,并不受非视距和节点功率差异等因素的影响。
(3)无线传感器网络移动节点定位研究
研究将粒子滤波算法应用于移动传感器节点自定位,提出了基于RSSI测量的移动节点自定位设计方案。分析了移动节点运动模型和RSSI测量模型,采用在线顺序迭代得到定位结果,给出了采用粒子滤波实现移动传感器节点自定位的具体方法,对于移动节点定位,粒子滤波算法定位性能优于传统的传感器网络MLE算法和EKF定位跟踪算法。
(4)传感器网络定位在井下安全监控系统中的应用实现
根据煤矿安全生产的需要,设计实现了一种基于无线传感器网络定位的监控系统。该系统采用两级网络结构,采用自主设计的传感器节点硬件平台,设计实现了数据实时传输与路由维护结合的树形路由协议,并根据井下的地形特点和实际需要,设计实现了基于RSSI的井下定位系统。该系统作为无线传感器网络定位的具体应用实例,为以后进一步的应用和研究打下基础。
Wireless Sensor Networks (WSN), which integrates the sensor, micro-electro-mechanism system (MEMS), modern network technology and wireless communication, becomes a hot research topic at the current Information Technology field. The WSN has many advantages such as self-organization, high accuracy measurements, and high fault-toleration, wide coverage et al. Characteristics of low cost, low power, flexible deployment make it more widely applied ranging from the military areas to the abundant civil areas.
Automatic Location of the sensor nodes is a key supporting technology in WSN. The overwhelming reason is that a sensor’s location must be known for its data to be useful. There are still lots of problems in this technology as great affection by circumstances, high algorithm complexity and power-cost, unsuitable for mobile sensor networks, etc. With the rapid development of WSN technology, its applicatons will become wider and wider in the future. Research on location technology of WSN are of significance both theoretical and practical value.
This dissertation deeply studies the node localization technology, and puts forward a series of methods to solve node localization problems in WSN. An application system of WSN localization is developed, which serves for further studying and practical new applications.
The concrete contents of this dissertation are given as below:
(1) Research on improvement of range-free localization in wireless sensor network
The traditional DV-Hop location algorithm is improved from two aspects. Through limiting location information obtained in local area, the location accuracy of DV-Hop algorithm is improved and suitable to anisotropy network. Because of the problems of heavy computation, low precision, algorithm invalidation caused by method of maximum likelihood and trilateration in DV-Hop algorithm, imitation method is adopted to computer node position. Simulation results show that the location method has features of low computational complexity and high location accuracy under the same network parameters.
(2) Research on the localization scheme based on neural network in wireless sensor networks
Neural network is used to estimate node position in wireless sensor networks and the realization steps of localization scheme based on neural network are described. The two classical feed forward neural network models which are suitable for modeling of location, back-propagation (BP), radial basis function (RBF) are analysed and compared in this dissertation. The modeling method using neural network is analyzed from the selection of model, inpute singal and error criterion. The trained neural network can integrate global information of the sensor network as anchor nodes character, nodes deposited, environment, etc. The results of simulation demonstrate that the location scheme can obtain higher accuracy of location estimation, require less anchor nodes and not be affected by NLOS environment and irregularity of anchor nodes radio power.
(3) Research on mobile node localization in wireless sensor network
Particle filter is applied to self-localization of mobile sensor node and the self-localization scheme of mobile sensor node based on RSSI is proposed. Mobile node movement model and RSSI range model are analyzed and online sequential iterative method is used to obtain location result. The dentail concrete steps of mobile sensor node self-localization adopt particle filter is designed. The simulation results show that the location accuracy of particle filter algorithm is better than traditional maximum likelihood estimation (MLE) and extended Kalman filter (EKF) algorithm.
(4) Application implementation of WSN location in mine monitoring system
To meet the needs of safe production in coal mine, a monitoring system based on wireless sensor networks is designed. Two-level network is adopted in the system. We construct communication protocols and the most important one is tree routing protocol which combines the real-time transmission of data with routing maintenance. Considering the topographic features of coal mine and practical needs, RSSI-based location system of wireless sensor networks is designed. As a location system application of WSN, it offers a general development platform for reasearch of the future related problems in WSN.
节点定位技术是无线传感器网络的关键支撑技术之一,节点自身的正确定位是提供监测事件位置信息的前提。目前现有的无线传感器网络节点定位算法普遍存在有受环境影响大、精确度不高、不适用于移动节点定位等问题,随着无线传感器网络技术的不断进步成熟,其应用将会越来越广泛,节点定位技术研究对于传感器网络技术性能提高和实用性保证有重要的理论意义和应用价值。
本文针对无线传感器网络节点定位技术进行了系统研究,提出了一系列解决无线传感器网络节点定位问题的方法,并结合科研及实际需要,设计实现了无线传感器网络定位应用系统。
具体地,论文主要的研究成果和创新有:
(1)无线传感器网络无需测距定位算法的改进研究
对DV-Hop定位算法进行了局部化及定位计算方面的改进。通过对DV-Hop算法进行局部化,提高了定位精度,减少了数据包发送量,并且对于各向异性网络的节点定位具有较强的适应性。针对DV-Hop算法中采用极大似然估计法和三边测量法而引起的运算量大、精度不高、算法可能失效问题,采用模拟方法计算坐标位置,在同样网络参数的条件下,该方法计算量小,并提高了平均定位精度。
(2)基于神经网络的传感器网络节点定位研究
研究将神经网络应用于传感器网络节点定位,提出了基于神经网络的传感器节点定位方法。分析比较了两种适用于定位场景建模的经典前馈神经网络模型:BP网络和RBF网络。从模型、输入信号和误差准则选择角度对传感器网络建模进行了分析,与极大似然估计法相比,神经网络方法由于综合了全局性的定位信息,具有更高的定位精度,并不受非视距和节点功率差异等因素的影响。
(3)无线传感器网络移动节点定位研究
研究将粒子滤波算法应用于移动传感器节点自定位,提出了基于RSSI测量的移动节点自定位设计方案。分析了移动节点运动模型和RSSI测量模型,采用在线顺序迭代得到定位结果,给出了采用粒子滤波实现移动传感器节点自定位的具体方法,对于移动节点定位,粒子滤波算法定位性能优于传统的传感器网络MLE算法和EKF定位跟踪算法。
(4)传感器网络定位在井下安全监控系统中的应用实现
根据煤矿安全生产的需要,设计实现了一种基于无线传感器网络定位的监控系统。该系统采用两级网络结构,采用自主设计的传感器节点硬件平台,设计实现了数据实时传输与路由维护结合的树形路由协议,并根据井下的地形特点和实际需要,设计实现了基于RSSI的井下定位系统。该系统作为无线传感器网络定位的具体应用实例,为以后进一步的应用和研究打下基础。
Wireless Sensor Networks (WSN), which integrates the sensor, micro-electro-mechanism system (MEMS), modern network technology and wireless communication, becomes a hot research topic at the current Information Technology field. The WSN has many advantages such as self-organization, high accuracy measurements, and high fault-toleration, wide coverage et al. Characteristics of low cost, low power, flexible deployment make it more widely applied ranging from the military areas to the abundant civil areas.
Automatic Location of the sensor nodes is a key supporting technology in WSN. The overwhelming reason is that a sensor’s location must be known for its data to be useful. There are still lots of problems in this technology as great affection by circumstances, high algorithm complexity and power-cost, unsuitable for mobile sensor networks, etc. With the rapid development of WSN technology, its applicatons will become wider and wider in the future. Research on location technology of WSN are of significance both theoretical and practical value.
This dissertation deeply studies the node localization technology, and puts forward a series of methods to solve node localization problems in WSN. An application system of WSN localization is developed, which serves for further studying and practical new applications.
The concrete contents of this dissertation are given as below:
(1) Research on improvement of range-free localization in wireless sensor network
The traditional DV-Hop location algorithm is improved from two aspects. Through limiting location information obtained in local area, the location accuracy of DV-Hop algorithm is improved and suitable to anisotropy network. Because of the problems of heavy computation, low precision, algorithm invalidation caused by method of maximum likelihood and trilateration in DV-Hop algorithm, imitation method is adopted to computer node position. Simulation results show that the location method has features of low computational complexity and high location accuracy under the same network parameters.
(2) Research on the localization scheme based on neural network in wireless sensor networks
Neural network is used to estimate node position in wireless sensor networks and the realization steps of localization scheme based on neural network are described. The two classical feed forward neural network models which are suitable for modeling of location, back-propagation (BP), radial basis function (RBF) are analysed and compared in this dissertation. The modeling method using neural network is analyzed from the selection of model, inpute singal and error criterion. The trained neural network can integrate global information of the sensor network as anchor nodes character, nodes deposited, environment, etc. The results of simulation demonstrate that the location scheme can obtain higher accuracy of location estimation, require less anchor nodes and not be affected by NLOS environment and irregularity of anchor nodes radio power.
(3) Research on mobile node localization in wireless sensor network
Particle filter is applied to self-localization of mobile sensor node and the self-localization scheme of mobile sensor node based on RSSI is proposed. Mobile node movement model and RSSI range model are analyzed and online sequential iterative method is used to obtain location result. The dentail concrete steps of mobile sensor node self-localization adopt particle filter is designed. The simulation results show that the location accuracy of particle filter algorithm is better than traditional maximum likelihood estimation (MLE) and extended Kalman filter (EKF) algorithm.
(4) Application implementation of WSN location in mine monitoring system
To meet the needs of safe production in coal mine, a monitoring system based on wireless sensor networks is designed. Two-level network is adopted in the system. We construct communication protocols and the most important one is tree routing protocol which combines the real-time transmission of data with routing maintenance. Considering the topographic features of coal mine and practical needs, RSSI-based location system of wireless sensor networks is designed. As a location system application of WSN, it offers a general development platform for reasearch of the future related problems in WSN.
引文
[1] Akyildiz I., Su W., Sankarasubramaniam Y., Cayirci E, A survey on sensor networks [J]. IEEE Communications Magazine, 2002, 40(8): 102-114
[2] Tilak S., Abu-Ghazaleh N.B., Heinzelman W., A taxonomy of wireless micro-sensor network models [J]. Mobile Computing and Communications Review, 2002, 6(2):1-8
[3] Cullar D., Estrin D., Strvastava M., Overview of sensor network [J]. Computer, 2004, 37(8): 41-49
[4]王福豹,史龙,任丰原.无线传感器网络中的自身定位系统和算法[J].软件学报, 2005, 16(5): 857-868
[5]李建中,高宏.无线传感器网络的研究进展,计算机研究与发展[J], 2008, 45(1): 1-15
[6]孙利民,李建中,陈渝等.无线传感器网络[M],北京:清华大学出版社, 2005.5: 3-26
[7] Warneke B., Last M., Liebowitz B., Pister K.S.J., Smart dust: Communication with a cubic-millimeter computer [J]. IEEE Computer Magazine, 2001, 34(1): 44-51
[8] Arici T., Altunbasak Y., Adaptive sensing for environment monitoring using wireless sensor networks [C]. The IEEE Wireless Communications and Networking Conference (WCNC), Atlanta, GA, 2004, 2347-2352
[9] Mainwaring A., Polastre J., Szewcyzk R., et al., Wireless Sensor Networks for Habitat Monitoring [C]. The 1st ACM Int’l Workshop on Wireless Sensor Networks and Applications (WSNA 2002), Atlanta, Georgia, USA, 2002, 88-97
[10] Delin K. A., Jackson S. P., The sensor Web: A new instrument concept [C]. SPIE Symposium on Integerated Optics, San Jose, CA, 2001, 20-26
[11] Knaian N., A wireless sensor network for smart roadbeds and intelligent transportation systems. M.S. thesis, MIT, Electrical Engineering and Computer Science and the MIT Media Laboratory, Cambridge, MA, 2000.
[12] Brennan S. M, Mielke A. M., Tomey D. C., Radiation detection with distributed sensor networks [J]. Computer, 2004, 37(8): 57-59
[13] Schwiebert L., Gupta S. K. S., Weinmann J., Research challenges in wireless networks of biomedical sensors [C]. The 7th annual international comference on Mobile computing andneworking, Rome, Italy, 2001, 151-165
[14] Meyer S., Rakotonirainy A., A survey of research on context-aware homes [C]. Workshop on Wearable, Invisible, Context-Aware, Ambient, Pervasive and Ubiquitous Computing, Vol.21, Adelaide, Australia, 2003, 159-168
[15] Priyantha N.B., Chakraborty A., Balakrishnan H., The cricket location-support system [C]. 6th Annual Int’l Conf. on Mobile Computing and Networking. Boston: ACM Press, 2000, 32-43
[16] Fontana R. J., Richley E., Barney J. A., Commercialization of an ultra wideband precision asset location system [C]. IEEE conf. on Ultra Wideband Systems and Technologies, Reston, VA, 2003, 369-373
[17] Wade R., Mitchell W.M., Petter F., Ten emerging technologies that will change the world.Technology Review [J], 2003, 106(1):22-49.
[18] Asada G, Dong M, Lin T., et al., Wireless integrated network sensors (WINS) for tactical information systems [C]. In: Proceedings of the 1998 European Solid State Circuits Conference. New York: ACM Press, 1998, 15~20
[19] Edgar H.C., Wireless sensor networks: architectures and protocols [M]. Boca Raton, Florida: CRC Press LLc, 2004:1-40
[20] Kahn J.M., Katz R.H., Pister K.S.J., Next century challenges: mobile networking for smart dust [C]. International Conference on Mobile Computing and Networking (MOBICOM), Seattle, 1999, 271-278
[21] Wong K.D., Physical layer considerations for wireless sensor networks networking [C]. IEEE Int’l Conf. on Sensing and Control, Taipei, 2004, 1202-1206
[22] Demirkol I., Ersoy C., Alagoz, F. MAC protocols for wireless sensor networks: a survey [J]. IEEE Communications Magazine, 2006, 44(4): 115~121
[23] Akkaya K., Younis M.. A survey on routing protocols for wireless sensor networks [J]. Ad Hoc Networks, 2005, 3(3): 325~349
[24] Rahimi M., Shah H, Sukhatme G.S., et al., Studying the feasibility of energy harvesting in a mobile sensor network [C]. The 2003 Int'l Conf. on Robotics & Automation, Vol.1, Taipei, 2003, 19-24
[25] Sinha A., Chandrakasan A., Dynamic power management in wireless sensor networks [J].Design & Test of Computers, 2001, 18(2): 62-74
[26] Boulis A., Srivastava M.B., Node-level energy management for sensor networks in the presence of multiple applications [C]. The 1st IEEE Int,l Conf. on Pervasive Computing and Communications, Dallas-Fort, Worth, Texas, USA, 2003, 41-49
[27] Du X.J., Chen H.H., Security in wireless sensor networks [J]. IEEE Wireless Communications, 2008,15(4): 60-66
[28] Wang Y., Attebury G., Ramamurthy B., A survey of security issues in wireless sensor networks [J]. IEEE Communications Surveys & Tutorials, 2006, 8(2): 2-23
[29] Boyle D., Newe T., Security Protocols for Use with Wireless Sensor Networks: A Survey of Security Architectures [C]. Proceedings of the Third International Conference on Wireless and Mobile Communications (ICWMC'07) Guadeloupe, French Caribbean, 2007, 54-60
[30] Peter D., Deepak G., Prashant S., TSAR: A two tier sensor storage architecture using interval skips graphs [C]. ACM Conference on Embedded Sensor Systems, San Diego, USA, 2005, 39-50
[31] Gaurav M., Peter D., Deepak G., et al., Capsule: An energy-optimized object storage system for memory-constrained sensor devices [C]. ACM Conference on Embedded Sensor Systems, Colorado, USA, 2006, 195-208
[32] Yao Y., Gehrke J., The cougar approach to in-network query processing in sensor networks [J]. SIGMOD Record, 2002, 31(3): 9-18
[33] Abadi D.J., Madden S., Lindner W., REED: Robust, efficient filtering and event dectection in sensor networks [C]. International Conference on Very Large Data Bases(VLDB'05), Trondheim, Norway, 2005, 769-780
[34] Chee-Yee C., Kumar S.P., Sensor networks: evolution, opportunities, and challenges [J]. Proceedings of the IEEE, 2003, 91(8): 1247~1256
[35] Kostrzewski A.A., Wang W., Jannson T.P., WLAN visual sensor networking [C]. Proceedings of the SPIE, 2002, 4787: 174-178
[36] Raghunathan V., Schurgers C., Park S., et al. Energy-aware wireless microsensor networks [J]. IEEE Signal Processing Magazine, 2002, 19(2): 40-50
[37] Hightower J., Borriello G., Location systems for ubiquitous computing [J]. IEEE Computer, 2001, 34(8): 57~66
[38] Harter A., Hopper A., Steggles P., et al., The anatomy of a context-aware application [C]. the Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 99), Seattle, WA, USA: ACM Press, 1999, 59~68
[39] Viani, F., Lizzi, L., Rocca, P., et al., Object tracking through RSSI measurements in wireless sensor networks [J]. Electronics Letters, 2008, 44(10): 653-654
[40] Li D., Wong K., Hu Y.Y., et al., Detection classification and tracking of targets in distributed sensor networks [J]. IEEE Signal Processing Magazine, 2002, 19(2): 17-29
[41] Brooks R.R., Ramamathan P., Sayeed A.M., Distributed target classification and tracking in sensor network [J]. Proc of the IEEE, 2003, 91(8): 1163-1171
[42] Yeow, W.L., Tham C.K., Wong W.C., Energy Efficient Multiple Target Tracking in Wireless Sensor Networks [J]. Vehicular Technology, 2007, 56(2): 918-928
[43] Orpilla M., Palumbo J., Thomas S., et al., Geographically-aware, reliable multicast protocol for tactical sensor networks [C]. 2005 IEEE Military Communications Conference. MILCOM. 2005, 590-595
[44] Hong X., Xu K., Gerla M. Scalable routing protocols for mobile ad hoc networks [J]. IEEE Network magazine, 2002, 16(4): 11-21
[45] Ko Y.B., Vaidya N.H. Location-Aided Routing (LAR) in Mobile Ad Hoc Networks [J]. Wireless Networks, 2000, 6(4): 307-321
[46] Xu Y., Heideman J., Estrin D., Geography-informed Energy Conservation for Ad Hoc Routing [C]. The ACM Int'l Conf. on Mobile Computing and Networking (MobiCom), New York: ACM Press, 2001, 70-84
[47] Bulusu N., Estrin D., Girod L., et al., Scalable Coordination for Wireless Sensor Networks: Self-Configuring Localization Systems [C]. The 6th International Symposium on Communication Theory and Applications (ISCTA 01), Vol.2, Ambleside, Lake District, UK, 2001, 1797-1800
[48] Cerpa A., Estrin D., Ascent: Adaptive self-configuring sensor network topologies [J]. ACM SIGCOMM Computer Communication Review, 2002, 32(1): 62-62
[49]王永才,赵千川,郑大钟.传感器网络自身定位方法的设计与实现[J].计算机工程与应用[J], 2005, 41(13): 4-6
[50]孙学斌,周正.无线传感器网中目标定位算法的研究[J].计算机工程与应用, 2004,40(3): 132-134
[51]马祖长,孙怡宁.无线传感器网络节点的定位算法[J].计算机工程, 2004, 30(7): 13-15
[52] Want R., Hopper A., Falcao V., et al. The active badge location system. ACM Trans. on Information Systems [J], 1992, 10(1): 91-102
[53] Harter A., Hopper A., A distributed location system for the active office [J]. IEEE Network, 1994, 8(1): 62-70.
[54] Doherty L., Algorithms for position and data recovery in wireless sensor networks. M.S. thesis, Berkeley: University of California, 2000
[55] Doherty L., Pister K.S.J., Ghaoui L.E., Convex position estimation in wireless sensor networks [C]. IEEE INFOCOM 2001, Vol.3, Anchorage, Alaska, 2001, 1655-1663
[56] Nicolescu D., Nath B., Ad-Hoc positioning systems (APS) [C]. 2001 IEEE Global Telecommunications Conf., Vol.5, San Antonio, 2001, 2926-2931
[57] Niculescu D., Nath B., DV based positioning in ad hoc networks [J]. Journal of Telecommunication Systems, 2003, 22(1/4): 267-280.
[58] Niculescu D, Nath B. Ad hoc positioning system (APS) using AoA [C]. IEEE INFOCOM 2003, Vol.3, San Francisco, 2003, 1734-1743
[59] Savarese C., Rabaey J.M., Beutel J., Locationing in distributed ad-hoc wireless sensor network [C]. 2001 IEEE Int’l Conf.on Acoustics, Speech, and Signal, Vol.4, Salt Lake, 2001, 2037-2040
[60] Savvides A., Han C-C., Srivastava M.B., Dynamic fine-grained localization in ad-hoc networks of sensors [C]. 7th Annual Int’l Conf. on Mobile Computing and Networking, Rome: ACM Press, 2001, 166-179
[61] Avvides A., Park H., Srivastava M.B., The bits and flops of the N-hop multilateration primitive for node localization problems [C]. 1st ACM Int’l Workshop on Wireless Sensor Networks and Applications, Atlanta, 2002, 112-121
[62] Shang Y., Ruml W., Zhang Y., Fromherz M.P.J., Localization from mere connectivity [C]. 4th ACM Int’l Symp. on Mobile Ad Hoc Networking & Computing, Annapolis, 2003, 201-212.
[63] Bahl P., Padmanabhan V.N., RADAR: An in-building RF-based user location and tracking system [C]. IEEE INFOCOM 2000. Vol.2, Tel Aviv, 2000, 775-784.
[64] Harter A., Hopper A., Steggles P., Ward A., et al., The anatomy of a context-aware application [C]. 5th Annual ACM/IEEE Int’l Conf. on Mobile Computing and Networking. Seattle, 1999, 59-68.
[65] Harter A., Jones A., Hooper A., A new location technique for the active office [J]. IEEE Personal Communications, 1997, 4(5): 42-47
[66] Shang Y., Ruml W., Zhang Y., et al., Localization from mere connectivity [C], ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC), Annapolis: ACM Press , 2003, 201-212
[67] Hightower J., Boriello G., Want R., Spot ON: An indoor 3D location sensing technology based on RF signal strength. Technical Report UW CSE 2000-02-02, Seattle: Department of Computer Science and Engineering, University of Washington, 2000
[68] Welch G., Bishop G., Vicci L., et al., The HiBall tracker: High-Performance wide-area tracking for virtual and augmented environments [C]. In: Proc. of the ACM Symp. on Virtual Reality Software and Technology. London, 1999, 1-11.
[69] Want R., Schilit B.N., Adams N.I., et al., An overview of the ParcTab ubiquitous computing experiment [J]. IEEE Personal Communications, 1995, 2(6):28-43.
[70] Orr R.J., Abowd G.D., The smart floor: A mechanism for natural user identification and tracking [C]. 2000 Conf. on Human Factors in Computing Systems, The Hague, 2000, 275-276.
[71] Pentland A., Machine understanding of human action [C]. 7th Int’l Forum on Frontier of Telecommunication Technology. Tokyo, 1995, 757-764
[72] Bulusu N., Heidemann J., Estrin D., GPS-Less low cost outdoor localization for very small devices [J]. IEEE Personal Communications, 2000, 7(5):28-34.
[73] Hightower J., Boriello G., Location systems for ubiquitous computing [J]. Computer, 2001, 34(8): 57-66.
[74] Girod L., Bychovskiy V., Elson J., et al., Locating tiny sensors in time and space: A case study [C]. 2002 IEEE Int’l Conf. on Computer Design: VLSI in Computers and Processors, Freiburg, 2002, 214-219
[75] Girod L., Estrin D., Robust range estimation using acoustic and multimodal sensing [C]. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems (IROS 01), Vol.3, Maui, 2001,1312-1320
[76] Priyantha N.B., Miu A.K.L., Balakrishnan H., et al., The cricket compass for context-aware mobile applications [C]. 7th Annual Int’l Conf. on Mobile Computing and Networking, Rome, 2001, 1-14
[77] Meguerdichian S., Slijepcevic S., Karayan V., et al., Localized algorithms in wireless ad-hoc networks: Location discovery and sensor exposure [C]. 2nd ACM Int’l Symp. on Mobile Ad Hoc Networking & Computing, Long Beach, 2001, 106-116
[78] Bergamo P., Mazzini G., Localization in sensor networks with fading and mobility [C]. 13th IEEE Int’l Symp. On Personal, Indoor and Mobile Radio Communications, Vol.2, Lisbon, 2002, 750-754
[79] Savarese C., Rabay J., Langendoen K., Robust positioning algorithms for distributed ad-hoc wireless sensor networks [C]. USENIX Technical Annual Conf. Monterey, 2002, 317-327
[80] He T., Huang C.D., Blum B.M., et al., Range-Free localization schemes in large scale sensor networks [C]. 9th Annual Int’l Conf. on Mobile Computing and Networking. San Diego, 2003, 81-95
[81] Rao A., Papadimitriou C., Shenker S., et al., Geographic routing without location information [C]. 9th Annual Int’l Conf. on Mobile computing and Networking. San Diego, 2003, 96-108
[82] Capkun S., Hamdi M., Hubaux J-P., GPS-Free positioning in mobile ad-hoc networks [J]. Cluster Computing, 2002, 5(2): 157-167
[83] Niculescu D., Nath B., Localized positioning in ad hoc networks [C]. 1st IEEE Int’l Workshop on Sensor Network Protocols and Applications. Anchorage: IEEE Communications Societies, 2003, 42-50.
[84] Hightower J., Boriello G., Want R., SpotON: An indoor 3D location sensing technology based on RF signal strength. Technical Report UW CSE 2000-02-02, Seattle: Department of Computer Science and Engineering, University of Washington, 2000.
[85] Hazas M., Ward A., A novel broadband ultrasonic location system [C]. 4th Int’l Conf. on Ubiquitous Computing, Goteborg: Springer-Verlag, 2002, 264-280
[86] Hazas M., Ward A., A high performance privacy-oriented location system [C]. 1st IEEE Int’l Conf. on Pervasive Computing and Communications, Fort Worth: IEEE ComputerSociety, 2003, 216-233
[87] Bulusu N., Estrin D., Heidemann J., Tradeoffs in location support systems: The case for quality-expressive location models for applications [C]. Ubicomp 2001 Workshop on Location Modeling for Applications. Atlanta, 2001, 7-12
[88] Chen P.C., A non-line-of-sight error mitigation algorithm [C]. IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA. USA: IEEE Computer and Communications Societies 1994, 316-321
[89] Wylie M.P. Holtzman J., The non-line of sight problem in mobile location estimation [C]. International Conference on Universal Personal Communications, Cambridge, MA. IEEE Communications Society, 1996, 827-831
[90]夏启军,孙优贤,周春晖.渐消卡尔曼滤波器的最佳自适应算法及其应用自动化学报, 1990,16(3): 210-216
[91] Sage A.P., Husa G.W., Adaptive filtering with unknown prior statistics [C]. Joint Automatic Control Conference, 1969, 760-769
[92] Larsson E.G., Cramer-Rao Bound Analysis of Distributed Positioning in Sensor Networks [J]. IEEE Signal Processing Letters, 2004, 11(3): 334-337
[93] Chang C., Shahai A., Cramer-Rao type bounds for localization [J]. Eurasip Journal on Applied Signal Processing, 2006, 2006(1): 1-13
[94] Chang C., Shahai A., Estimation bounds for localization [C]. First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, Santa Clara, CA, 2004, 415-424
[95] Patwari N., Hero III A.O., Perkins M., et al., Relative location estimation in wireless sensor networks [J]. IEEE Transactions on Signal Processing, 2003, 51(8): 2137-2148
[96] Sundaram N, Ramanathan P. Connectivity based location estimation scheme for wireless ad hoc networks [C]. 2002 IEEE Global Telecommunications Conf. Vol.1, Taipei: IEEE Communications Society, 2002, 143-147
[97] Sichitiu M.L., Veerarittiphan C., Simple, accurate time synchronization for wireless sensor networks [C]. IEEE Wireless Communications and Networking Conference (WCNC’03). Piscataway, NJ, US: IEEE Press, 2003, 1266-1273
[98] Arias J., Lazaro J., Astarloa A., Location algorithm for wireless sensor networks inindustrial applications [C]. IEEE International Conference on Industrial Technology (ICIT), Vol.2, Hammamet (Túnez), 2004, 757-762
[99] Hagan M.T., Demuth H.B., Beale M.H., Neural Network Design [M]. Beijing: China Machine Press, 2002
[100] Simon Haykin著,叶世伟,史忠植译.神经网络原理[M].北京:机械工业出版社.2004, 1:89-93
[101] Fukuda, T., Shibata, T., Theory and applications of neural networks for industrial control systems [J]. Industrial Electronics, 1992, 39(6): 472-489
[102] Wu Z.C., Research on remote sensing image classification using neural network based on rough sets [C]. Info-tech and Info-net, Vol.1, Beijing, 2001, 279-284
[103] Ghaffari M., Genaidy A.M., A Neural Network Model for Human Performance Optimization in the Workplace [C]. 2nd International IEEE EMBS Conference on Neural Engineering, 2005, 648-651
[104] Zaheeruddin Z., Garima G., Application of Artificial Neural Networks for Prediction of Human Work Efficiency in Noisy Environment [C]. Intelligent Agents, Web Technologies and Internet Commerce, Vol.2, 2005, 842-846
[105] Chowdhury F.N., Wahi P., Raina R., et al., A survey of neural networks applications in automatic control [C]. Proceedings of the 33rd Southeastern Symposium on System Theory, 2001, 349-353
[106] Hornik K.M., Stinchcombe M., White H., Multilayer feedforward networks are universal approximators [J]. Neural Networks, 1989, 2(5):359-366
[107] Rumelhart D.E., Hinton G.E. and Williams R.J., Learning representations by back-propagationg errors [J], Nature, 1986, 323: 533-536
[108] Carpenter G.A. Grossberg S., A massively parallel architecture for a self-organizing neural pattern recognition machine [J]. Computre Vision, Graphics, and Image Processing, 1987, 37: 54-115
[109] Hopfield J.J., Neural networks and physical systems with emergent collective computational properties [J]. Proceeding of National academy of sciences, 1982, 79: 2554-2558
[110] Kohonen T., The Self-organizing Map [J]. Neurocomputing, 1998, 21(1):1-6
[111] Vogl T.P., Mangis J.K., Accelerating the convergence of the backpropagation method [J]. Biological Cybernetics, 1988, 59(9): 256-264
[112] Jacobs R.A., Increased rates of convergence through learning rate adaptation [J]. Neural Networks, 1988, 1:295-307
[113] Tollenaere T., SuperSAB: fast adaptive back propagation with good scaling properties [J]. Neural Networks, 1990, 3(5): 561-573
[114] Rigler A.K., Irvine J.M., Vogl T.P., Rescaling of variables in back propagation learning [J]. Neural Networks, 1991, 4(2): 225-229
[115] Barnard E., Optimization for training neural nets [J]. IEEE Transactions on neural networks, 1992, 3(2): 232-240
[116] Battiti R., First and second order methods for learning: between steepest descent and Newton’s method [J]. Neural computation, 1992, 4(2): 141-166
[117] Charalambous C., Conjugate gradient algorithm for efficient training of artificial neural networks [J]. IEEE proceeding, 1992, 139(3): 301-310
[118] Shanno D.F., Recent advances in numerical techniques for large-scale optimization [C]. Neural networks for control, Cambridge MA: MIT Press, 1990, 171-178
[119] Hagan M.T., Menhaj Menhaj M., Training feedforward networks with the Marquardt algorithm [J]. IEEE Tansactions on Neural Networks, 1994, 5(6): 989-993
[120] Powell M.J.D., Radial basis functions for multivariable interpolation: A review [C]. IMA Conference on Algorithms for Approximation of Functions and Data, Oxford, England: Clarendon Press, 1987, 143-167
[121] Broomhead and D.S., Lowe D., Multivariable functional interpolation and adaptive networks [J]. Complex Systems, 1988, 2: 321-355
[122] Moody J.E., Darken C.J., Fast learning in networks of locally-tuned processing units [J]. neural Computation, 1989, 1: 281-294
[123] Jackson I.R.H., Radial basis functions: a survey and new results [C]. The Mathematics of Surfaces III, 1989, 115-133
[124] Jang J.S.R., Sun C.T., Mizutani E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence [M]. Prentice Hall, 1996
[125] Margare H.Dunham著,郭崇慧,田风占,靳晓明等译.数据挖掘教程[M].北京:清华大学出版社. 2005, 5: 120-122
[126] Karayiannis N.B., Gradient Descent Learning of Radial Basis Neural Network [C]. International Conference on Neural Networks, 1997, 3: 1815-1820
[127] QingNian Zhang Q.N., He X.Y., Liu J.Q., RBF Network Based on Genetic Algorithm Optimization for Nonlinear Time Series Prediction [C]. Proceedings of the 2003 International Symposium on Circuits and Systems, 2003, 5: 693-696.
[128] Abdella M., Marwala T., The Use of Genetic Algorithms and Neural Networks to Approximate Missing Data in Database [C]. IEEE 3rd International Conference on Computational Cybernetics, 2005, 207-212
[129] Chng E., Yang H., Bos S., Orthogonal least-squares learning algorithm with local adaptation process for the radial basis function networks [J]. Signal Processing Letters, 1996, 3(8): 253-255.
[130] Poggio T., Girosi F., Networks for Approximation and Learning [J]. Proceedings of the IEEE, 1990, 78(9): 1481-1497
[131] Bianchini M., Frasconi P., Cori M.; Learning without Local Minima in Radial Basis Function Network [J]. IEEE Transactions on Neural Network, 1995, 6(3): 749-756.
[132]何玉彬,李新忠.神经网络控制技术及其应用[M].北京:科学出版社, 2000
[133]邓小龙.基于粒子滤波的目标跟踪与系统辨识方法的研究, [博士学位论文],上海交通大学,2005
[134]李安平.复杂环境下的视频目标跟踪算法研究, [博士学位论文],上海交通大学, 2006
[135]张尧庭.贝叶斯统计推断[M].北京:科学出版社, 1991
[136] Kalman R.E., A new approach to linear filtering and prediction and prediction problems [J], Trans. ASME, Journal of basic Engineering, 1960, 82D(1): 35-45
[137] Jazwinski AH, Stochastic processes and filtering theory [M], New York, Academic Press, 1970
[138] Wan E.A., Van Der Merwe R, The unscented Kalman filter for nonlinear estimation [C], proceeding of the Symposium on Adaptive Systems for Signal Processing, Communication and control, 2000, 153-158
[139] Gordon N.J., Salmond D.J., Smith A.F., Novel approach to nonlinear/non Gaussian Bayesian state estimation [J]. IEE Proceedings-F (0143-7070), 1993, 140(2):107-113
[140] Liu J.S., Chen R., Sequential Monte Carlo methods for dynamic system [J], Journal of the American Statistical Association, 1998, 93(443): 1032-1044
[141] Kitagawa G., Monte carlo filter and smoother for Non-Gaussian nonlinear state space models [J], Journal of computation and graphical statistics, 1996, 5(1):1-25
[142] Doucet A., On sequential simulation-based methods for Bayesian filtering. Technical Report CUED-F-ENG-TR310, Dept. of. Engineering, University of Cambridge, 1998
[143] Crisan D., Doucet A., A survey of convergence results on particle filtering methods for practitioners [J], IEEE Trans. Signal Processing, 2002, 5(3): 738-746
[144] Arulampalam M.S., Maskell S., Gordon N., et al., A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J], IEEE Trans. on signal Processing, 2002, 50(2): 174-188
[145] Andrieu C., Djuric P., Doucet A., Model selection by MCMC computation [J], Signal processing, 2001, 81(1): 19-37
[146] Gilks W., Berzuini C., Following a moving target-Monte Carlo inference for Bayesian dynamic models [J]. Journal of royal Statistic society B, 2004, 63(1): 127-146
[147] Andrieu C., de Freitas N., Doucet A., et al., An introduction to MCMC for machine learning [J]. Machine Learning (0885-6125), 2003, 50(1/2): 5-43
[148] Chen R., Liu J.S., Predictive updating methods with application to Bayesian classification [J]. Journal of the Royal Statistical Society B, 1996, 58: 397-415
[149] Tanizaki H., Mariano R.S., Prediction filtering and smoothing in non-linear and non-normal cases using monte carlo integration [J]. Journal of applied econometrics, 1994, 9(2): 163-179
[150] de Freitas J.F.G., Niranjan M., Gee A.H., et al, Sequential monte carlo methods to train neural network models [J]. Neural Computation, 2000,12(4): 955-993
[151] van der Merwe R., de Freitas N., Doucet A., et al, The unscented particle filter, Technical Report CUED/F-INFENG-TR380, Cambridge University Engineering Department, 2000
[152] Pahlavan K., Levesque A., Wireless Information Networks, NewYork, John Wiley & Sons, 1995, 127-135
[153] Hashemi H., The indoor radio propagation channel [J]. Proc. IEEE, 1993, 81(7): 943–968
[154] Patwari N., O’Dea R.J., Wang Y., Relative location in wireless networks [C]. IEEE VTC,vol. 2, 2001, 1149-1153
[155] Coulson A.J., Williamson A.G., Vaughan R.G., A statistical basis for lognormal shadowing effects in multipath fading channels [J]. IEEE Trans. Veh. Technol., 1998, 46(4): 494–502
[156]赵延明,高军,杨国庆等.煤矿安全监控系统的现状与发展[J].煤矿机电,2007, (3): 39-41
[157]张力.我国煤矿安全科技现状、问题及对策建议[J].中国煤炭, 2008, 34(7): 95-96
[158] Wu J. X., Ding Y., Jiang X. Z., Research on Integration of Coal Mine Monitoring and Control System with Field Bus Control System[J]. Journal of China University of Mining and Technology, 2006, 16(1): 82-85
[159] Song D. D., Ren Z. H., Gu Y. X., Design and Implement of An Intelligent Coal Mine Monitoring System[C]. Piscataway, NJ, USA: IEEE, Electronic Measurement and Instruments. (ICEMI '07), 2007, 849-852
[160] Li M., Liu Y., Underground Structure Monitoring with Wireless Sensor Networks[C]. Information Processing in Sensor Networks (IPSN’07), New York, USA: Association for Computing Machinery, 2007, 69-78
[161] Sohrabi K, Gao J., Ailawadhi V., et al., Protocols for self-organization of a wireless sensor network [J]. IEEE Personal Communications, 2000, 7(5): 16-27.
[162]唐勇,周明天,张欣.无线传感器网络路由协议研究进展[J].软件学报, 2006, 17(3): 410-421
[2] Tilak S., Abu-Ghazaleh N.B., Heinzelman W., A taxonomy of wireless micro-sensor network models [J]. Mobile Computing and Communications Review, 2002, 6(2):1-8
[3] Cullar D., Estrin D., Strvastava M., Overview of sensor network [J]. Computer, 2004, 37(8): 41-49
[4]王福豹,史龙,任丰原.无线传感器网络中的自身定位系统和算法[J].软件学报, 2005, 16(5): 857-868
[5]李建中,高宏.无线传感器网络的研究进展,计算机研究与发展[J], 2008, 45(1): 1-15
[6]孙利民,李建中,陈渝等.无线传感器网络[M],北京:清华大学出版社, 2005.5: 3-26
[7] Warneke B., Last M., Liebowitz B., Pister K.S.J., Smart dust: Communication with a cubic-millimeter computer [J]. IEEE Computer Magazine, 2001, 34(1): 44-51
[8] Arici T., Altunbasak Y., Adaptive sensing for environment monitoring using wireless sensor networks [C]. The IEEE Wireless Communications and Networking Conference (WCNC), Atlanta, GA, 2004, 2347-2352
[9] Mainwaring A., Polastre J., Szewcyzk R., et al., Wireless Sensor Networks for Habitat Monitoring [C]. The 1st ACM Int’l Workshop on Wireless Sensor Networks and Applications (WSNA 2002), Atlanta, Georgia, USA, 2002, 88-97
[10] Delin K. A., Jackson S. P., The sensor Web: A new instrument concept [C]. SPIE Symposium on Integerated Optics, San Jose, CA, 2001, 20-26
[11] Knaian N., A wireless sensor network for smart roadbeds and intelligent transportation systems. M.S. thesis, MIT, Electrical Engineering and Computer Science and the MIT Media Laboratory, Cambridge, MA, 2000.
[12] Brennan S. M, Mielke A. M., Tomey D. C., Radiation detection with distributed sensor networks [J]. Computer, 2004, 37(8): 57-59
[13] Schwiebert L., Gupta S. K. S., Weinmann J., Research challenges in wireless networks of biomedical sensors [C]. The 7th annual international comference on Mobile computing andneworking, Rome, Italy, 2001, 151-165
[14] Meyer S., Rakotonirainy A., A survey of research on context-aware homes [C]. Workshop on Wearable, Invisible, Context-Aware, Ambient, Pervasive and Ubiquitous Computing, Vol.21, Adelaide, Australia, 2003, 159-168
[15] Priyantha N.B., Chakraborty A., Balakrishnan H., The cricket location-support system [C]. 6th Annual Int’l Conf. on Mobile Computing and Networking. Boston: ACM Press, 2000, 32-43
[16] Fontana R. J., Richley E., Barney J. A., Commercialization of an ultra wideband precision asset location system [C]. IEEE conf. on Ultra Wideband Systems and Technologies, Reston, VA, 2003, 369-373
[17] Wade R., Mitchell W.M., Petter F., Ten emerging technologies that will change the world.Technology Review [J], 2003, 106(1):22-49.
[18] Asada G, Dong M, Lin T., et al., Wireless integrated network sensors (WINS) for tactical information systems [C]. In: Proceedings of the 1998 European Solid State Circuits Conference. New York: ACM Press, 1998, 15~20
[19] Edgar H.C., Wireless sensor networks: architectures and protocols [M]. Boca Raton, Florida: CRC Press LLc, 2004:1-40
[20] Kahn J.M., Katz R.H., Pister K.S.J., Next century challenges: mobile networking for smart dust [C]. International Conference on Mobile Computing and Networking (MOBICOM), Seattle, 1999, 271-278
[21] Wong K.D., Physical layer considerations for wireless sensor networks networking [C]. IEEE Int’l Conf. on Sensing and Control, Taipei, 2004, 1202-1206
[22] Demirkol I., Ersoy C., Alagoz, F. MAC protocols for wireless sensor networks: a survey [J]. IEEE Communications Magazine, 2006, 44(4): 115~121
[23] Akkaya K., Younis M.. A survey on routing protocols for wireless sensor networks [J]. Ad Hoc Networks, 2005, 3(3): 325~349
[24] Rahimi M., Shah H, Sukhatme G.S., et al., Studying the feasibility of energy harvesting in a mobile sensor network [C]. The 2003 Int'l Conf. on Robotics & Automation, Vol.1, Taipei, 2003, 19-24
[25] Sinha A., Chandrakasan A., Dynamic power management in wireless sensor networks [J].Design & Test of Computers, 2001, 18(2): 62-74
[26] Boulis A., Srivastava M.B., Node-level energy management for sensor networks in the presence of multiple applications [C]. The 1st IEEE Int,l Conf. on Pervasive Computing and Communications, Dallas-Fort, Worth, Texas, USA, 2003, 41-49
[27] Du X.J., Chen H.H., Security in wireless sensor networks [J]. IEEE Wireless Communications, 2008,15(4): 60-66
[28] Wang Y., Attebury G., Ramamurthy B., A survey of security issues in wireless sensor networks [J]. IEEE Communications Surveys & Tutorials, 2006, 8(2): 2-23
[29] Boyle D., Newe T., Security Protocols for Use with Wireless Sensor Networks: A Survey of Security Architectures [C]. Proceedings of the Third International Conference on Wireless and Mobile Communications (ICWMC'07) Guadeloupe, French Caribbean, 2007, 54-60
[30] Peter D., Deepak G., Prashant S., TSAR: A two tier sensor storage architecture using interval skips graphs [C]. ACM Conference on Embedded Sensor Systems, San Diego, USA, 2005, 39-50
[31] Gaurav M., Peter D., Deepak G., et al., Capsule: An energy-optimized object storage system for memory-constrained sensor devices [C]. ACM Conference on Embedded Sensor Systems, Colorado, USA, 2006, 195-208
[32] Yao Y., Gehrke J., The cougar approach to in-network query processing in sensor networks [J]. SIGMOD Record, 2002, 31(3): 9-18
[33] Abadi D.J., Madden S., Lindner W., REED: Robust, efficient filtering and event dectection in sensor networks [C]. International Conference on Very Large Data Bases(VLDB'05), Trondheim, Norway, 2005, 769-780
[34] Chee-Yee C., Kumar S.P., Sensor networks: evolution, opportunities, and challenges [J]. Proceedings of the IEEE, 2003, 91(8): 1247~1256
[35] Kostrzewski A.A., Wang W., Jannson T.P., WLAN visual sensor networking [C]. Proceedings of the SPIE, 2002, 4787: 174-178
[36] Raghunathan V., Schurgers C., Park S., et al. Energy-aware wireless microsensor networks [J]. IEEE Signal Processing Magazine, 2002, 19(2): 40-50
[37] Hightower J., Borriello G., Location systems for ubiquitous computing [J]. IEEE Computer, 2001, 34(8): 57~66
[38] Harter A., Hopper A., Steggles P., et al., The anatomy of a context-aware application [C]. the Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 99), Seattle, WA, USA: ACM Press, 1999, 59~68
[39] Viani, F., Lizzi, L., Rocca, P., et al., Object tracking through RSSI measurements in wireless sensor networks [J]. Electronics Letters, 2008, 44(10): 653-654
[40] Li D., Wong K., Hu Y.Y., et al., Detection classification and tracking of targets in distributed sensor networks [J]. IEEE Signal Processing Magazine, 2002, 19(2): 17-29
[41] Brooks R.R., Ramamathan P., Sayeed A.M., Distributed target classification and tracking in sensor network [J]. Proc of the IEEE, 2003, 91(8): 1163-1171
[42] Yeow, W.L., Tham C.K., Wong W.C., Energy Efficient Multiple Target Tracking in Wireless Sensor Networks [J]. Vehicular Technology, 2007, 56(2): 918-928
[43] Orpilla M., Palumbo J., Thomas S., et al., Geographically-aware, reliable multicast protocol for tactical sensor networks [C]. 2005 IEEE Military Communications Conference. MILCOM. 2005, 590-595
[44] Hong X., Xu K., Gerla M. Scalable routing protocols for mobile ad hoc networks [J]. IEEE Network magazine, 2002, 16(4): 11-21
[45] Ko Y.B., Vaidya N.H. Location-Aided Routing (LAR) in Mobile Ad Hoc Networks [J]. Wireless Networks, 2000, 6(4): 307-321
[46] Xu Y., Heideman J., Estrin D., Geography-informed Energy Conservation for Ad Hoc Routing [C]. The ACM Int'l Conf. on Mobile Computing and Networking (MobiCom), New York: ACM Press, 2001, 70-84
[47] Bulusu N., Estrin D., Girod L., et al., Scalable Coordination for Wireless Sensor Networks: Self-Configuring Localization Systems [C]. The 6th International Symposium on Communication Theory and Applications (ISCTA 01), Vol.2, Ambleside, Lake District, UK, 2001, 1797-1800
[48] Cerpa A., Estrin D., Ascent: Adaptive self-configuring sensor network topologies [J]. ACM SIGCOMM Computer Communication Review, 2002, 32(1): 62-62
[49]王永才,赵千川,郑大钟.传感器网络自身定位方法的设计与实现[J].计算机工程与应用[J], 2005, 41(13): 4-6
[50]孙学斌,周正.无线传感器网中目标定位算法的研究[J].计算机工程与应用, 2004,40(3): 132-134
[51]马祖长,孙怡宁.无线传感器网络节点的定位算法[J].计算机工程, 2004, 30(7): 13-15
[52] Want R., Hopper A., Falcao V., et al. The active badge location system. ACM Trans. on Information Systems [J], 1992, 10(1): 91-102
[53] Harter A., Hopper A., A distributed location system for the active office [J]. IEEE Network, 1994, 8(1): 62-70.
[54] Doherty L., Algorithms for position and data recovery in wireless sensor networks. M.S. thesis, Berkeley: University of California, 2000
[55] Doherty L., Pister K.S.J., Ghaoui L.E., Convex position estimation in wireless sensor networks [C]. IEEE INFOCOM 2001, Vol.3, Anchorage, Alaska, 2001, 1655-1663
[56] Nicolescu D., Nath B., Ad-Hoc positioning systems (APS) [C]. 2001 IEEE Global Telecommunications Conf., Vol.5, San Antonio, 2001, 2926-2931
[57] Niculescu D., Nath B., DV based positioning in ad hoc networks [J]. Journal of Telecommunication Systems, 2003, 22(1/4): 267-280.
[58] Niculescu D, Nath B. Ad hoc positioning system (APS) using AoA [C]. IEEE INFOCOM 2003, Vol.3, San Francisco, 2003, 1734-1743
[59] Savarese C., Rabaey J.M., Beutel J., Locationing in distributed ad-hoc wireless sensor network [C]. 2001 IEEE Int’l Conf.on Acoustics, Speech, and Signal, Vol.4, Salt Lake, 2001, 2037-2040
[60] Savvides A., Han C-C., Srivastava M.B., Dynamic fine-grained localization in ad-hoc networks of sensors [C]. 7th Annual Int’l Conf. on Mobile Computing and Networking, Rome: ACM Press, 2001, 166-179
[61] Avvides A., Park H., Srivastava M.B., The bits and flops of the N-hop multilateration primitive for node localization problems [C]. 1st ACM Int’l Workshop on Wireless Sensor Networks and Applications, Atlanta, 2002, 112-121
[62] Shang Y., Ruml W., Zhang Y., Fromherz M.P.J., Localization from mere connectivity [C]. 4th ACM Int’l Symp. on Mobile Ad Hoc Networking & Computing, Annapolis, 2003, 201-212.
[63] Bahl P., Padmanabhan V.N., RADAR: An in-building RF-based user location and tracking system [C]. IEEE INFOCOM 2000. Vol.2, Tel Aviv, 2000, 775-784.
[64] Harter A., Hopper A., Steggles P., Ward A., et al., The anatomy of a context-aware application [C]. 5th Annual ACM/IEEE Int’l Conf. on Mobile Computing and Networking. Seattle, 1999, 59-68.
[65] Harter A., Jones A., Hooper A., A new location technique for the active office [J]. IEEE Personal Communications, 1997, 4(5): 42-47
[66] Shang Y., Ruml W., Zhang Y., et al., Localization from mere connectivity [C], ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC), Annapolis: ACM Press , 2003, 201-212
[67] Hightower J., Boriello G., Want R., Spot ON: An indoor 3D location sensing technology based on RF signal strength. Technical Report UW CSE 2000-02-02, Seattle: Department of Computer Science and Engineering, University of Washington, 2000
[68] Welch G., Bishop G., Vicci L., et al., The HiBall tracker: High-Performance wide-area tracking for virtual and augmented environments [C]. In: Proc. of the ACM Symp. on Virtual Reality Software and Technology. London, 1999, 1-11.
[69] Want R., Schilit B.N., Adams N.I., et al., An overview of the ParcTab ubiquitous computing experiment [J]. IEEE Personal Communications, 1995, 2(6):28-43.
[70] Orr R.J., Abowd G.D., The smart floor: A mechanism for natural user identification and tracking [C]. 2000 Conf. on Human Factors in Computing Systems, The Hague, 2000, 275-276.
[71] Pentland A., Machine understanding of human action [C]. 7th Int’l Forum on Frontier of Telecommunication Technology. Tokyo, 1995, 757-764
[72] Bulusu N., Heidemann J., Estrin D., GPS-Less low cost outdoor localization for very small devices [J]. IEEE Personal Communications, 2000, 7(5):28-34.
[73] Hightower J., Boriello G., Location systems for ubiquitous computing [J]. Computer, 2001, 34(8): 57-66.
[74] Girod L., Bychovskiy V., Elson J., et al., Locating tiny sensors in time and space: A case study [C]. 2002 IEEE Int’l Conf. on Computer Design: VLSI in Computers and Processors, Freiburg, 2002, 214-219
[75] Girod L., Estrin D., Robust range estimation using acoustic and multimodal sensing [C]. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems (IROS 01), Vol.3, Maui, 2001,1312-1320
[76] Priyantha N.B., Miu A.K.L., Balakrishnan H., et al., The cricket compass for context-aware mobile applications [C]. 7th Annual Int’l Conf. on Mobile Computing and Networking, Rome, 2001, 1-14
[77] Meguerdichian S., Slijepcevic S., Karayan V., et al., Localized algorithms in wireless ad-hoc networks: Location discovery and sensor exposure [C]. 2nd ACM Int’l Symp. on Mobile Ad Hoc Networking & Computing, Long Beach, 2001, 106-116
[78] Bergamo P., Mazzini G., Localization in sensor networks with fading and mobility [C]. 13th IEEE Int’l Symp. On Personal, Indoor and Mobile Radio Communications, Vol.2, Lisbon, 2002, 750-754
[79] Savarese C., Rabay J., Langendoen K., Robust positioning algorithms for distributed ad-hoc wireless sensor networks [C]. USENIX Technical Annual Conf. Monterey, 2002, 317-327
[80] He T., Huang C.D., Blum B.M., et al., Range-Free localization schemes in large scale sensor networks [C]. 9th Annual Int’l Conf. on Mobile Computing and Networking. San Diego, 2003, 81-95
[81] Rao A., Papadimitriou C., Shenker S., et al., Geographic routing without location information [C]. 9th Annual Int’l Conf. on Mobile computing and Networking. San Diego, 2003, 96-108
[82] Capkun S., Hamdi M., Hubaux J-P., GPS-Free positioning in mobile ad-hoc networks [J]. Cluster Computing, 2002, 5(2): 157-167
[83] Niculescu D., Nath B., Localized positioning in ad hoc networks [C]. 1st IEEE Int’l Workshop on Sensor Network Protocols and Applications. Anchorage: IEEE Communications Societies, 2003, 42-50.
[84] Hightower J., Boriello G., Want R., SpotON: An indoor 3D location sensing technology based on RF signal strength. Technical Report UW CSE 2000-02-02, Seattle: Department of Computer Science and Engineering, University of Washington, 2000.
[85] Hazas M., Ward A., A novel broadband ultrasonic location system [C]. 4th Int’l Conf. on Ubiquitous Computing, Goteborg: Springer-Verlag, 2002, 264-280
[86] Hazas M., Ward A., A high performance privacy-oriented location system [C]. 1st IEEE Int’l Conf. on Pervasive Computing and Communications, Fort Worth: IEEE ComputerSociety, 2003, 216-233
[87] Bulusu N., Estrin D., Heidemann J., Tradeoffs in location support systems: The case for quality-expressive location models for applications [C]. Ubicomp 2001 Workshop on Location Modeling for Applications. Atlanta, 2001, 7-12
[88] Chen P.C., A non-line-of-sight error mitigation algorithm [C]. IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA. USA: IEEE Computer and Communications Societies 1994, 316-321
[89] Wylie M.P. Holtzman J., The non-line of sight problem in mobile location estimation [C]. International Conference on Universal Personal Communications, Cambridge, MA. IEEE Communications Society, 1996, 827-831
[90]夏启军,孙优贤,周春晖.渐消卡尔曼滤波器的最佳自适应算法及其应用自动化学报, 1990,16(3): 210-216
[91] Sage A.P., Husa G.W., Adaptive filtering with unknown prior statistics [C]. Joint Automatic Control Conference, 1969, 760-769
[92] Larsson E.G., Cramer-Rao Bound Analysis of Distributed Positioning in Sensor Networks [J]. IEEE Signal Processing Letters, 2004, 11(3): 334-337
[93] Chang C., Shahai A., Cramer-Rao type bounds for localization [J]. Eurasip Journal on Applied Signal Processing, 2006, 2006(1): 1-13
[94] Chang C., Shahai A., Estimation bounds for localization [C]. First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, Santa Clara, CA, 2004, 415-424
[95] Patwari N., Hero III A.O., Perkins M., et al., Relative location estimation in wireless sensor networks [J]. IEEE Transactions on Signal Processing, 2003, 51(8): 2137-2148
[96] Sundaram N, Ramanathan P. Connectivity based location estimation scheme for wireless ad hoc networks [C]. 2002 IEEE Global Telecommunications Conf. Vol.1, Taipei: IEEE Communications Society, 2002, 143-147
[97] Sichitiu M.L., Veerarittiphan C., Simple, accurate time synchronization for wireless sensor networks [C]. IEEE Wireless Communications and Networking Conference (WCNC’03). Piscataway, NJ, US: IEEE Press, 2003, 1266-1273
[98] Arias J., Lazaro J., Astarloa A., Location algorithm for wireless sensor networks inindustrial applications [C]. IEEE International Conference on Industrial Technology (ICIT), Vol.2, Hammamet (Túnez), 2004, 757-762
[99] Hagan M.T., Demuth H.B., Beale M.H., Neural Network Design [M]. Beijing: China Machine Press, 2002
[100] Simon Haykin著,叶世伟,史忠植译.神经网络原理[M].北京:机械工业出版社.2004, 1:89-93
[101] Fukuda, T., Shibata, T., Theory and applications of neural networks for industrial control systems [J]. Industrial Electronics, 1992, 39(6): 472-489
[102] Wu Z.C., Research on remote sensing image classification using neural network based on rough sets [C]. Info-tech and Info-net, Vol.1, Beijing, 2001, 279-284
[103] Ghaffari M., Genaidy A.M., A Neural Network Model for Human Performance Optimization in the Workplace [C]. 2nd International IEEE EMBS Conference on Neural Engineering, 2005, 648-651
[104] Zaheeruddin Z., Garima G., Application of Artificial Neural Networks for Prediction of Human Work Efficiency in Noisy Environment [C]. Intelligent Agents, Web Technologies and Internet Commerce, Vol.2, 2005, 842-846
[105] Chowdhury F.N., Wahi P., Raina R., et al., A survey of neural networks applications in automatic control [C]. Proceedings of the 33rd Southeastern Symposium on System Theory, 2001, 349-353
[106] Hornik K.M., Stinchcombe M., White H., Multilayer feedforward networks are universal approximators [J]. Neural Networks, 1989, 2(5):359-366
[107] Rumelhart D.E., Hinton G.E. and Williams R.J., Learning representations by back-propagationg errors [J], Nature, 1986, 323: 533-536
[108] Carpenter G.A. Grossberg S., A massively parallel architecture for a self-organizing neural pattern recognition machine [J]. Computre Vision, Graphics, and Image Processing, 1987, 37: 54-115
[109] Hopfield J.J., Neural networks and physical systems with emergent collective computational properties [J]. Proceeding of National academy of sciences, 1982, 79: 2554-2558
[110] Kohonen T., The Self-organizing Map [J]. Neurocomputing, 1998, 21(1):1-6
[111] Vogl T.P., Mangis J.K., Accelerating the convergence of the backpropagation method [J]. Biological Cybernetics, 1988, 59(9): 256-264
[112] Jacobs R.A., Increased rates of convergence through learning rate adaptation [J]. Neural Networks, 1988, 1:295-307
[113] Tollenaere T., SuperSAB: fast adaptive back propagation with good scaling properties [J]. Neural Networks, 1990, 3(5): 561-573
[114] Rigler A.K., Irvine J.M., Vogl T.P., Rescaling of variables in back propagation learning [J]. Neural Networks, 1991, 4(2): 225-229
[115] Barnard E., Optimization for training neural nets [J]. IEEE Transactions on neural networks, 1992, 3(2): 232-240
[116] Battiti R., First and second order methods for learning: between steepest descent and Newton’s method [J]. Neural computation, 1992, 4(2): 141-166
[117] Charalambous C., Conjugate gradient algorithm for efficient training of artificial neural networks [J]. IEEE proceeding, 1992, 139(3): 301-310
[118] Shanno D.F., Recent advances in numerical techniques for large-scale optimization [C]. Neural networks for control, Cambridge MA: MIT Press, 1990, 171-178
[119] Hagan M.T., Menhaj Menhaj M., Training feedforward networks with the Marquardt algorithm [J]. IEEE Tansactions on Neural Networks, 1994, 5(6): 989-993
[120] Powell M.J.D., Radial basis functions for multivariable interpolation: A review [C]. IMA Conference on Algorithms for Approximation of Functions and Data, Oxford, England: Clarendon Press, 1987, 143-167
[121] Broomhead and D.S., Lowe D., Multivariable functional interpolation and adaptive networks [J]. Complex Systems, 1988, 2: 321-355
[122] Moody J.E., Darken C.J., Fast learning in networks of locally-tuned processing units [J]. neural Computation, 1989, 1: 281-294
[123] Jackson I.R.H., Radial basis functions: a survey and new results [C]. The Mathematics of Surfaces III, 1989, 115-133
[124] Jang J.S.R., Sun C.T., Mizutani E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence [M]. Prentice Hall, 1996
[125] Margare H.Dunham著,郭崇慧,田风占,靳晓明等译.数据挖掘教程[M].北京:清华大学出版社. 2005, 5: 120-122
[126] Karayiannis N.B., Gradient Descent Learning of Radial Basis Neural Network [C]. International Conference on Neural Networks, 1997, 3: 1815-1820
[127] QingNian Zhang Q.N., He X.Y., Liu J.Q., RBF Network Based on Genetic Algorithm Optimization for Nonlinear Time Series Prediction [C]. Proceedings of the 2003 International Symposium on Circuits and Systems, 2003, 5: 693-696.
[128] Abdella M., Marwala T., The Use of Genetic Algorithms and Neural Networks to Approximate Missing Data in Database [C]. IEEE 3rd International Conference on Computational Cybernetics, 2005, 207-212
[129] Chng E., Yang H., Bos S., Orthogonal least-squares learning algorithm with local adaptation process for the radial basis function networks [J]. Signal Processing Letters, 1996, 3(8): 253-255.
[130] Poggio T., Girosi F., Networks for Approximation and Learning [J]. Proceedings of the IEEE, 1990, 78(9): 1481-1497
[131] Bianchini M., Frasconi P., Cori M.; Learning without Local Minima in Radial Basis Function Network [J]. IEEE Transactions on Neural Network, 1995, 6(3): 749-756.
[132]何玉彬,李新忠.神经网络控制技术及其应用[M].北京:科学出版社, 2000
[133]邓小龙.基于粒子滤波的目标跟踪与系统辨识方法的研究, [博士学位论文],上海交通大学,2005
[134]李安平.复杂环境下的视频目标跟踪算法研究, [博士学位论文],上海交通大学, 2006
[135]张尧庭.贝叶斯统计推断[M].北京:科学出版社, 1991
[136] Kalman R.E., A new approach to linear filtering and prediction and prediction problems [J], Trans. ASME, Journal of basic Engineering, 1960, 82D(1): 35-45
[137] Jazwinski AH, Stochastic processes and filtering theory [M], New York, Academic Press, 1970
[138] Wan E.A., Van Der Merwe R, The unscented Kalman filter for nonlinear estimation [C], proceeding of the Symposium on Adaptive Systems for Signal Processing, Communication and control, 2000, 153-158
[139] Gordon N.J., Salmond D.J., Smith A.F., Novel approach to nonlinear/non Gaussian Bayesian state estimation [J]. IEE Proceedings-F (0143-7070), 1993, 140(2):107-113
[140] Liu J.S., Chen R., Sequential Monte Carlo methods for dynamic system [J], Journal of the American Statistical Association, 1998, 93(443): 1032-1044
[141] Kitagawa G., Monte carlo filter and smoother for Non-Gaussian nonlinear state space models [J], Journal of computation and graphical statistics, 1996, 5(1):1-25
[142] Doucet A., On sequential simulation-based methods for Bayesian filtering. Technical Report CUED-F-ENG-TR310, Dept. of. Engineering, University of Cambridge, 1998
[143] Crisan D., Doucet A., A survey of convergence results on particle filtering methods for practitioners [J], IEEE Trans. Signal Processing, 2002, 5(3): 738-746
[144] Arulampalam M.S., Maskell S., Gordon N., et al., A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J], IEEE Trans. on signal Processing, 2002, 50(2): 174-188
[145] Andrieu C., Djuric P., Doucet A., Model selection by MCMC computation [J], Signal processing, 2001, 81(1): 19-37
[146] Gilks W., Berzuini C., Following a moving target-Monte Carlo inference for Bayesian dynamic models [J]. Journal of royal Statistic society B, 2004, 63(1): 127-146
[147] Andrieu C., de Freitas N., Doucet A., et al., An introduction to MCMC for machine learning [J]. Machine Learning (0885-6125), 2003, 50(1/2): 5-43
[148] Chen R., Liu J.S., Predictive updating methods with application to Bayesian classification [J]. Journal of the Royal Statistical Society B, 1996, 58: 397-415
[149] Tanizaki H., Mariano R.S., Prediction filtering and smoothing in non-linear and non-normal cases using monte carlo integration [J]. Journal of applied econometrics, 1994, 9(2): 163-179
[150] de Freitas J.F.G., Niranjan M., Gee A.H., et al, Sequential monte carlo methods to train neural network models [J]. Neural Computation, 2000,12(4): 955-993
[151] van der Merwe R., de Freitas N., Doucet A., et al, The unscented particle filter, Technical Report CUED/F-INFENG-TR380, Cambridge University Engineering Department, 2000
[152] Pahlavan K., Levesque A., Wireless Information Networks, NewYork, John Wiley & Sons, 1995, 127-135
[153] Hashemi H., The indoor radio propagation channel [J]. Proc. IEEE, 1993, 81(7): 943–968
[154] Patwari N., O’Dea R.J., Wang Y., Relative location in wireless networks [C]. IEEE VTC,vol. 2, 2001, 1149-1153
[155] Coulson A.J., Williamson A.G., Vaughan R.G., A statistical basis for lognormal shadowing effects in multipath fading channels [J]. IEEE Trans. Veh. Technol., 1998, 46(4): 494–502
[156]赵延明,高军,杨国庆等.煤矿安全监控系统的现状与发展[J].煤矿机电,2007, (3): 39-41
[157]张力.我国煤矿安全科技现状、问题及对策建议[J].中国煤炭, 2008, 34(7): 95-96
[158] Wu J. X., Ding Y., Jiang X. Z., Research on Integration of Coal Mine Monitoring and Control System with Field Bus Control System[J]. Journal of China University of Mining and Technology, 2006, 16(1): 82-85
[159] Song D. D., Ren Z. H., Gu Y. X., Design and Implement of An Intelligent Coal Mine Monitoring System[C]. Piscataway, NJ, USA: IEEE, Electronic Measurement and Instruments. (ICEMI '07), 2007, 849-852
[160] Li M., Liu Y., Underground Structure Monitoring with Wireless Sensor Networks[C]. Information Processing in Sensor Networks (IPSN’07), New York, USA: Association for Computing Machinery, 2007, 69-78
[161] Sohrabi K, Gao J., Ailawadhi V., et al., Protocols for self-organization of a wireless sensor network [J]. IEEE Personal Communications, 2000, 7(5): 16-27.
[162]唐勇,周明天,张欣.无线传感器网络路由协议研究进展[J].软件学报, 2006, 17(3): 410-421