摘要
无线传感器网络有望在很多领域取得广泛的应用,包括环境监测、智能农业、目标探测和跟踪、机器人应用等。并且该技术有望嵌入到因特网中,实现外部世界与人类社会的信息交换,拓宽人类获取信息的方式。无线传感器网络因其低功耗、低成本、自组织性、可扩展性、隐蔽性等优点而将在监视和跟踪领域发挥越来越重要的作用。
本文首先介绍了无线传感器网络的组成、拓扑结构、路由协议、应用前景等方面的知识。接着描述了无线传感器网络中常用的节点定位算法。使用多维标度定位算法对无线传感器网络中的节点进行定位,并且根据不同的仿真参数给出定量的分析结果。最后,给出了扩展卡尔曼滤波算法、无迹卡尔曼滤波算法和粒子滤波算法的推导过程和仿真分析。针对无线传感器网络中的多传感器融合目标跟踪,提出一种混合滤波算法,称为扩展混合粒子滤波算法(EM-PF)。该算法使用了一个混合的粒子传播方案。在使用粒子滤波算法对无线传感器网络中的节点测量信息进行融合时,粒子滤波器中的一部分粒子使用从扩展卡尔曼滤波状态传播获得的高斯分布作为建议分布进行粒子传播,而剩余的另一部分粒子则简单地使用状态转移先验分布进行粒子传播。无线传感器网络中的融合跟踪仿真结果表明,和纯粒子滤波算法相比,在仿真速率相当的情况下,混合滤波算法明显提高了跟踪精度和稳定性。
Wireless sensor networks are expected to find a wide variety of applications in many areas, including environmental monitoring, intelligent agriculture, target detection and tracking, robotics applications and so on. The technology of wireless sensor networks is expected to be embedded into the Internet, so as to facilitate information exchange between the external world and human beings, and broaden the way people seek information. Because of the merit of low power consumption, low cost, high capacity of self-organizing, scalability and imperceptibility, wireless sensor networks will play a more and more important role in the field of surveillance and tracking.
This thesis gives an introduction of the knowledge of wireless sensor networks' components, architecture, routing protocols, application prospects and so on at the beginning. After that, some node localization algorithms frequently used in wireless sensor networks are described. Multidimentional scaling map algorithm is employed to achieve node localization in wireless sensor networks, and quantitative analysis depends on different simulation parameters is provided. Finally, the computational procedures and simulation analysis of extended Kalman filtering algorithm, unscented Kalman filtering algorithm and particle filtering algorithm is presented. Aiming at multisensor fusion based target tracking applications in wireless sensor networks, a mixed algorithm is proposed, called extended-mixed particle filter(EM-PF). The algorithm utilizes a mixed particle propagation scheme. In the process of multi-sensor measurement fusion using a particle filter in a wireless sensor network, a certain number of particles in the particle filter are propagated by using a Gaussian distribution obtained from an extended Kalman filter as the proposal distribution, while the rest of the particles are simply propagated by using the state transition prior distribution. Simulation results of fusion-tracking in wireless sensor networks show that, with a similar simulation speed, the mixed filter significantly increases the tracking accuracy and robustness as compared with the results obtained by the pure particle filter.
引文
[1]Priya Shashank, Popa Dan, Lewis Frank. Energy efficient mobile wireless sensor networks. Proceedings of 2006 ASME International Mechanical Engineering Congress and Exposition-Aerospace Division,2006.
[2]孙亭,杨永田,李立宏.无线传感器网络技术发展现状.电子技术应用.2006,6：1-5.
[3]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.2008,45(1)：1-15.
[4]杨小莹,彭刚.浅析无线传感器网络及其在目标跟踪中的应用.电脑知识与技术.2008,4(4)：872-874.
[5]陈英,舒坚,陈宇斌等.无线传感器网络技术研究.传感器与微系统.2007,26(10)：1-4.
[6]A. Willig. Wireless sensor networks:concept, challenges and approaches. Elektro-technik and Informationstechnik.2006,6,123(6):224-231.
[7]Joseph M. Hellerstein, Wei Hong, Samuel R. Madden. The sensor spectrum: Technology, trends, and requirements. SIGMOD Record.2003,12,32(4):22-27.
[8]B. F. Spencer Jr, Manuel E. Ruiz-Sandoval, Narito Kurata. Smart sensing technology:opportunities and challenges. Structural Control and Health Monitoring.2004,10,11(4):349-368.
[9]Shahin Farshchi, Paul H. Nuyujukian, Aleksey Pesterev, et al. A TinyOS-enabled MICA2-based wireless neural interface. IEEE Transactions on Biomedical Engineering.2006,7,53(7):1416-1424.
[10]David Gay, Phil Levis, David Culler. Software Design Patterns for TinyOS. ACM SIGPLAN Notices.2005,40(7):40-49.
[11]Liang Song, Dimitrios Hatzinakos. A Cross-Layer Architecture of Wireless Sensor Networks for Target Tracking. IEEE/ACM Transcations on Networking.2007,2, 15(1):145-158.
[12]Bih-Yaw Shih, Jyun-Ying Tseng. The Development of Enhanced Hash Mechanism for Improving Performance of IEEE 802.15.4. Proceedings-8th International Conference on Intelligent Systems Design and Applications.2008, val.2:184-189.
[13]Malcolm Streeton, Calvin Stanfield. ZIGBEE:The Telemetry Solution? IEE Conference Publication.2005:55-60.
[14]E. Dalila Pinedo-Frausto, J. Antonio Garcia-Macias. An Experimental Analysis of Zigbee Networks. Proceedings-33rd IEEE Conference on Local Computer Networks.2008:723-729.
[15]P. Ferrari, A. Flammini, D. Marioli, et al. An Innovative Distributed Instrument for WirelessHART Testing.2009 IEEE Intrumentation and Measurement Technology Conference.2009:1097-1101.
[16]C.M. De Dominicis, P. Ferrari, M. Bertocco, et al. Investigating WirelessHART coexistence issues through a specifically designed simulator.2009 IEEE Intrumentation and Measurement Technology Conference.2009:1091-1096.
[17]The ISA100 Standards-Overview & Status,.
[18]Yosuke Ishii. Exploiting backbone routing redundancy in industrial wireless systems. IEEE Transactions on Industrial Electronics.2009,10,56(10): 4288-4295.
[19]Xin Ma, Wei Luo. The analysis of 6LowPAN technology. Proceedings-2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application. 2008, val.1:963-966.
[20]Geoff Mulligan. The 6LoWPAN architecture. Proceedings of the 4th Workshop on Embedded Networked Sensors.2007:78-82.
[21]Choon-Sung Nam, Hee-Jin Jeong, Dong-Ryeol Shin. Extended hierarchical routing over 6LoWPAN. Proceedings-4th International Conference on Networked Computing and Advanced Information Management.2008, val.l:403-405.
[22]IEEE standard for information technology, wireless medium access control (MAC) and physical layer (PHY) specifications for high rate wireless personal area networks (WPANs),2003.
[23]V. B. Kirubanand, S. Palaniammal. Performance modeling of client-server with wibree application using queueing petri nets and markov algorithm. Proceedings-2009 International Conference on Information Management and Engineering. 2009:357-361.
[24]Mauri Kuorilehto, Marko Hannikainen, Timo D. Hamalainen. A Survey of Application Distribution In Wireless Sensor Networks. EURASIP Journal on Wireless Communications and Networking.2005,2005(5):774-788.
[25]Th. Arampatzis, J. Lygeros, S. Manesis. A Survey of Applications of Wireless Sensors and Wireless Sensor Networks. Proceedings of the 13th Mediterranean Conference on Control and Automation.2005, val.2005:719-724.
[26]Jyh-How Huang, Saqib Amjad, PShivakant Mishra. CenWits:a sensor-based loosely coupled search and rescue system using witnesses. Proceedings of the 3rd international conference on embedded networked sensor systems.2005,11: 180-191.
[27]G. Simon, G. Balogh, G. Pap, et al. Sensor network-based countersniper system. Proceedings of the Second International Conference on Embedded Networked Sensor Systems.2004:1-12.
[28]A Line in the Sand,.
[29]Bruno Sinopoli, Courtney Sharp, Luca Schenato, et al. Distributed control applications within sensor networks. Proceedings of the IEEE on Sensor Networks and Applications.2003,8,91(8):1235-1245.
[30]Mohammad Mostafizur Rahman Mozumdar, Luciano Lavagno, Laura Vanzago. A comparison of software platforms for wireless sensor networks:MANTIS, TinyOS, and ZigBee. ACM Transactions on Embedded Computing Systems. 2009,1,8(2):1-23.
[31]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.软件学报.2005,16(5)：857-868.
[32]曹小红,李颖,丰皇.无线传感器网络节点定位技术综述.信息技术.2009,7：233-235.
[33]史龙,王福豹,段渭军等.无线传感器网络Range-Free自身定位机制与算法.计算机工程与应用.2004,val.23：127-130.
[34]张先毅,王英龙,赵洪磊.无线传感器网络节点自身定位算法.信息技术与信息化.2008,val.1：34-37.
[35]郭龙江,李建中,李金宝.无线传感器网络若干定位算法的研究.计算机工程与设计.2006,27(12)：2114-2118.
[36]韩彪,徐昌彪,袁海等.无线传感器网络中一种改进的APIT定位算法.计算机工程与应用.2008,44(4)：122-124.
[37]Yi Shang, Wheeler Ruml, Ying Zhang, et al. Localization from Connectivity in Sensor Networks. IEEE Transactions on Parallel and Distributed Systems.2004, 12,15(11):961-974.
[38]冯贤文,于宁.基于多维定标的无线传感器网络定位算法研究.仪器仪表标准化与计量.2007,6：13-16.
[39]Xiang Ji, Hongyuan Zha. Sensor Positioning in Wireless Ad-hoc Sensor Networks Using Multidimensional Scaling. Proceedings-IEEE INFOCOM:Conference on Computer Communications-Twenty Third Annual Joint Conference of the IEEE Computer and Communications Societies.2004, val.4:2652-2661.
[40]Onur Ozdemir, Ruixin Niu, Pramod K. Varshney. Tracking in wireless sensor networks using particle filtering:Physical Layer Considerations. IEEE Transactions on Signal Processing.2009,57(5):1987-1999
[41]黄艳,梁韦华,于海斌.基于粒子滤波的无线传感器网络目标跟踪算法.控制与决策.2008,12,23(12)：1389-1394.
[42]Petar M. Djuric, Mahesh Vemula, Monica F. Bugallo. Target tracking by particle filtering in binary sensor networks. IEEE Transactions on Signal Processing. 2008,56(6):2229-2238.
[43]Fatma Aounallah, Rim Amara, Monia Turki-Hadj Alouane. Particle filtering based on sign of innovation for distributed estimation in binary wireless sensor networks. IEEE Workshop on Signal Processing Advances in Wireless Communications.2008:629-633.
[44]Mahesh Vemula, Monica F. Bugallo, Petar M. Djuric. Performance comparison of Gaussian-based filters using information measures. IEEE Signal Processing Letters.14(12):1020-1023.
[45]Fred Daum. Nonlinear filters:beyond the Kalman filter. IEEE Aerospace and Electronic Systems Magazine.2005,20(8):57-69.
[46]Xu Zhen, Rui Liyang. A Self-organizing Bearings-only Target Tracking Algorithm in Wireless Sensor Network. Chinese Journal of Aeronautics.2009, 22(6):627-636.
[47]A. Farina, B. Ristic, D. Benvenuti. Tracking a ballistic target:comparison of several nonlinear filters. IEEE Transactions on Aerospace and Electronic Systems. 2002,38(3):854～867.
[48]Simon J. Julier, Jeffrey K. Uhlmann. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, Sequential State Estimation:From Kalman Filters to Particles Filters.2004,92(3):401-422.
[49]Yuanxin Wu, Dewen Hu, Meiping Wu, et al. Unscented Kalman Filtering for Additive Noise Case:Augmented versus Nonaugmented. IEEE Signal Processing Letters.2005,12(5):357-360.
[50]Zhan R, Wan J. Iterated unscented Kalman filter for passive target tracking. IEEE Transactions on Aerospace and Electronic Systems.2007,43(3):1155-1163.
[51]Jayesh H. Kotecha, Petar M. Djuric. Gaussian particle filtering. IEEE Transactions on Signal Processing.2003,51(10):2592-2601.
[52]Ekman Mats, Bergman Nielas. Ground target tracking with acoustic sensors using particle filters and statistical data association. NSSPW-Nonlinear Statistical Signal Processing Workshop 2006.2006.
[53]Mahesh Vemula, Monica F. Bugallo, Petar M. Djuric. Particle filtering-based target tracking in binary sensor networks using adaptive thresholds.2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing.2007:17-20.
[54]Joaquin Miguez, Antonio Artes-Rodriguez. Particle filtering algorithms for tracking a maneuvering target using a network of wireless dynamic sensors. EURASIP Journal on Applied Signal Processing.2006,2006:1-16.
[55]Orton M, Fitzgerald W. A Bayesian approach to tracking multiple targets using sesor arrays and particle filters. IEEE Transactions on Signal Processing.2002, 50(2):216-223.
[56]Yan Zhai, Mark B. Yeary, Joseph P. Havlicek, et al. A new centralized sensor fusion-tracking methodology based on particle filtering for power-aware systems. IEEE Transactions on Instrumentation and Measurement.2008,57(10):2377-2387.
[57]D. G. Lainiotis, Paraskevas Papaparaskeva. A new class of efficient adaptive nonlinear filters (ANLF). IEEE Transactions on Signal Processing.1998,46(6): 1730-1737.
