无线传感器网络网络信息融合与目标跟踪的研究
|
摘要
随着无线通信,集成电路,传感器技术和微机电系统等技术的逐渐成熟和迅速发展,同时传感器信息技术也加快发展,无线传感器的网络技术随之产生。它是由部署在监测控制范围内大量的小型,微型传感器节点构成,通过无线通信方式构成一个多跳的自组织的网络系统,其目的是能做到协作感应,收集,辨识和运作网络覆盖区域中的被监测的对象,并将最终处理结果传送给观测者。由于无线传感器网络具有价格优势,成本不高,体积相对较小,组网灵活便捷等诸多优点,所以在国防安全,环境勘测,交通监控,紧急救护,反恐抗灾等诸多领域,无线传感器网络有着极为重要的科研价值和可观的发展潜力,得到全世界广泛研究学者的密切关注,被公认为是21世纪对人类生活产生巨大影响的新兴IT技术之一。
本文针对无线传感器网络的特点,分析了信息融合的基本概念和特点,结合了无线传感器网络和信息融合算法,从而达到提高所收集信息的准确性以及收集信息的效率的目的。同时,由于传感器节点的监测半径范围有限,为了降低无线传感器网络中的能量开销,减少存储和网络资源,本文采用了一种新的信息融合机制。根据目标的当前所处的位置,将传感器网络中的节点进行动态分簇,建立出一个分布式跟踪的机制。针对线性系统,本文将三种算法应用于分层分簇的机制下,与不采用分簇机制的算法进行了仿真对比。结果表明,基于分簇机制下的信息融合算法能够将精度条件保持在一定范围内,并且有效地减少网络能耗,降低了传输时间,延长了网络的生存周期。此外,针对实际应用中存在的网络丢包问题,采用Grubbs准则剔除了异常数据,能有效地保证精度。
针对非线性系统以及网络能耗问题,本文提出了一种基于分簇机制下的无迹卡尔曼滤波算法,仿真结果表明与传统扩展卡尔曼和无迹卡尔曼滤波算法相比,基于分簇机制下的无迹卡尔曼具有良好的跟踪效果,减少了数据传输量,减轻网络冲突和拥塞,降低了网络能耗,延长了网络生存周期。
With rapid development of wireless telecommunication, integrated circuits, sensor information technology, micro-electromechanical systems and other technologies, wireless sensor network emerges as the era requires. Wireless sensor network is composed of a large number of tiny sensor nodes, which constitute a multi-hop and self-organized network system. Its goal is to perceive, acquire and deal with the target in the monitoring area collaboratively and to send the results to the observer. Because of the advantages in cost, size and flexibility of wireless sensor network, it is widely used in national security, environmental monitoring, traffic management, medical care, anti-terrorism, anti-disaster and other fields. Therefore, wireless sensor network, recognized as one of the IT technologies which have unprecedented impact on human life in 21st century, has significant scientific values and broad prospects of applications.
In this thesis, wireless sensor network is combined with information fusion algorithm to improve the accuracy and efficiency of the collected information based on the characteristics of wireless sensor network and the concepts and features of information fusion. In the meantime, this paper introduces a new information fusion mechanism in order to reduce the energy, decrease the storage and network resources. This distributed tracking mechanism is based on the strategy that dynamic clusters in wireless sensor network are organized according to the current position of the target. For linear system, three fusion algorithms are applied into clustering mechanism and compared with the algorithms without clustering mechanism respectively. According to the simulation results, the effectivity and reliability of this algorithm have been verified. Additionally, Grubbs guideline can be used to eliminate the abnormal data to get better accuracy when packet-dropping exists in wireless sensor network.
For nonlinear system and energy problem, Unscented Kalman Filter based on clustering mechanism is proposed. Simulation results show that this algorithm has a good tracking performance, reduces network conflicts and congestion effectively, cuts down the consumption of wireless sensor network and prolongs the life period of wireless sensor network, compared to the traditional Extended Kalman Filter and Unscented Kalman Filter.
本文针对无线传感器网络的特点,分析了信息融合的基本概念和特点,结合了无线传感器网络和信息融合算法,从而达到提高所收集信息的准确性以及收集信息的效率的目的。同时,由于传感器节点的监测半径范围有限,为了降低无线传感器网络中的能量开销,减少存储和网络资源,本文采用了一种新的信息融合机制。根据目标的当前所处的位置,将传感器网络中的节点进行动态分簇,建立出一个分布式跟踪的机制。针对线性系统,本文将三种算法应用于分层分簇的机制下,与不采用分簇机制的算法进行了仿真对比。结果表明,基于分簇机制下的信息融合算法能够将精度条件保持在一定范围内,并且有效地减少网络能耗,降低了传输时间,延长了网络的生存周期。此外,针对实际应用中存在的网络丢包问题,采用Grubbs准则剔除了异常数据,能有效地保证精度。
针对非线性系统以及网络能耗问题,本文提出了一种基于分簇机制下的无迹卡尔曼滤波算法,仿真结果表明与传统扩展卡尔曼和无迹卡尔曼滤波算法相比,基于分簇机制下的无迹卡尔曼具有良好的跟踪效果,减少了数据传输量,减轻网络冲突和拥塞,降低了网络能耗,延长了网络生存周期。
With rapid development of wireless telecommunication, integrated circuits, sensor information technology, micro-electromechanical systems and other technologies, wireless sensor network emerges as the era requires. Wireless sensor network is composed of a large number of tiny sensor nodes, which constitute a multi-hop and self-organized network system. Its goal is to perceive, acquire and deal with the target in the monitoring area collaboratively and to send the results to the observer. Because of the advantages in cost, size and flexibility of wireless sensor network, it is widely used in national security, environmental monitoring, traffic management, medical care, anti-terrorism, anti-disaster and other fields. Therefore, wireless sensor network, recognized as one of the IT technologies which have unprecedented impact on human life in 21st century, has significant scientific values and broad prospects of applications.
In this thesis, wireless sensor network is combined with information fusion algorithm to improve the accuracy and efficiency of the collected information based on the characteristics of wireless sensor network and the concepts and features of information fusion. In the meantime, this paper introduces a new information fusion mechanism in order to reduce the energy, decrease the storage and network resources. This distributed tracking mechanism is based on the strategy that dynamic clusters in wireless sensor network are organized according to the current position of the target. For linear system, three fusion algorithms are applied into clustering mechanism and compared with the algorithms without clustering mechanism respectively. According to the simulation results, the effectivity and reliability of this algorithm have been verified. Additionally, Grubbs guideline can be used to eliminate the abnormal data to get better accuracy when packet-dropping exists in wireless sensor network.
For nonlinear system and energy problem, Unscented Kalman Filter based on clustering mechanism is proposed. Simulation results show that this algorithm has a good tracking performance, reduces network conflicts and congestion effectively, cuts down the consumption of wireless sensor network and prolongs the life period of wireless sensor network, compared to the traditional Extended Kalman Filter and Unscented Kalman Filter.
引文
[1]Akyildiz I F, Su W, Sankarasubramaniam Y, et al. A Survey on Sensor Networks[J]. IEEE Communications Magazine.2002,40(8):102-114
[2]Cullar D, Estrin D, Strvastava M. Overview of Sensor Network[J]. Computer.2004, 37(8):41-49
[3]Callaway E H. Wireless Sensor Network:Architecture and Protocols[M]. CRC Press LLC.2004:41-62
[4]Byrne J A.21 ideas for 21st Century[J]. Business Week.1999,8:78-167
[5]Wade R, Mitehel W M, Petter F. Ten Emerging Technologies that will Change the World[J]. Technology Review.2003,106(1):33-49
[6]Asada G, Dong M, Lin T S, Newberg F, Pottie G, Kaiser W J, Marcy H O. 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
[7]Kahn J M, Katz R H, Pister K S J. Next century challenges:mobile networking for "Smart Dust"[C]. Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking.1999:271-278
[8]Chong C Y, Kumar S P. Sensor Networks:Evolution, Opportunities, and Challenges[C]//Proceedings of the IEEE, USA:IEEE.2003:1247-1256
[9]Walrod J. Sensor Network Technology for Joint Undersea Warfare[C]. In Proceeding of the NDIA Joint Undersea Warfare Technology Conference, San Diego.2002:246-254
[10]Ho L, Moh M, Walker Z, et al. A Prototype on RFID and Sensor Networks for Elder Healthcare:Progress Report[R]. SIGCOMM'05 Workshops.2005:70-75
[11]Werner-Allen G, Swieskowski P, and Welsh M. Demonstration:Realtime volcanic earthquake localization[C]. In Proceedings of the 9th ACM SenSys, Boulder, Co. Nov 2006
[12]Martinez K, Padhy P, Riddoch A, Ong H, and Hart J. Glacial enviorment monitoring using sensor networks[C]. In Proceedings on ACM REALWSN, Stockholm, Sweden. Jun.2005
[13]Karpinski M, Senart A, Cahill V. Sensor Networks for Smart Roads[C]. In Proceeding of the 2nd IEEE International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS).2006:158-167
[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.2003,21:159-168
[15]Lindsey S, Raghavendra C S, Sivalingam K M. Data gathering in sensor networks using the energy delay metric[C]. In Proc 15th Int'l Parallel and Distributed Processing Symp (IPDPS'01), San Francisco, CA. April 2001
[16]Krishnamachari B, Estrin D, Wicker S. Impact of data aggregation in wireless sensor networks[C]. In:Proc 22nd Int'l Conf on Distributed Computing Systems, Vienna, Austria. July 2002
[17]Zhu Y M. Multisensor Decision and Estimation Fushion[M]. Boston:Klvwer Academic Publishers,2003
[18]Krishnamachari B, Estrin D, Wicker S. Modeling Data-centric Routing in Wireless Sensor Networks[C]. In Proceeding of IEEE INFOCOM,2002
[19]Rao B S Y, Durrant-Whyte H F, Sheen J A. A fully decentralized multi-sensor system for tracking and surveillancefJ]. International Journal of Robotics Research.1993,12(1): 20-44
[20]Sanders C W, Tacker E C, Linton T D. A new class of decentralized filters for interconnected systems[J]. IEEE Transactions on Automatic Control.1974,19(3): 259-262
[21]Iftar A. Decentralized estimation and control with overlapping input, state and output decomposition[J]. Automatica.1993,29(2):511-516
[22]Luo H, Luo J, Liu Y H, et al. Energy Efficient Routing with Adaptive Data Fusion in Sensor Networks [C]. In Proceeding of DIALM-POMC05.2005:80-88
[23]Intanagonwiwat C, Govindan R, Estrin D, Heidemann J. Directed diffusion for wireless sensor networking. IEEE/ACM Trans. on Networking.2003,11(1):2-16
[24]于立娟,李思敏.无线传感器网络LEACH改进算法的设计与仿真[J].光通信研究.2008,22(5)：67-70
[25]郭强,孙强,李雪,曹蕾.无线传感器网络LEACH协议的研究[J].通信技术.2008,41(12)：74-76
[26]余静涛,胡同森,钟明霞.无线传感器网络路由协议LEACH的研究与改进[J].计算机系统应用.2009,18(2)：30-34
[27]房晓菲,沈永增,姚俊杰.一种基于LEACH的新型WSN路由算法[J].机电工程.2008,25(5)：100-103
[28]季莹莹,章坚武,虞成磊.无线传感器网络权值分簇路由协议改进[J].杭州电子科技大学学报.2008,28(6)：29-32
[29]吴迪,胡钢,倪刚,张桌,李威.无线传感器网络多路径簇头链分簇式路由算法[J].计算机工程与科学.2008,30(6)：101-105
[30]陈建明,王青海,路建军.自适应分簇拓扑算法EC-LEACH的研究[J].测试技术学报.2008,14(6)：538-543
[31]Chiasserini C F, RAO R R. On the concept of distributed digital signal processing in Wireless Sensor Networks[C]. Proceedings of MILCOM, vol.1.2002:260-264
[32]Kalman R E. A new approach to linear filtering and prediction problems[J]. Transactions of the ASME:Journal of Basic Engineering.1960,82(1):35-45
[33]Saber R O. Distributed Kalman Filter with embedded consensus filters[C]. In Proceedings of the 44th IEEE Conference on Decision and Control and European Control Conference(CDC-ECC'05). Seville, Spain:IEEE.2005:8179-8184
[34]Saber R O. Distributed Kalman filtering for sensor networks[C]//Proceedings of the 46th IEEE Conference on Decision and Control. Piscataway. NJ, USA:IEEE.2007: 5492-5498
[35]Benediktsson J A and Swain P H. Consensus theoretic classification methods[J]. IEEE Transactions on Systems, Man and Cybernetics.1992,22(4):688-704
[36]Strogatz S H. Exploring the onset of synchronization in populations of coupled oscillators[J]. Physica D.2000(143):1-20
[37]Sepulchre R, Paley D and Leonard N. Collective motion and oscillator Synchronization[C]. Proc. Block Island Workshop Cooperative Control, Block Island, RI. Jun.2003
[38]Papachristodoulou A and Jadbabaie A. Synchronization in oscillator networks:Switching topologies and non-homogeneous delays[C].44th IEEE Conference.2005:5692-5697
[39]Tanner H G, Jadbabaie A, and Pappas G J. Stable flocking of mobile agents, Part Ⅰ:Fixed topology[J]. Proc. IEEE Conference on Decision and Control, Maui, Hawaii USA. 2003(2):2010-2015
[40]Tanner H. Flocking with Obstacle Avoidance in Switching Networks of Interconnected Vehicles[C]. IEEE International Conference Robotics and Automation[C].New Orleans LA. April 26-May 1.2004:3006-3011
[41]Gupta V, Hassibi B, and Murray R. Stability analysis of stochastically varying formations of dynamic agents[C]. Proc. of CDC.2003:504-509
[42]Roy S, Saberi A, and Herlugson K. Formation and alignment of distributed sensing agents with double-integrator dynamics and actuator saturation[C]. Sensor Network Applications, IEEE Press.2004
[43]席峰,刘中.基于信息矩阵加权一致策略的分布式Kalman滤波器[J].信息与控制.2010,39(2)：194-199
[44]姜晓君,孔金生,刘华普.基于无线传感器网络的分布递阶信息融合算法[J].计算机工程与应用.2007,43(22)：147-149
[45]Lee M, Wong V. An energy-aware spanning tree algorithm for data aggregation in wireless sensor networks. In Proceedings of IEEE PACRIM'05. Aug.2005:300-303
[46]Yang Y, Wu B, Chen I H. Shortest Hop Routing Tree for Wireless Sensor Networks[C]. IEEE International Conference Off Communications. DC:IEEE Computer Society. 2006(8):3450-3454
[47]杨小军,邢科义,施坤林,潘泉.传感器网络下机动目标动态协同跟踪算法[J].自动化学报.2007,33(10)：1029-1035
[48]Zhao F, Shin J, Reich J. Information driven dynamic sensor collaboration[J]. IEEE Signal Processing Magazine.2002,19(2):61-71
[49]Kumar S, Zhao F, Shepherd D. Collaborative signal and information processing in microsensor networks[J]. IEEE Signal Processing Magazine.2002,19(1):13-14
[50]Chen W P, Hou J C, Lui S. Dynamic clustering for acoustic target tracking in wireless sensor networks[J]. IEEE Transactions on Mobile Computing.2004,3(3):258-271
[51]冯清青,李霁野.无线传感器网络节点分簇与非分簇性能比较[J].计算机应用研究.2009,26(11)：4244-4247
[52]薛锋,刘忠,曲毅.无线传感器网络中的分布式目标被动跟踪算法[J].系统仿真学报.2007,19(15)：3499-3502
[53]杨斌,阳建宏,徐金梧,杨德斌.无线传感器网络中混合簇头选举算法的研究[J].计算机应用研究.2008,25(4)：1227-1260
[54]张海玉,冯秀芳.无线传感器网络中的数据融合研究[J].电脑开发与应用.2010,23(10)：15-17
[55]张冬梅,俞立,周明华.具有快变时延和丢包的网络控制系统镇定[J].控制理论与应用.2008,3(25)：480-484
[56]Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan M I, Sastry S S. Kalman filtering with intermittent observations[J]. IEEE Transactions on Automatic Control. 2004,49:1453-1464
[57]潘泉,杨峰,叶亮等.一类非线性滤波器——UKF综述[J].控制与决策.2005,20(5)：1-10
[58]Veronique D, Ramesh N N, Richard G B. Robust Distributed Estimation Using the Embedded Subgraphs Algorithm[J]. IEEE Transactions on Signal Processing.2006, 54:2998-3010
[59]Neeta T, Balakrishnan N. Graphical Models for Distributed Inference in Wireless Sensor Networks[C].2009 Third International Conference on Sensor Technologies and Applications.2009:596-603
[60]Alexander T I, John W F, Randolph L M. Nonparametric Belief Propagation for Sensor Self-Calibration[C].2004:861-864
[61]李闻白,刘明雍,李虎雄,陈学永.基于单领航者相对位置测量的多AUV协同导航系统定位性能分析[J].自动化学报.2011,37(6)：724-736
[62]王志刚,芮国胜,胡昊.UPF算法及其在目标跟踪中的应用[J].雷达科学与技术.2005，01：1672-1676
[63]于海滨,曾鹏.分布式无线传感器网络协议研究[J].通信学报.2004,25(10)：102-110
[64]Choi W, Shah P, Das S K.A framework for energy-saving data gathering using two-phase clustering in wireless sensor networks[C]. Proceedings of the International Conference on Mobile and Ubiquitous Systems:Networking and Services(MOBIQUITOUS), Boston, MA.2004:203-212
[65]戴晓华,王智,蒋鹏,夏锋,孙优贤.无线传感器网络智能信息处理研究.传感器技术学报.2006,19(1)：1-17
[2]Cullar D, Estrin D, Strvastava M. Overview of Sensor Network[J]. Computer.2004, 37(8):41-49
[3]Callaway E H. Wireless Sensor Network:Architecture and Protocols[M]. CRC Press LLC.2004:41-62
[4]Byrne J A.21 ideas for 21st Century[J]. Business Week.1999,8:78-167
[5]Wade R, Mitehel W M, Petter F. Ten Emerging Technologies that will Change the World[J]. Technology Review.2003,106(1):33-49
[6]Asada G, Dong M, Lin T S, Newberg F, Pottie G, Kaiser W J, Marcy H O. 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
[7]Kahn J M, Katz R H, Pister K S J. Next century challenges:mobile networking for "Smart Dust"[C]. Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking.1999:271-278
[8]Chong C Y, Kumar S P. Sensor Networks:Evolution, Opportunities, and Challenges[C]//Proceedings of the IEEE, USA:IEEE.2003:1247-1256
[9]Walrod J. Sensor Network Technology for Joint Undersea Warfare[C]. In Proceeding of the NDIA Joint Undersea Warfare Technology Conference, San Diego.2002:246-254
[10]Ho L, Moh M, Walker Z, et al. A Prototype on RFID and Sensor Networks for Elder Healthcare:Progress Report[R]. SIGCOMM'05 Workshops.2005:70-75
[11]Werner-Allen G, Swieskowski P, and Welsh M. Demonstration:Realtime volcanic earthquake localization[C]. In Proceedings of the 9th ACM SenSys, Boulder, Co. Nov 2006
[12]Martinez K, Padhy P, Riddoch A, Ong H, and Hart J. Glacial enviorment monitoring using sensor networks[C]. In Proceedings on ACM REALWSN, Stockholm, Sweden. Jun.2005
[13]Karpinski M, Senart A, Cahill V. Sensor Networks for Smart Roads[C]. In Proceeding of the 2nd IEEE International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS).2006:158-167
[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.2003,21:159-168
[15]Lindsey S, Raghavendra C S, Sivalingam K M. Data gathering in sensor networks using the energy delay metric[C]. In Proc 15th Int'l Parallel and Distributed Processing Symp (IPDPS'01), San Francisco, CA. April 2001
[16]Krishnamachari B, Estrin D, Wicker S. Impact of data aggregation in wireless sensor networks[C]. In:Proc 22nd Int'l Conf on Distributed Computing Systems, Vienna, Austria. July 2002
[17]Zhu Y M. Multisensor Decision and Estimation Fushion[M]. Boston:Klvwer Academic Publishers,2003
[18]Krishnamachari B, Estrin D, Wicker S. Modeling Data-centric Routing in Wireless Sensor Networks[C]. In Proceeding of IEEE INFOCOM,2002
[19]Rao B S Y, Durrant-Whyte H F, Sheen J A. A fully decentralized multi-sensor system for tracking and surveillancefJ]. International Journal of Robotics Research.1993,12(1): 20-44
[20]Sanders C W, Tacker E C, Linton T D. A new class of decentralized filters for interconnected systems[J]. IEEE Transactions on Automatic Control.1974,19(3): 259-262
[21]Iftar A. Decentralized estimation and control with overlapping input, state and output decomposition[J]. Automatica.1993,29(2):511-516
[22]Luo H, Luo J, Liu Y H, et al. Energy Efficient Routing with Adaptive Data Fusion in Sensor Networks [C]. In Proceeding of DIALM-POMC05.2005:80-88
[23]Intanagonwiwat C, Govindan R, Estrin D, Heidemann J. Directed diffusion for wireless sensor networking. IEEE/ACM Trans. on Networking.2003,11(1):2-16
[24]于立娟,李思敏.无线传感器网络LEACH改进算法的设计与仿真[J].光通信研究.2008,22(5)：67-70
[25]郭强,孙强,李雪,曹蕾.无线传感器网络LEACH协议的研究[J].通信技术.2008,41(12)：74-76
[26]余静涛,胡同森,钟明霞.无线传感器网络路由协议LEACH的研究与改进[J].计算机系统应用.2009,18(2)：30-34
[27]房晓菲,沈永增,姚俊杰.一种基于LEACH的新型WSN路由算法[J].机电工程.2008,25(5)：100-103
[28]季莹莹,章坚武,虞成磊.无线传感器网络权值分簇路由协议改进[J].杭州电子科技大学学报.2008,28(6)：29-32
[29]吴迪,胡钢,倪刚,张桌,李威.无线传感器网络多路径簇头链分簇式路由算法[J].计算机工程与科学.2008,30(6)：101-105
[30]陈建明,王青海,路建军.自适应分簇拓扑算法EC-LEACH的研究[J].测试技术学报.2008,14(6)：538-543
[31]Chiasserini C F, RAO R R. On the concept of distributed digital signal processing in Wireless Sensor Networks[C]. Proceedings of MILCOM, vol.1.2002:260-264
[32]Kalman R E. A new approach to linear filtering and prediction problems[J]. Transactions of the ASME:Journal of Basic Engineering.1960,82(1):35-45
[33]Saber R O. Distributed Kalman Filter with embedded consensus filters[C]. In Proceedings of the 44th IEEE Conference on Decision and Control and European Control Conference(CDC-ECC'05). Seville, Spain:IEEE.2005:8179-8184
[34]Saber R O. Distributed Kalman filtering for sensor networks[C]//Proceedings of the 46th IEEE Conference on Decision and Control. Piscataway. NJ, USA:IEEE.2007: 5492-5498
[35]Benediktsson J A and Swain P H. Consensus theoretic classification methods[J]. IEEE Transactions on Systems, Man and Cybernetics.1992,22(4):688-704
[36]Strogatz S H. Exploring the onset of synchronization in populations of coupled oscillators[J]. Physica D.2000(143):1-20
[37]Sepulchre R, Paley D and Leonard N. Collective motion and oscillator Synchronization[C]. Proc. Block Island Workshop Cooperative Control, Block Island, RI. Jun.2003
[38]Papachristodoulou A and Jadbabaie A. Synchronization in oscillator networks:Switching topologies and non-homogeneous delays[C].44th IEEE Conference.2005:5692-5697
[39]Tanner H G, Jadbabaie A, and Pappas G J. Stable flocking of mobile agents, Part Ⅰ:Fixed topology[J]. Proc. IEEE Conference on Decision and Control, Maui, Hawaii USA. 2003(2):2010-2015
[40]Tanner H. Flocking with Obstacle Avoidance in Switching Networks of Interconnected Vehicles[C]. IEEE International Conference Robotics and Automation[C].New Orleans LA. April 26-May 1.2004:3006-3011
[41]Gupta V, Hassibi B, and Murray R. Stability analysis of stochastically varying formations of dynamic agents[C]. Proc. of CDC.2003:504-509
[42]Roy S, Saberi A, and Herlugson K. Formation and alignment of distributed sensing agents with double-integrator dynamics and actuator saturation[C]. Sensor Network Applications, IEEE Press.2004
[43]席峰,刘中.基于信息矩阵加权一致策略的分布式Kalman滤波器[J].信息与控制.2010,39(2)：194-199
[44]姜晓君,孔金生,刘华普.基于无线传感器网络的分布递阶信息融合算法[J].计算机工程与应用.2007,43(22)：147-149
[45]Lee M, Wong V. An energy-aware spanning tree algorithm for data aggregation in wireless sensor networks. In Proceedings of IEEE PACRIM'05. Aug.2005:300-303
[46]Yang Y, Wu B, Chen I H. Shortest Hop Routing Tree for Wireless Sensor Networks[C]. IEEE International Conference Off Communications. DC:IEEE Computer Society. 2006(8):3450-3454
[47]杨小军,邢科义,施坤林,潘泉.传感器网络下机动目标动态协同跟踪算法[J].自动化学报.2007,33(10)：1029-1035
[48]Zhao F, Shin J, Reich J. Information driven dynamic sensor collaboration[J]. IEEE Signal Processing Magazine.2002,19(2):61-71
[49]Kumar S, Zhao F, Shepherd D. Collaborative signal and information processing in microsensor networks[J]. IEEE Signal Processing Magazine.2002,19(1):13-14
[50]Chen W P, Hou J C, Lui S. Dynamic clustering for acoustic target tracking in wireless sensor networks[J]. IEEE Transactions on Mobile Computing.2004,3(3):258-271
[51]冯清青,李霁野.无线传感器网络节点分簇与非分簇性能比较[J].计算机应用研究.2009,26(11)：4244-4247
[52]薛锋,刘忠,曲毅.无线传感器网络中的分布式目标被动跟踪算法[J].系统仿真学报.2007,19(15)：3499-3502
[53]杨斌,阳建宏,徐金梧,杨德斌.无线传感器网络中混合簇头选举算法的研究[J].计算机应用研究.2008,25(4)：1227-1260
[54]张海玉,冯秀芳.无线传感器网络中的数据融合研究[J].电脑开发与应用.2010,23(10)：15-17
[55]张冬梅,俞立,周明华.具有快变时延和丢包的网络控制系统镇定[J].控制理论与应用.2008,3(25)：480-484
[56]Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan M I, Sastry S S. Kalman filtering with intermittent observations[J]. IEEE Transactions on Automatic Control. 2004,49:1453-1464
[57]潘泉,杨峰,叶亮等.一类非线性滤波器——UKF综述[J].控制与决策.2005,20(5)：1-10
[58]Veronique D, Ramesh N N, Richard G B. Robust Distributed Estimation Using the Embedded Subgraphs Algorithm[J]. IEEE Transactions on Signal Processing.2006, 54:2998-3010
[59]Neeta T, Balakrishnan N. Graphical Models for Distributed Inference in Wireless Sensor Networks[C].2009 Third International Conference on Sensor Technologies and Applications.2009:596-603
[60]Alexander T I, John W F, Randolph L M. Nonparametric Belief Propagation for Sensor Self-Calibration[C].2004:861-864
[61]李闻白,刘明雍,李虎雄,陈学永.基于单领航者相对位置测量的多AUV协同导航系统定位性能分析[J].自动化学报.2011,37(6)：724-736
[62]王志刚,芮国胜,胡昊.UPF算法及其在目标跟踪中的应用[J].雷达科学与技术.2005，01：1672-1676
[63]于海滨,曾鹏.分布式无线传感器网络协议研究[J].通信学报.2004,25(10)：102-110
[64]Choi W, Shah P, Das S K.A framework for energy-saving data gathering using two-phase clustering in wireless sensor networks[C]. Proceedings of the International Conference on Mobile and Ubiquitous Systems:Networking and Services(MOBIQUITOUS), Boston, MA.2004:203-212
[65]戴晓华,王智,蒋鹏,夏锋,孙优贤.无线传感器网络智能信息处理研究.传感器技术学报.2006,19(1)：1-17