基于无线传感器网络的分布式跟踪算法
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摘要
作为一种新兴的IT热点技术,无线传感器网络在军事与民用诸多领域有着广阔的应用前景。在军事领域,基于无线传感器网络的目标跟踪技术可以应用于导弹防御、海陆空防御和作战监视等；在民用领域,可以用于交通管制、导航及其机器人的道路规划和避碍等,因此对目标跟踪进行研究具有重大的理论和实际意义。
由于在传感器网络中传感器节点处理能力有限并且携带能量有限,因此设计出简单高效的目标跟踪算法对于延长传感器网络的生命期以及增强传感器网络的健壮性有着非常重要的意义。目标跟踪的极端复杂性与无线传感器网络节点能力相对不足形成矛盾,分布式是解决这对矛盾的有力武器。本文从跟踪的能量消耗、跟踪精度、跟踪的鲁棒性和跟踪反应时间四个指标出发,提出了一种由中心计算机、移动目标和无线传感器网络组成的分布式目标跟踪算法,通过邻域节点间的协同工作,克服了单个节点的能力不足,满足了目标跟踪的应用需求。在算法中提出的退避定时机制用于首领节点的选取及传输序贯的确定；改进的扩展卡尔曼滤波算法提高了滤波的精度和稳定度。
为了实现实时而准确地运用已知无线传感器网络对目标进行定位跟踪,本文结合粒子滤波和卡尔曼滤波的各自特点提出了JPEKF (Jointed Particle and Extended-Kalman Filter)算法。改进的滤波算法利用粒子滤波对初始误差不敏感的特点获取目标比较准确的初始位置估计,克服了扩展卡尔曼由于初始误差较大而引起的发散问题；然后使用扩展卡尔曼滤波进行目标的跟踪保持从而保证了系统的实时性。当目标出现丢失的情况时,系统将启动重定位机制,JPEKF滤波算法也将重新初始化,这种机制可以迅速定位丢失的目标保证了系统的稳定性。仿真结果表明提出的这种滤波算法具有很高的精度和稳定性以及较低的计算复杂度,利用节点间的协同工作,能有效地对目标进行跟踪。
As a new attractive IT technology, wireless sensor network (WSN) is very promising in many military and civil applications. In the military field, the target tracking technolodge based on wireless sensor network can be applied to missile defense, air defense and surveillance operations, etc. In the civilian areas, it can be used for traffic control, navigation and robot path planning and obstacle avoidance, etc. Therefore the study of target tracking is of great theoretical and practical significance.
Since the limited energy of the sensors and the weak processing capability, it is significant to design an alogorithm with low complexity as well as high efficiency. There is a contradiction between the extreme complexity of target tracking and the low capacity of each sensor node. Distributed is an essential characteristic of WSN, which is also a powerful measure to solve this contradiction. Through the collaborative work among all neighboring nodes, the lack of powerful capcity for a single node is overcome in order to satisfy the application requirements.
In order to realize real-time and accurate localization and tracking of mobile target on known wireless sensor network, we propose a novel algorithm:JPEKF (Jointed Particle and Extended-Kalman Filter). This alogrthm has two modules:it first applies insensitiveness of Particle Filter to initial estimation error to catch the target, which avoids the divergence due to the big error in initial estimation when using Extended Kalman Filter (EKF), and then uses EKF to keep the tracking of target. When the target is missing, the system will start relocalization mechanism and JPEKF also will initialize.The simulation results show that JPEKF is a robust algorithm with high accuracy and computing efficency.
由于在传感器网络中传感器节点处理能力有限并且携带能量有限,因此设计出简单高效的目标跟踪算法对于延长传感器网络的生命期以及增强传感器网络的健壮性有着非常重要的意义。目标跟踪的极端复杂性与无线传感器网络节点能力相对不足形成矛盾,分布式是解决这对矛盾的有力武器。本文从跟踪的能量消耗、跟踪精度、跟踪的鲁棒性和跟踪反应时间四个指标出发,提出了一种由中心计算机、移动目标和无线传感器网络组成的分布式目标跟踪算法,通过邻域节点间的协同工作,克服了单个节点的能力不足,满足了目标跟踪的应用需求。在算法中提出的退避定时机制用于首领节点的选取及传输序贯的确定；改进的扩展卡尔曼滤波算法提高了滤波的精度和稳定度。
为了实现实时而准确地运用已知无线传感器网络对目标进行定位跟踪,本文结合粒子滤波和卡尔曼滤波的各自特点提出了JPEKF (Jointed Particle and Extended-Kalman Filter)算法。改进的滤波算法利用粒子滤波对初始误差不敏感的特点获取目标比较准确的初始位置估计,克服了扩展卡尔曼由于初始误差较大而引起的发散问题；然后使用扩展卡尔曼滤波进行目标的跟踪保持从而保证了系统的实时性。当目标出现丢失的情况时,系统将启动重定位机制,JPEKF滤波算法也将重新初始化,这种机制可以迅速定位丢失的目标保证了系统的稳定性。仿真结果表明提出的这种滤波算法具有很高的精度和稳定性以及较低的计算复杂度,利用节点间的协同工作,能有效地对目标进行跟踪。
As a new attractive IT technology, wireless sensor network (WSN) is very promising in many military and civil applications. In the military field, the target tracking technolodge based on wireless sensor network can be applied to missile defense, air defense and surveillance operations, etc. In the civilian areas, it can be used for traffic control, navigation and robot path planning and obstacle avoidance, etc. Therefore the study of target tracking is of great theoretical and practical significance.
Since the limited energy of the sensors and the weak processing capability, it is significant to design an alogorithm with low complexity as well as high efficiency. There is a contradiction between the extreme complexity of target tracking and the low capacity of each sensor node. Distributed is an essential characteristic of WSN, which is also a powerful measure to solve this contradiction. Through the collaborative work among all neighboring nodes, the lack of powerful capcity for a single node is overcome in order to satisfy the application requirements.
In order to realize real-time and accurate localization and tracking of mobile target on known wireless sensor network, we propose a novel algorithm:JPEKF (Jointed Particle and Extended-Kalman Filter). This alogrthm has two modules:it first applies insensitiveness of Particle Filter to initial estimation error to catch the target, which avoids the divergence due to the big error in initial estimation when using Extended Kalman Filter (EKF), and then uses EKF to keep the tracking of target. When the target is missing, the system will start relocalization mechanism and JPEKF also will initialize.The simulation results show that JPEKF is a robust algorithm with high accuracy and computing efficency.
引文
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16. Alan Mainwaring. Wireless Sensor Networks for Habitat Monitoring[C]. Proceedings of Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications. Atlanta, Georgia, USA,2002.
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22. Addlesee M E. Implenmenting a sentient computing system[J]. IEEE Computer Magazine,2001,34(8):50-56.
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36. Nirupama Bulusu, John Heidemamm, Deborah Estrim. Low Cost Outdoor Localization for Very Small Devices[C]. IEEE Personal Communications,2000,7(5).28-34.
37. Nicolescu D, Nath B. Ad-Hoc positioning systems [C]. In:Proc. of the 2001 IEEE Global Telecommunications Conf. Vol.5, San Antonio:IEEE Communications Society,2001. 2926-2931.
38. Niculescu D, Nath B. DV based positioning in ad hoc networks[J]. Journal of Telecommunication Systems.2003,22(1/4):267-280.
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40. Liu J J, Liu J,Chu M,et al. Distributed State Representation for Tracking Problems in Sensor Networks[J]. In Proc.of 3rd workshop on Information Processing in Sensor Net-works (IPSN),2007.
41. Lance Doherty, Kristofer S.J.Pister, Laurent El Ghaoui. Convex position estimation in wireless sensor networks [J]. IEEE INFOCOM,2001.
42.孙利民、李建中.无线传感器网络[M].北京：清华大学出版社,2005.
43. LIU J, REICH J, ZHAO F. Collaborative in network processing for target tracking[J]. EURASIP Journal on App lied Signal Processing,2003 (4):378-391.
44.周宏仁,敬忠良.王培德.机动目标跟踪.国防工业出版社[M].北京,1991.
45.康嫩红.数据融合理论与应用[M].西安：电子科技大学出版社,第二版,2006.14.
46. Bar-Shalom Y. Tracking in a cuttered environment with Probabilistic data association[C]. Auto-matica, Pages 16-21,1975.
47. Blom HAP An efficient filter for abruptly changing systems[C]. In Proceeding of the 23rd IEEE conf.on Decision and Control, Las Vegas, NV, Dec.1984.
48. Lance Doherty, Kristofer S.J.Pister, Laurent EI Ghaoui. Convex position estimation in wireless sensor networks [J]. IEEE INFOCOM,2001.
49. Alberia Cerpa, Deborah Estrin. Ascent:Adaptive self-configuring sensor network topologies [C].. Technical Report C'SD-TR 01-009, University of Califonia. Los Angeles. Computer Science department,2001.5.
50. A. Galstyan, B. Krishnamachari, K. Lerman, S. Patterm. Distributed online localization in sensor networks using a mobile target [J]. In Proc. Int. Symp. Information Processing Sensor Networks (IPSN), Berkeley, CA, Apr.2004, pp.61-70.
51.张正勇,孙智,王刚,余荣,梅顺良.基于移动锚节点的无线传感器网络节点定位[J].清华大学学报(自然科学版),2007年,第47卷,第4期.
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2. Akyildiz I, Su W, Sankarasubramaniam Y, Cayirci E. A survey on sensor networks[J]. IEEE Communications Magazine.2002(8):102-114.
3. 李建中,李金宝,石胜飞.传感器网络及其数据管理的概念、问题与进展,软件学报[J].2003,14(10)：1717-1727.
4. 孙利民,李建中,陈渝等.无线传感器网络[M].清华大学出版社.2005.
5. 林瑞仲.面向目标跟踪的无线传感器网络研究[D].浙江大学.2005
6. 李建中,李金宝,石胜飞.传感器网络及其数据管理的概念、问题与进展[J].软件学报,2003,14(10)：1717—1727.
7. Hong W, Madden S. Tiny Schema:Creating Attributes and Commands in TinyOS[C]. 2005,2704:1-16.
8. University of California at Los Angeles. WINS:Wireless Integrated Network Sensors[J]. IEEE Computer and Communications Societies,2003.1734-1743.
9. Heidemann J, Silva F, Intanagonwiwat C, et al. Building Efficient Wireless Sensor Networks with Low Level Naming [A]. Proceedings of the 18th ACM Symposium on Operating System Principles, Banff (Canada),2001, New York (NY, USA):ACM Press, 2001,146—159.
10. Heinzelman W R, Kulik J, Balakrishnan H. Adaptive Protocols for Information Dissemination in Wireless Sensor Networks [M]. Seattle (WA, USA),1999, New York (NY, USA):ACM Press,1999.174—185.
11. Rentala P, Musunuri R, Gandham S, et al. Survey on Sensor Networks [C]. Technical Report, UTDCS-33-02. Dallas(TX,USA):University of Texas at Dallas,2002.
12. University of California at Berkeley. TinyDB:A Declarative Database for Sensor Networks [C]. IEEE:Conf. on Computer Vision. Nice,2003:1048-1055.
13. Intanagonwiwat C. Impact of Network Density on Data Aggregation in Wireless Sensor Networks[J]. Technical Report,01-750. Los Angeles (CA,USA):Computer Science Department, University of Southern California,2001.
14. Callaway E H, Jr.无线传感器网络：体系结构与协议[M].北京：电子工业出版社,2002.390—340.
15. Sadler C. Hardware design experiences in ZebraNet[C]. Proceedings of Proceedings of the Second ACM Conference on Embedded Networked Sensor Systems. Batimore, MD,2004.
16. Alan Mainwaring. Wireless Sensor Networks for Habitat Monitoring[C]. Proceedings of Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications. Atlanta, Georgia, USA,2002.
17. Great Duck Island H M. http://www.greatduckisland.net/.
18. Butler Z, Corke P, Peterson R. Virtual fences for controlling cows[C]. Proceedings IEEE International Conference on Robotics and Automation,2004,2004(5):4429-4436.
19. Priyantha N B, Chakraborty A, Balakrishnan H. Cricket location-support system[C]. Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM,2000.32-43.
20. Boettcher P W, Sherman J A, Shaw G A.Target localization using acoustic time-difference of arrival in distributed sensor networks[J]. Proceedings of SPIE The International Society for Optical Engineering,2002,4741:180-191.
21. Demirbas M, Arora A, Gouda M G. A pursuer-evader game for sensor networks[C]. Selfstabilizing Systems, Proc.,2003,2704:1-16.
22. Addlesee M E. Implenmenting a sentient computing system[J]. IEEE Computer Magazine,2001,34(8):50-56.
23. Xueli An J W. OPT-Online Person Tracking System for Context-awareness in Wireless Personal Network[C]. Proceedings of REALMAN'08, Florence, Italy,2008.47-54.
24. Lorincz K, Welsh M. MoteTrack:A Robust, Decentralized Approach to RF-Based Location Tracking[J]. Proceedings of Proceedings of the International Workshop on Location and Context-Awareness (LoCA 2005),2005.
25. Polastre J, Szewczyk R, Culler D. Telos:Enabling ultra-low power wireless research[C]. 2005 4th International Symposium on Information Processing in Sensor Networks, IPSN 2005,2005,2005:364-369.
26. Arora A, Dutta P, Bapat S, et al. A line in the sand:a wireless sensor network for target detection, classification, and tracking[C]. Computer Networks in the Internet Computer Telecommunications Networking,2004,46:605-634.
27. R. Orr G A. The smart floor:a mechanism for natural user identification and tracking[C]. Proceedings of Conference on Human Factors in Computing Systems, The Hague, Netherlands,2000.
28. Lesser V T M. Distributed Sensor Networks:A Multiagent Perspective[C]. Kluwer Academic Publishers,2003.
29. Waltz E L J. Multisensor Data Fusion[J]. Artech House, Norwood, MA,1990.
30. Hara S, Zhao D, Yanagihara K, et al. Propagation characteristics of IEEE 802.15.4 radio signal and their application for location estimation[C]. IEEE Vehicular Technology Conference,2005,61(1):97-101.
31. Girod L, Bychovskiy V, Elson J, Estrin D. Locating tiny sensors in time and space:A case study [C]. In:Winner Proc. of the 2002 IEEE Intel Conf. on Computer Design:VLSI in Computers and Processors. Freiburg:IEEE Computer Society,2002.214-219.
32. Harter A, Hopper A, Steggles P, Ward A, Webster P. The anatomy of a context-aware application [C]. In:Proc. of the 5th Annual ACM/IEEE Intel Conf. on Mobile Computing and Networking. Seattle:ACM Press,1999.59-68..
33. Girod L, Estrin D. Robust range estimation using acoustic and multimodal sensing [C]. In: Proc. of the IEEE/RSJ Intel Conf. on Intelligent Robots and Systems (IROS 01). Vol.3, Maui:IEEE Robotics and Automation Society,2001.1312-1320.
34. Priyantha NB, Miu AKL, Balakrishnan H, Teller S. The cricket compass for context-aware mobile applications [C]. In:Proc. of the 7th Annual Intel Conf. on Mobile Computing and Networking. Rome:ACM Press,2001.1-14.
35. Niculescu D, Nath B. Ad hoc positioning system (APS) using AOA [J]. In:Proc. of the IEEE INFOCOM 2003. Vol.3, San Francisco:IEEE Computer and Communications Societies,2003.1734-1743.
36. Nirupama Bulusu, John Heidemamm, Deborah Estrim. Low Cost Outdoor Localization for Very Small Devices[C]. IEEE Personal Communications,2000,7(5).28-34.
37. Nicolescu D, Nath B. Ad-Hoc positioning systems [C]. In:Proc. of the 2001 IEEE Global Telecommunications Conf. Vol.5, San Antonio:IEEE Communications Society,2001. 2926-2931.
38. Niculescu D, Nath B. DV based positioning in ad hoc networks[J]. Journal of Telecommunication Systems.2003,22(1/4):267-280.
39. He Tian, Huang Chengdu, Blum B M, et al. Range-free Localization Schemes in Large Scale Sensor Networks[C]. Proceedings of the 9th Annual International Conference on Mobile Computing and Networking. San Diego, California, USA. ACM Press,2003: 81-95.
40. Liu J J, Liu J,Chu M,et al. Distributed State Representation for Tracking Problems in Sensor Networks[J]. In Proc.of 3rd workshop on Information Processing in Sensor Net-works (IPSN),2007.
41. Lance Doherty, Kristofer S.J.Pister, Laurent El Ghaoui. Convex position estimation in wireless sensor networks [J]. IEEE INFOCOM,2001.
42.孙利民、李建中.无线传感器网络[M].北京：清华大学出版社,2005.
43. LIU J, REICH J, ZHAO F. Collaborative in network processing for target tracking[J]. EURASIP Journal on App lied Signal Processing,2003 (4):378-391.
44.周宏仁,敬忠良.王培德.机动目标跟踪.国防工业出版社[M].北京,1991.
45.康嫩红.数据融合理论与应用[M].西安：电子科技大学出版社,第二版,2006.14.
46. Bar-Shalom Y. Tracking in a cuttered environment with Probabilistic data association[C]. Auto-matica, Pages 16-21,1975.
47. Blom HAP An efficient filter for abruptly changing systems[C]. In Proceeding of the 23rd IEEE conf.on Decision and Control, Las Vegas, NV, Dec.1984.
48. Lance Doherty, Kristofer S.J.Pister, Laurent EI Ghaoui. Convex position estimation in wireless sensor networks [J]. IEEE INFOCOM,2001.
49. Alberia Cerpa, Deborah Estrin. Ascent:Adaptive self-configuring sensor network topologies [C].. Technical Report C'SD-TR 01-009, University of Califonia. Los Angeles. Computer Science department,2001.5.
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