臺灣博碩士論文加值系統

本論文提出在無線傳感器網絡( Wireless Sensor Networks)中把目標物放在兩個不同的拓樸中去追蹤目標物(Target Tracking)的方法。我們利用平面式網路拓樸( Flat Network)與叢集式網路拓樸( Cluster-based Network)進行目標追蹤,並且以領導節點為基礎(leader-based)的演算法來追蹤目標物。在平面式網路(Flat Network)中使用計時器(timer)來建立叢集(Cluster)，而在叢集式網路(Cluster-based Network)中則用The clustering algorithm via waiting timer (CAWT)提供叢集模型(Cluster Model)。對於目標物追蹤(Target Tracking)部分採用粒子過濾器(particle filter)]估測目標物的路徑，把得到的數據再經由協方差交集(Covariance Intersection)做數據間的融合(Data Fusion )。最後，我們將比較此兩種不同網路拓樸之追蹤的精準度，並分別比較這兩種拓樸的效能。

This thesis presents distributed methods for target tracking in wireless sensor networks. We consider flat network and cluster-based network topologies for target tracking and provide leader-based information processing to track the target. The method with a flat network uses a timer to create clusters dynamically and the method with a cluster-based network uses the Clustering Algorithm via Waiting Timer (CAWT) to create the cluster model. Particle filtering is applied to estimate the location of the target, and then Covariance Intersection algorithm is used to make data fusion with local estimates. Finally, we compare the estimation accuracy with the two different network topologies.

目次（含頁碼） 摘要 i
Abstract ii
Chapter 1 1
Introduction 1
1.1 motivations 1
1.2 Introduction of information fusion 3
1.3 Organization of Thesis 4
Chapter 2 5
Literature Review and Target Tracking System: Models and Algorithms 5
2.1 Literature Review 6
2.2 The clustering algorithm via waiting timer (CAWT) 7
2.3 Tracking Positioning with Particle Filter 10
2.3.1 Representation of dynamic systems 10
2.3.2 Bearings-only tracking 11
2.3.3 Sequential importance sampling and Resample 12
2.4 Covariance Intersection (CI) Method 15
2.4.1 Covariance Intersection Algorithm 15
2.4.2 Decentralized Data Fusion with CI 16
Chapter 3 17
Distributed Target Tracking Algorithms 17
3.1 Distributed target tracking with a flat network approach 17
3.1.1 Election of Cluster leader and Cluster members 17
3.1.2 Target Tracking using Particle Filter 20
3.1.3 Information fusion of sensor nodes with Covariance Intersection 21
3.2 Distributed target tracking with a cluster network approach 23
3.2.1 Election of Clusters 23
3.2.2 Target Tracking using Particle Filter and Information fusion 24
Chapter 4 26
Simulation and Numerical Results 26
4.1 The Initial Simulation Set-up 26
4.2 Results of distributed target tracking with a flat network 27
4.2.1 The local target estimation and target tracking error distance 27
4.2.2 The global target estimation and target tracking error distance 30
4.3 Results of distributed target tracking with a cluster network 33
4.4 Energy consumption Analysis 39
Conclusion 44
References 45

[1] Feng Zhao, Leonidas J. Guibas, “Wireless Sensor Networks: An Information Processing Approach”, 2004.

[2] G. Pottie and W. Kaiser. Wireless integrated network sensors. Communications of the ACM, 43(5):51–58, May 2000.

[3] C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proc. 6th Annual International Conference on Mobile Computing and Networks (MobiCom 2000), pages 56–67, Boston, ACM Press, August 2000.

[4] Chih-Yu Wen and William A. Sethares, “Automatic Decentralized Clustering for Wireless Sensor Networks”, EURASIP Journal on Wireless Communications and Networking 2005:5, 686–697.

[5] A. Doucet, N. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2001.

[6] S. Godsill and P. Djuric, “Monte Carlo methods for statistical signal processing,” IEEE Trans. Signal Processing (Special Issue), vol. 50, no. 2, pp. 173–499,2002.

[7] N. Gordon, D. Salmond, and A.F.M. Smith, “Novel approach tononlinear/non-Gaussian Bayesian state estimation, Proc. Inst. Elect. Eng., vol.140, no. 2, pt. F, pp. 107–113, 1993.

[8] EDUARDO F. NAKAMURA, ANTONIO A. F. LOUREIRO and ALEJANDRO C. FRERY, “Information Fusion for Wireless Sensor Networks: Methods, Models, and Classifications” ACM Computing Surveys, Vol. 39, No. 3, Article 9, Publication date: August 2007.

[9] ARNAUD DOUCET, SIMON GODSILL and CHRISTOPHE ANDRIEU, “On sequential Monte Carlo sampling methods for Bayesian filtering” ,Statistics and Computing (2000) 10, 197–208.

[10] M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon, and Tim Clapp, “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking”, IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 50, NO. 2, FEBRUARY 2002.

[11] Suganya .S,“A Cluster-based Approach for Collaborative Target Tracking in Wireless Sensor Networks”, IEEE Computer Society DOI 10.1109/ICETET.2008.241.

[12] James V. Candy:“Bootstrap Particle Filtering” IEEE SIGNAL PROCESSING MAGAZINE MAY 2007.

[13] Bar-Shalom, Y., and Li, X. R. Estimation and Tracking: Principles, Techniques, and Software. Norwood, MA: Artech House, 1993.

[14] Lee, M-S., and Kim, Y-H. An efficient multitarget tracking algorithm for car applications. IEEE Transactions on Industrial Electronics, 50, 2 (2003), 397—399.

[15] Montemerlo, M., Thrun, S., and Whittaker, W. Conditional particle filter for simultaneous mobile robot localization and people tracking. In Procceedings of the IEEE Conference on Robotics and Automation, vol. 1, 2002, 695—701.

[16] Isard, M., and Blake, A. Visual tracking by stochastic propagation of conditional density. In Proceedings of the 4th European Conference on Computer Vision, Cambridge, England, 1996, 343—356.

[17] Blackman, S. S. Mulitple-Target Tracking with Radar Applications. Norwood, MA: Artech House, 1986.

[18] Hui Ma, Brian W.-H. Ng, “Distributive Target Tracking in Wireless Sensor Networks under Measurement Origin Uncertainty”, ISSNIP 2007.

[19] M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Processing, vol. 50, no. 2, pp. 174–188, 2002.

[20]Lingji Chen, _Pablo O. Arambel _and Raman K. Mehra.“Estimation under Unknown Correlation: Covariance Intersection Revisited”

[21] C. Y. Chong and Shozo Mori. Convex combination and covariance intersection algorithms in distributed fusion. In Proceedings of Fusion 2001, 2001.

[22] Pablo O. Arambel, Constantino Rago, and Raman K. Mehra. Covariance intersection algorithm for distributed spacecraft state estimation. In Proceedings of the 2001 American Control Conference, volume 6, pages 4398 –4403, 2001.

[23] Simon Julier and Jeffrey Uhlmann. General decentralized data fusion with Covariance Intersection (CI). In D. Hall and J. Llians, editors, Handbook of multisensor data fusion, chapter 12, pages 12–1 to 12–25. CRC Press, 2001.

[24] 蕭宇成, "分散式自我定位演算法在無線隨意感測網路上之研究," 中興大學, 台中, 2008.

[25] Nandini Easwar and Jogen Shah, “Object Tracking using Particle Filter”, CIS 601, Fall 2003.

[26] T.Vercauteren, D.Guo, and X.Wang, “Joint multiple target tracking and classification in collaborative sensor networks,” IEEE Journal on Selected Areas in Communications, Volume 23, Issue 4, April 2005.

[27] Hui Ma and Brian W.-H. Ng, “The collaborative signal processing framework and algorithms for targets tracking in wireless sensor networks,” in Proceedings of SPIE Vol.6035, 2005.

[28] F.Zhao and J.Shin and J.Reich, “Information-Driven Dynamic SensorCollaboration for Tracking Applications”, Proceedings of the IEEE,Vol. 91, No. 8, 2003.

[29] Ying-Chih Chen, Pei-Lun Chung, and Chih-Yu Wen, “On Autonomous Clustering in Wireless Sensor Networks With Directional Antennas,” in Proc. of the 2010 Fourth International Conference on Sensor Technologies and Applications - SensorComm2010, Venice, Italy, July 2010.