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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Roberts, Matthew Simon
Languages: English
Types: Doctoral thesis
Subjects: QA76
The use of small, cheap, networked devices to collaboratively perform a task presents an attractive opportunity for many scenarios. One such scenario is the tracking and classification of an object moving through a region of interest. A single sensor is capable of very little, but a group of sensors can potentially provide a flexible, self-organising system that can carry out tasks in harsh conditions for long periods of time. This thesis presents a new framework for tracking and classification with a wire less sensor network. Existing algorithms have been integrated and extended within this framework to perform tracking and classification whilst managing energy usage in order to balance the quality of information with the cost of obtaining it. Novel improvements are presented to perform tracking and classification in more realistic scenarios where a target is moving in a non-linear fashion over a varying terrain. The framework presented in this thesis can be used not only in algorithm development, but also as a tool to aid sensor deployment planning. All of the algorithms presented in this thesis have a common basis that results from the integration of a wireless sensor network management algorithm and a tracking and classification algorithm both of which are considered state-of-the-art. Tracking is performed with a particle filter, and classification is performed with the Transferable Belief Model. Simulations are used throughout this thesis in order to compare the performance of different algorithms. A large number of simulations are used in each experiment with various parameter combinations in order to provide a detailed analysis of each algorithm and scenario. The work presented in this thesis could be of use to developers of wireless sensor network algorithms, and also to people who plan the deployment of nodes. This thesis focuses on military scenarios, but the research presented is not limited to this.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • ALERT Systems (2005). ALERT Systems homepage, h ttp : //www. a l e r t sy stem s. o rg / [Accessed 19 Oct 2010].
    • Arora, A., Dutta, R, Bapat, S., Kulathumani, V., Zhang, H., Naik, V., Mittal, V., Cao, H., Demirbas, M., Gouda, M., Choi, Y., Herman, T., Kulkami, S., Arumugam, U., Nesterenko, M., Vora, A., and Miyashita, M. (2004). A line in the sand: a wireless sensor network for target detection, classification, and tracking. Computer Networks, 46(5):605-634.
    • Arulampalam, S., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50(2): 174-188.
    • Atmel Corporation (2010). ATmegal28/L datasheet, http://www.atmel.com/dyn/ resources/prod_documents/doc24 67 .pdf [Accessed 23 Oct 2010].
    • Bauer, M. (1997). Approximation algorithms and decision making in the dempstershafer theory of evidence - an empirical study. International Journal o f Approximate Reasoning, 17(2-3):217 - 237.
    • Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions o f the Royal Society o f London, (53):370-418.
    • Brooks, R. R., Ramanathan, P., and Sayeed, A. (2003). Distributed target classification and tracking in sensor networks. Proceedings o f The IEEE, 91(8): 1163-1171.
    • Caron, F., Ristic, B., Duflos, E., and Vanheeghe, P. (2006). Least committed basic belief density induced by a multivariate gaussian pdf. In Proceedings o f International Conference on Information Fusion 2006.
    • Caron, F., Smets, P., Duflos, E., and Vanheeghe, P. (2005). Multisensor data fusion in the frame of the TBM on reals, application to land vehicle positioning. In Proceedings o f International Conference on Information Fusion 2005, volume 2.
    • Challa, S. and Pulford, G. W. (2001). Joint target tracking and classification using radar and ESM sensors. IEEE Transactions on Aerospace and Electronic Systems, 37(3): 1039-1055.
    • Chu, M., Haussecker, H., and Zhao, F. (2002). Scalable information-driven sensor querying and routing for ad hoc heterogeneous sensor networks. International Journal o f High Performance Computing Applications, 16(3):293-313.
    • Condor Team (2009a). Condor Version 7.2.5 Manual. University of WisconsinMadison. http://www. cs .wise.edu/condor/manual/v7 .2/condor-V7_2_ 5-Manual .pdf [Accessed 2 Aug 2010].
    • Condor Team (2009b). Directed acyclic graph manager, http://www.cs.wise.edu/ condor/dagman/ [Accessed 13 Aug 2010].
    • Cover, T. M. and Thomas, J. A. (1991). Elements o f Information Theory. John Wiley & Sons.
    • Crossbow Technology Inc. (2005). Cricket series mote, http://www.xbow.com/ Products/productsdetails.aspx?sid=116 [Accessed 3 Feb 2006].
    • Crossbow Technology Inc. (2006a).
    • Doucet, A., de Freitas, N., and Gordon, N., editors (2001b). Sequential Monte Carlo Methods in Practice. Springer, New York, NY, USA.
    • Dubois, D. and Prade, H. (1988). Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence, 4(3):244-264.
    • Ertin, E., Fisher, J., and Potter, L. (2003). Maximum mutual information principle for dynamic sensor query problems. In Information Processing in Sensor Networks, volume 3 of Lecture Notes in Computer Science, pages 405^416. Springer-Verlag.
    • Fiche, A., Martin, A., Cexus, J.-C., and Khenchaf, A. (2010). Continuous belief functions and a-stable distributions. In Proceedings o f International Conference on Information Fusion 2010, Edinburgh, UK.
    • Fosbury, A. M., Singh, T., Crassidis, J. L., and Springen, C. (2007). Ground target tracking using terrain information. In Proceedings o f International Conference on Information Fusion 2007, pages 1-8.
    • Garcfa-hemdndez, C. F., Ibargiiengoytia-gonzalez, P. H., Garcfa-hemandez, J., and Perez-dfaz, J. A. (2007). Wireless sensor networks and applications: a survey. International Journal o f Computer Science and Network Security, 7(3):264-273.
    • Gordon, N. J., Maskell, S., and Kirubarajan, T. (2002). Efficient particle filters for joint tracking and classification. In Proceedings ofSPIE, volume 4728, pages 439-449. SPIE.
    • Gordon, N. J., Salmond, D. J., and Smith, A. F. M. (1993). Novel approach to nonlinear/non-gaussian bayesian state estimation. In Radar and Signal Processing, IEE Proceedings F, volume 140, pages 107-113. IEE.
    • Haenggi, M. (2005). Opportunities and challenges in wireless sensor networks. In Ilyas, M. and Mahgoub, I., editors, Handbook o f Sensor Networks: Compact Wireless and Wired Systems, chapter 1. CRC Press.
    • Hussain, S., Schaffner, S., and Moseychuck, D. (2009). Applications of wireless sensor networks and rfid in a smart home environment. In Proceedings o f o f the Annual Conference Communication Networks and Services Research, pages 153-157, Los Alamitos, CA, USA. IEEE Computer Society.
    • Jamieson, A., Breslin, S., Nixon, P., and Smeed, D. (2004). M/POS - the mote indoor positioning system. In 1st International Workshop on Wearable and Implantable Body Sensor Networks.
    • Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions o f the ASM E-Joumal o f Basic Engineering, 82(Series D):35-45.
    • Khan, J. M., Katz, R. H., and Pister, K. S. J. (2000). Emerging challenges: Mobile networking for “smart dust”. Journal o f Communications and Networks, 2(3): 188- 196.
    • Kong, K., Liu, J. S., and Wong, W. H. (1994). Sequential imputations and bayesian missing data problems. Journal o f the American Statistical Association, 89(425):278- 288.
    • Liu, J. S., Chen, R., and Logvinenko, T. (2001). A Theoretical Frameworkfo r Sequential Importance Sampling with Resampling, chapter 11. In (Doucet et al., 2001b).
    • Lowrance, J. D., Garvey, T. D., and Strat, T. M. (1986). A framework for evidentialreasoning systems. In Proceedings o f AAAI-86, pages 896-903.
    • Maroti, M., Simon, G., Ledeczi, A., and Sztipanovits, J. (2004). Shooter localization in urban terrain. Computer, 37(8):60 - 61.
    • Martin, A. (2009). Implementing general belief function framework with a practical codification for low complexity. In Smarandache, F. and Dezert, J., editors, Advances and Applications o f DSmTfo r Information Fusion, volume 3, chapter 7, pages 217- 274. American Research Press.
    • Martin, A. and Osswald, C. (2006). A new generalization of the proportional conflict redistribution rule stable in terms of desicion. In (Smarandache and Dezert, 2006a), chapter 2, pages 69-88.
    • Martin, A. and Osswald, C. (2007). Toward a combination rule to deal with partial conflict and specificity in belief functions theory. In Proceedings o f International Conference on Information Fusion 2007, Quebec, Canada.
    • Maskell, S. (2008). A bayesian approach to fusing uncertain, imprecise and conflicting information. Information Fusion, 9(2):259-277.
    • Maybeck, P. S. (1979). Stochastic models, estimation, and control, volume 1 of Mathematics in Science and Engineering. Academic Press.
    • Mercier, D., Quost, B., and Denoeux, T. (2005). Contextual discounting of belief functions. In Godo, L., editor, Symbolic and Quantitative Approaches to Reasoning with Uncertainty, volume 3571 of Lecture Notes in Computer Science, pages A12-412. Springer Berlin / Heidelberg.
    • Ordnance Survey (2005). Carmarthen & Kidwelly, [map] 1:25,000, 4 cm to 1 km. Ordanance Survey, Southampton, UK.
    • Ordnance Survey (2007). Brecon Beacons national park, western area, [map] 1:25,000, 4 cm to 1 km. Ordanance Survey, Southampton, UK.
    • Osswald, C. and Martin, A. (2006). Understanding the large family of dempster-shafer theory's fusion operators - a decision-based measure. In Proceedings o f International Conference on Information Fusion 2006.
    • PermaSense Project (2010). PermaSense homepage, http://www.permasense.ch/ [Accessed 14 Nov 2010].
    • Pottie, G. J. and Kaiser, W. J. (2000). Wireless integrated network sensors. Communications o f the ACM, 43(5):51-58.
    • Powell, G. and Marshall, D. (2005). Joint tracking and classification of nonlinear trajectories of multiple objects using the transferable belief model and multi-sensor fusion framework. In Proceedings o f International Conference on Information Fusion 2005, volume 2 .
    • Powell, G., Marshall, D., Smets, P., Ristic, B., and Maskell, S. (2006). Joint tracking and classification of airboume objects using particle filters and the continuous transferable belief model. In Proceedings o f International Conference on Information Fusion 2006.
    • Powell, G., Roberts, M., and Marshall, D. (2010a). Empty set biasing issues in the Transferable Belief Model for fusing and decision making. In Proceedings o f International Conference on Information Fusion 2010, Edinburgh, UK.
    • Powell, G., Roberts, M., and Marshall, D. (2010b). Pitfalls for recursive iteration in set based fusion. In Workshop on the Theory o f Belief Functions, Brest, France.
    • Powell, G., Roberts, M., and Owen, T. (2010c). Transferable belief models for human welfare. In Proceedings o f International Conference on Information Fusion 2010, Edinburgh, UK.
    • Qi, H., Wang, X., Iyengar, S., and Chakrabarty, K. (2001). Multisensor data fusion in distributed sensor networks using mobile agents. In Proceedings o f International Conference on Information Fusion 2001, pages 11-16.
    • Ristic, B., Arulampalam, S., and Gordon, N. (2004a). Beyond the Kalman Filter. Artech House.
    • Ristic, B., Arulampalam, S., and Gordon, N. (2004b). A Tutorial on Particle Filters, chapter 3. In (Ristic et al., 2004a).
    • Ristic, B. and Smets, P. (2004). Belief function theory on the continuous space with an application to model based classification. Proceedings o f Information Processing and Management o f Uncertainty in Knowledge-Based Systems, IPMU, pages 4-9.
    • Robert, C. P. (2007). The Bayesian Choice. Springer, New York, NY, USA, 2nd edition.
    • Roberts, M. and Marshall, D. (2008). Using classification to improve wireless sensor network management with the continuous transferable belief model. In Carapezza, E. M., editor, Unmanned/Unattended Sensors and Sensor Networks V, volume 7112, page 711204, Cardiff, UK. SPIE.
    • Roberts, M., Marshall, D., and Powell, G. (2010). Improving joint tracking and classification with the Transferable Belief Model and terrain information. In Proceedings o f International Conference on Information Fusion 2010, Edinburgh, UK.
    • Sallai, J., Balogh, G., Maroti, M., Ledeczi, A., and Kusy, B. (2004). Acoustic ranging in resource-constrained sensor networks. In Proceedings o f International Conference on Wireless and Mobile Computing.
    • Shepherd, D. and Kumar, S. (2005). Microsensor applications. In Iyengar, S. S. and Brooks, R. R., editors, Distributed Sensor Networks, chapter 2, pages 11-27. Chapman & Hall/CRC.
    • Smarandache, F. and Dezert, J., editors (2006a). Advances and Applications o f DSmT fo r Information Fusion, volume 2. American Research Press, Rehoboth.
    • Smarandache, F. and Dezert, J. (2006b). Proportional conflict redistribution rules for information fusion. In (Smarandache and Dezert, 2006a), chapter 1, pages 3-68.
    • Smets, P. (1990). The combination of evidence in the transferable belief model. IEEE Pattern Analysis and Machine Intelligence, 12:447^458.
    • Smets, P. (1993). Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. International Journal o f Approximate Reasoning, 9:1-35.
    • Smets, P. (2000). Data fusion in the transferable belief model. In Proceedings o f International Conference on Information Fusion 2007, volume 1, pages PS-21-PS-33.
    • Smets, P. (2005). Belief functions on real numbers. International Journal o f Approximate Reasoning, 40(3): 181-223.
    • Smets, P. (2007). Analysing the combination of conflicting belief functions. Information Fusion, 8(4):387-412.
    • Smets, P. and Kennes, R. (1994). The Transferable Belief Model. Artificial Intelligence, 66(2): 191-234.
    • Smets, P. and Ristic, B. (2004). Kalman filter and joint tracking and classification in the TBM framework. In Proceedings o f International Conference on Information Fusion 2004, pages 46-53.
    • Szewczyk, R., Mainwaring, A., Polastre, J., Anderson, J., and Culler, D. (2004). An analysis of a large scale habitat monitoring application. In Proceedings o f the 2nd International Conference on Embedded Networked Sensor Systems, pages 214-226, New York, NY, USA. ACM.
    • TinyOS Community (2010). TinyOS home page, h ttp ://w w w .tin y o s .n e t/ [Accessed 23 Oct 2010].
    • United States (1994). Intelligence Preparation o f the Battlefield: Field Manual 34-130. Headquarters, Dept, of the Army, Washington, D.C.
    • Vanderbilt University (2008). Tracking of radio nodes. vanderbilt.edu/projects/rips [Accessed 19 Oct 2010].
    • Voorbraak, F. (1990). A computationally efficient approximation of dempster-shafer theory. In Gaines, B. R. and Boose, J. H., editors, Machine Learning and Uncertain Reasoning, pages 461-472. Academic Press Ltd., London, UK.
    • Wameke, B., Last, M., Liebowitz, B., and Pister, K. S. J. (2001). Smart dust: Communicating with a cubic-millimeter computer. Computer, 34:44-51.
    • Welch, G. and Bishop, G. (2004). An introduction to the Kalman filter. From the Department Of Computer Science, University of North Carolina-Chapel Hill.
    • Whitehouse, C. D. (2002). The design of calamari: an ad-hoc localization system for sensor networks. Master's thesis, University of California, Berkeley.
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