Abstract
Considering the issue of decentralized collaborative target tracking architecture in the sea battlefield for the wide perception and complex sensor networks, firstly a new target calculation mechanism of the collaborative target tracking is proposed. To increase the performance of robustness, self-organization and dynamic adaptability for the information dissemination and sharing strategy, research methods and technical route are discussed in detail on the basis of complex network theory. In order to effectively deal with different kinds of information sources in the sensor networks, a generalized fusion machine is presented by way of DSmT model. The proposed architecture is applicable to the further research of collaborative target tracking technologies in the sea battlefield.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Llinas, J.: Studying the complexities in distributed object tracking system. In: Proceeding of Int. Conf. on System, Man and Cybernetics, Washington, vol. 2, pp. 2035–2041 (2003)
Lin, X., Kirubarajan, T., Bar-Shalom, Y.: Exact multisensor dynamic bias estimation with local tracks. IEEE Trans. on AES 40(2), 576–590 (2004)
Hu, H.-T., Jing, Z.-L., Hu, S.-Q.: An Unscented Kalman Filter Based Multi-Platform Multi-Sensor Registration. J. Shanghai Jiaotong University 39(9), 1518–1521 (2005)
Wu, Z.-M., Ren, S.-J., Liu, X.: Research on Collaborative Registration Algorithm for Radar System Error. Acta Armamentarii 29(10), 1192–1196 (2008)
Hwang, J.S.: Analysis of Effectiveness of CEC (Cooperative Engagement Capability) Using Schutzer’s C2 Theory (Master Thesis). Naval Postgraduate School (2003)
Chen, J.-Y.: Chinese Modern Geodetic Datum——Chinese Geodetic Coordinate System 2000(CGCS 2000) and Its Frame. Acta Geodaetica et Cartographica Sinica 37(3), 269–271 (2008)
Julier, S., Uhlmann, J., Durrant-Whyte, H.F.: A New method for the nonlinear transformation of means and covariance in filters and estimators. IEEE Trans. Automatic Control 45(3), 477–482 (2000)
Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear-non-Gaussian Bayesian state estimation. IEEE Proceedings Radar and Signal Processing 140(2), 107–113 (1993)
Ostwald, J., Lesser, V., Abdallah, S.: Combinatorial auction for resource allocation in a distributed sensor network. In: RTSS (2005)
RuairÃ, M., Keane, M.: The Dynamics Regions Theory: Role Based Partitioning for Sensor Network Optimization. In: ATSN. AAMAS Workshop, pp. 25–30 (2007)
Waldock, A., Nicholson, D.: Cooperative Decentralized Data Fusion Using Probability Collectives. In: ATSN. AAMAS Workshop, pp. 47–53 (2007)
Ren, Q.-Q., Li, J.-Z., Gao, H., Cheng, S.-Y.: A Two-Phase Sleep Scheduling Based Protocol for Target Tracking in Sensor Networks. Chinese Journal of Computers 32(10), 1971–1979 (2009)
Pavlin, G.: Multi agent systems for flexible and robust Bayesian information fusion. In: 10th International Conference on Information Fusion (2007)
Watts, D., Strogatz, S.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)
Xu, Y., Lewis, M., Sycara, K., Scerri, P.: An Efficient Information Sharing Approach For Large Scale Multi-agent Team. In: Proceeding, the 11th Int. Conf. on Information Fusion, Cologne, Germany, pp. 1206–1213 (2008)
Moreno, Y., Nekovee, M., Vespignani, A.: Efficiency and reliability of epidemic data dissemination in complex networks. Phys. Rev. EÂ 69, 055101 (2004)
Zhang, S.-K., Cui, Z.-M., Gong, S.-R., Sun, Y.: An Investigation on Local Area Control of Compromised Nodes Spreading in Wireless Sensor Networks. Acta Electronica Sinica 37(4), 877–883 (2009)
Rhodes, Luenberger, D.: Differential games with imperfect state information. IEEE Transactions on Automatic Control 14(1), 29–38 (1969)
Dubois, D., Prade, H.: Possibility Theory: An approach to the computerized processing of uncertain. Plenum Press, New York (1988)
Huang, X., Li, X., Dezert, J., Wang, M.: A Fusion Machine Based on DSmT and PCR5 for Robot’s Map Reconstruction. International Journal of Information Acquisition 13(3), 201–213 (2006)
Li, X., Zhu, B., Dezert, J., Dai, X.: An Improved Fusion Machine for Robot Perception with sonar sensors. Journal of Intelligent and Robotic Systems (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Duan, L., Feng, K., Luo, B., Li, YN. (2012). Research of Decentralized Collaborative Target Tracking Architecture in the Sea Battlefield for the Complex Sensor Networks. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-33478-8_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33477-1
Online ISBN: 978-3-642-33478-8
eBook Packages: Computer ScienceComputer Science (R0)