Abstract
We consider the problem of distributed state estimation of continuous-time stochastic processes using a network of processing nodes. Each node performs measurement and estimation using the Kalman filtering technique, communicates its results to other nodes in the network, and utilizes similar results from the other nodes in its own computations. We assume that the connection graph of the network is not complete, i.e. not all nodes are directly connected, and that the nodes work asynchronously, i.e. they perform measurement and estimation in time moments independent of each other. We evaluate the impact of the way of propagation of information from most precise nodes over the network on the overall performance of distributed estimation.
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References
Bar-Shalom, Y., Li, X.R.: Estimation and Tracking: Principles, Techniques, and Software. Artech House, Boston (1993)
Bar-Shalom, Y., Li, X.R.: Multitarget-Multisensor Tracking: Principles and Techniques. YBS Publishing, Storrs (1995)
Carli, R., Chiuso, A., Schenato, L., Zampieri, S.: Distributed kalman filtering based on consensus strategies. IEEE Journal on Selected Areas in Communications 26(4), 622–633 (2008)
Cattivelli, F., Sayed, A.: Diffusion strategies for distributed Kalman filtering and smoothing. IEEE Transactions on Automatic Control 55(9), 2069–2084 (2010)
Chen, L., Arambel, P.O., Mehra, R.K.: Estimation under unknown correlation: covariance intersection revisited. IEEE Transactions on Automatic Control, AC-47(11), 1879–1882 (2002)
Hall, D.L., Llinas, J.: An introduction to multisensor data fusion. Proceedings of the IEEE 85(1), 6–23 (1997)
Hall, D.L., Llinas, J.: Handbook of Multisensor Data Fusion. CRC, Boca Raton (2001)
Julier, S., Uhlmann, J.: A non-divergent estimation algorithm in the presence of unknown correlations. In: Proc. of the American Control Conference, pp. 2369–2373 (1997)
Kalman, R.: A new approach to linear filtering and prediction problems. Transactions of the ASME, Journal of Basic Engineering 82, 34–45 (1960)
Karatzas, I., Shreve, S.E.: Brownian Motion and Stochastic Calculus. Springer, NY (1991)
Kowalczuk, Z., Domżalski, M.: Asynchronous distributed state estimation based on a continuous-time stochastic model. International Journal of Adaptive Control and Signal Processing 26(5), 384–399 (2012)
Kowalczuk, Z., Domżalski, M.: Optimal asynchronous estimation of 2D Gaussian Markov processes. Int. Journal of Systems Science 43(8), 1431–1440 (2012)
Liggins, M., Chong, C., Kadar, I., Alford, M., Vinnicola, V., Thomopoulos, S.: Distributed fusion architectures and algorithms for target tracking. Proceedings of the IEEE 85(1), 95–107 (1997)
Oksendal, B.: Stochastic Differential Equations: An Introduction with Applications. Springer, Berlin (2003)
Olfati-Saber, R.: Distributed Kalman filtering for sensor networks. In: Proc. of the 46th IEEE Conference on Decision and Control, New Orleans, USA, pp. 5492–5498 (2007)
Olfati-Saber, R., Shamma, J.: Consensus filters for sensor networks and distributed sensor fusion. In: Proc. of the 44th IEEE Conference on Decision and Control, and 2005 European Control Conference (CDC-ECC), pp. 6698–6703 (2005)
Rao, B., Durrant-Whyte, H., Sheen, J.: A fully decentralized multi-sensor system for tracking and surveillance. The International Journal of Robotics Research 12(1), 20–44 (1993)
Ribeiro, A., Giannakis, G.B., Roumeliotis, S.: SOI-KF: Distributed Kalman filtering with low-cost communications using the sign of innovations. IEEE Transactions on Signal Processing 54(12), 4782–4795 (2006)
Rogers, L., Williams, D.: Diffusion, Markov Processes and Martingales: Itô Calculus, vol. 2. Cambridge University Press, UK (2000)
Speranzon, A., Fischione, C., Johansson, K.: Distributed and collaborative estimation over wireless sensor networks. In: Proc. of the 45th IEEE Conference on Decision and Control, San Diego, USA, pp. 1025–1030 (2006)
Xiao, L., Boyd, S., Lall, S.: A scheme for robust distributed sensor fusion based on average consensus. In: Proc. of the 4th International Symposium on Information Processing in Sensor Networks, Los Angeles, USA, pp. 63–70 (2005)
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Kowalczuk, Z., Domżalski, M. (2014). Distributed State Estimation Using a Network of Asynchronous Processing Nodes. In: Korbicz, J., Kowal, M. (eds) Intelligent Systems in Technical and Medical Diagnostics. Advances in Intelligent Systems and Computing, vol 230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39881-0_38
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DOI: https://doi.org/10.1007/978-3-642-39881-0_38
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