Asynchronous distributed state estimation based on covariance intersection

• Autorzy: Kowalczuk Z. , Domżalski M.
• Języki publikacji: EN

Abstrakty

EN

In this paper, the problem of state estimation in an asynchronous distributed multi-sensor system is considered. In such a system, the state of an object of interest is estimated by a group of local estimators. Each local estimator, based on a Kalman filter, performs fusion of data from its local sensor and other (local) processors to compute possibly best state estimates. In performing data fusion, however, two important issues need to be addressed: unknown correlation between data in local processors and the problem of asynchronism of local processors. In this paper, a multi-sensor asynchronous estimation algorithm is presented. The problem of unknown correlation is solved by a covariance intersection method. To deal with asynchronous data a continuous-time stochastic object model is introduced. Simulated experiments illustrate the effectiveness of the proposed approach.

Słowa kluczowe

EN

systems theory; dynamical systems; distributed systems; state estimation; Kalman filtering

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2009
• Tom: Vol. 35, no 1
• Strony: 23--30
• Opis fizyczny: Bibliogr. 10 poz., wykr.

Twórcy

autor

Kowalczuk Z.

autor

Domżalski M.

• WETI (KSD), Gdańsk University of Technology, ul. Narutowicza 11/12, 80-952 Gdańsk, Poland,

Bibliografia

• [1] Bar-Shalom Y., Li X.R., Estimation and Tracking: Principles, Techniques, and Software, Artech House, Boston, MA, 1993.
• [2] Bar-Shalom Y., Li X.R., Multitarget-Multisensor Tracking: Principles and Techniques, CT:YBS Publishing, Storrs 1995.
• [3] Hall D.L., Llinas J., Handbook of Multisensor Data Fusion, CRC Press, Boca Raton, 2001.
• [4] Julier S.J., Uhlmann J.K., A non-divergent estimation algorithm in the presence of unknown correlations, Proceedings of the American Control Conference, 1997, pp. 2369-2373.
• [5] Kalman R.E., A new approach to linear filtering and prediction problems, Transactions of the ASME, Journal of Basic Engineering, 1960, Vol. 82, pp. 34-45.
• [6] Oksendal B., Stochastic Differential Equations, Springer-Verlag, Heidelberg, 1998.
• [7] Kowalczuk Z., Domżalski M., Covariance intersection method for distributed asynchronous multi-sensor state estimation, Proceedings of the 14th International Congress of Cybernetics and Systems of WOSC, Wrocław, CD-ROM, 2008, pp. 191-200.
• [8] http://www.python.org/
• [9] http://numpy.scipy.org/
• [10] http://stanford.edu/~boyd/cvx/