Vincent Sircoulomb, Ghaleb Hoblos, Houcine Chafouk, and José Ragot
Nonlinear filtering, Kalman filtering, hardware redundancy, observability, gramian, sensor network
This article deals with a method for providing the best estimate as possible of a nonlinear system state. It is concerned with both finding the most accurate state estimator and obtaining the best measurements as possible, thanks to the redundancy of sensors under hardware and economical constraints. These choices are algorithmically done. The selection of the state estimator takes place according to the variances of estimation errors. Concerning the elaboration of hardware redundancy, it is done by computing a new criterion evaluating estimation quality with respect to sensors losses. The proposed algorithm is then applied on the Lorenz system, parameterized to present a chaotic behaviour, which constitutes a priori difficult situation.
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