State estimation method based on evidential reasoning rule

This paper presents a new approach to dynamic system state estimation under bounded noises via the Evidential Reasoning(£K) rule. This method regards the dynamic system equations and the actual observations of the system states as two information sources. The random set description of evidence and the extension principle of random set are used to recursively generate state evidence and observation evidence respectively from the two information sources and to propagate them in system equations. At each time step, the ER rule is used to fuse the two pieces of evidence in observation domain and then the fused result is transformed to state domain by the extension principles. Pignistic expectation of the fused result is calculated as state estimation value. Compared with the estimation method using interval analysis and evidence theory given by Nassreddine, the proposed approach makes estimation results more accurate by using fusion mechanism of the ER rule considering weight and reliability of evidence. The method is shown to have better performances in an application to liquid level estimation of industrial level apparatus than does the Nassreddine´s method.