Abstract
This paper is concerned with developing particle filters for Boolean networks with multi-step randomly-delayed measurements. Based on the semi-tensor product of matrices and dummy selection matrices, a generalized measurement model including multi-step randomly-delayed measurements is introduced. Besides, multi-sensor fusion particle filtering is proposed and two recursive algorithms are designed to minimize the mean-square estimation errors by fusing multi-sensor measurements. Simulation results show the effectiveness of the proposed multi-sensor fusion particle filters. It can be seen from the simulation results that the estimation performance of multi-sensor fusion has been improved compared to that of one sensor.
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