Distributed State and Fault Fusion Filtering for Nonlinear Systems Subject to Quantization Effects

In this paper, the distributed state and fault fusion filtering problem is discussed for a class of multi-sensor networked systems (MSNSs) with randomly occurring quantized measurements (ROQMs). A series of random variables obeying the Bernoulli distribution are adopted to depict the phenomenon of the ROQMs and the fault signal is assumed to appear with an unknown constant type. The aim of this paper is to provide the state and fault fusion filtering algorithm by using the inverse covariance intersection fusion method. Moreover, a proper filter gain is designed by minimizing the local upper bound of filtering error covariance. Finally, the effectiveness of the presented fusion method is verified by using an illustrative example.