State Estimation of Markov Jump Neural Networks With Sensor Resolution and Innovation Saturation: A Binary-Encoding Scheme

As one of the most basic specifications for many types of sensors, sensor resolution has been largely overlooked in a multitude of state estimation studies. Under the binary-encoding mechanism, this article deals with the outlier-resilient state estimation problem of Markov jump neural networks (MJNNs) with sensor resolution. An improved binary-encoding procedure, capable of assigning distinct bit lengths to different MJNN modes, is proposed to accommodate diverse physical constraints and limited network resources. Building on this procedure, a mode-dependent state estimation scheme embedded with a saturation function is put forward to alleviate the by-effects of external disturbances, decoding errors, and measurement outliers. Sufficient conditions are derived to guarantee the exponential ultimately boundedness of estimation errors. Lastly, simulation experiments are carried out to demonstrate the applicability of the proposed method.