Encoding-Decoding-Based Recursive Filtering for Nonlinear Systems Subject to Multiple Missing Measurements

This paper investigates the recursive filtering (RF) problem for a class of time-varying nonlinear systems with an encoding-decoding mechanism. Multiple missing measurements (MMMs) phenomena are considered in the data transmission process, which is mainly caused by the transient failure of the sensor or external interference. A set of reciprocally independent Bernoulli random variables with uncertain probabilities are used to characterise the stochastic occurrence of MMMs. In wireless communication networks, a dynamic-quantization-based encoding-decoding mechanism is introduced to encrypt the measurements, safeguarding important data from potential theft. The connection between the real measurement and the decoded output is established through a bounded uncertainty. The purpose of this paper is to devise a RF algorithm for the considered phenomenon. Meanwhile, the filtering error covariance matrix is minimized by calculating the appropriate filtering gain. Finally, the effectiveness and practicality of the proposed filtering algorithm are verified by a simulation example.