Recursive Estimation for System With Random Transmission Loss and Censored Measurement

Abstract

Censored measurements are frequently occurring phenomena in sensor systems with the characteristics of output dead-zone or sensor saturation. In censoring sensor network systems, random transmission loss is inevitable, which results in the two measurement uncertainties occurring simultaneously. In this study, we address this problem and attempt to present an efficient solution. First, the random transmission losses are described by a set of Bernoulli random variables, and then the censored random matrix and translation transformation are used to modify the traditional Tobit censored measurement model, which includes calculating the probability that the latent measurement is not censored when the transmission is lost and is only pure noise. Then, an optimal recursive filtering algorithm for a system with the two uncertain measurements is derived by using the innovation analysis method and the full expectation rule. The effectiveness and superiority of the algorithm is verified by simulating an oscillator model and comparing with other existing algorithms.