Abstract
In this study, the authors focus on estimating the unknown constant latency probability of non-linear systems with one-step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probability estimation algorithm is proposed based on an expectation maximisation approach to obtain an approximate ML estimation of latency probability. The proposed algorithm consists of expectation step (E-step) and the maximisation step (M-step). In the E-step, the expectation of the complete data log-likelihood function is approximately computed based on the currently estimated latency probability, and in the M-step, the approximate expectation is maximised using the Newton approach. The efficacy of the proposed algorithm is illustrated in a numerical example concerning univariate non-stationary growth model.
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