This paper addresses the least-squares linear filtering problem of signals
from measurements which may be randomly delayed by one or two sampling
times. The delays are modelled by a homogeneous discrete-time
Markov chain to capture the dependence between them. Assuming that
the evolution equation generating the signal is not available and that only
the first- and second-order moments of the processes involved in the observation
model are known, a recursive filtering algorithm is derived using an
innovation approach. Recursive formulas for the filtering error variances are
also obtained to measure the precision of the proposed estimators.This research is supported by Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional
FEDER (grant no. MTM2014-52291-P)
