The estimation problem for autoregressive moving average(ARMA) signals with stochastic packet dropouts in
the networked environment is studied, where the phenomenon of the packet dropouts is described by a Bernoulli distributed
random variable. The estimation problem for ARMA signals with packet dropouts is converted to the one for the state and
white noise of state space model with packet dropouts by the transform from ARMA model to the state space model. Based
on projection theory, the optimal linear estimators for the state and white noise are derived in the linear minimum variance
sense respectively. Further, the estimators for ARMA signals are obtained. Simulation results show that, in the presence
of packet dropouts, compared with the previous optimal estimation algorithms based on the complete data, the proposed
algorithm has optimality and effectiveness.