Discrete estimation algorithms for randomly sampled stochastic signals

Sequential estimation of the state of a linear continuous system from randomly sampled noisy measurements is considered. Reformulation of the original problem leads to an interesting correlation between plant and measurement noise, which requires a special sequential algorithm. Effects on estimation error variance by varying the ratio of the discrete sample period to the average of the random sample period are discussed. An example is given to illustrate the techniques presented.