Distributed filtering with random sampling and delay

The problem of estimation and filtering in a distributed sensor environment is considered. The sensors obtain measurements about target trajectories at random times which transmit to the fusion center. The measurements arrive at the fusion with random delays which can be due to queuing at the sensor buffer and to delays in the transmission time as well as in the propagation time. The fusion generates estimates of the target tracks using the received measurements. The measurements are received from the sensors at random times, can have unknown time-origin and can arrive out of sequence. Optimal filters for the estimation of target tracks based on measurements of uncertain origin received by the fusion at random times and out of sequence have been derived for the cases of random sampling, random delay, and both random sampling and random delay. It is shown that the optimal filters constitute an extension to the Kalman filter to account for the uncertainty involved with the data time-origin