Encoding–decoding-based fusion estimation with filter-and-forward relays and stochastic measurement delays

This paper addresses the encoding–decoding-based fusion estimation problem for a class of systems with signal relays and stochastic measurement delays. A set of Bernoulli distributed random variables is used to model the randomly occurring measurement delays. To enhance the system performance and extend the transmission distance, a filter-and-forward relay is adopted in the transmission link. To handle the limited bandwidth of the communication channels, an encoding–decoding scheme based on a probabilistic quantizer is proposed for the sensor–relay and relay–estimator channels. For each relay, a local filter is constructed with a proper filter gain at each time step with aim to minimize an upper bound of the local estimation error covariance. The effects of the encoding–decoding scheme on the error covariance are also analyzed quantitatively. The estimates obtained from the local filters are then fused at the remote estimator using the covariance intersection fusion strategy. Finally, a target tracking simulation example is presented to demonstrate the effectiveness of the proposed framework.