Distributed Fusion Estimation for Multisensor Multirate Systems With Packet Dropout Compensations and Correlated Noises

This article investigates distributed fusion estimation problems for multisensor multirate (MSMR) stochastic systems with correlated noises (CNs) and packet dropouts (PDs). The state updates at the fast rate while sensors uniformly sample at positive integer multiples of the state updating period. Different sensors may have different sampling rates. The system noise and measurement noises are auto- and cross-correlated at the same instant. The phenomenon of PDs randomly occurs during data transmission from a sensor to a data processor through unreliable networks. A recent developed compensation strategy that a predictor of a lost packet is employed as a compensator is adopted to optimize the tracking process. First, an optimal linear local filter (LF) for each sensor at measurement sampling points (MSPs) is presented by using an innovation analysis approach. Then, a local estimator (LE) at state updating points (SUPs) is proposed by filtering or prediction based on the LF at MSPs. Furthermore, estimation error cross-covariance matrices (CCMs) between arbitrary two LEs at SUPs are deduced, which can recursively be calculated by three joint difference equations. Finally, a distributed fusion filter (DFF) weighted by matrices in the sense of linear unbiased minimum variance (LUMV) is addressed. Period steady-state (PSS) property of the LEs, CCMs, and DFF is proved. A simulation example verifies the effectiveness of algorithms.

Automated summary

This article investigates distributed fusion estimation problems for multisensor multirate stochastic systems with correlated noises and packet dropouts. It presents optimal linear local filters and local estimators, deduces estimation error cross-covariance matrices, and addresses a distributed fusion filter weighted by matrices for linear unbiased minimum variance. The effectiveness of the algorithms is verified through a simulation example.