期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 51, 期 9, 页码 5762-5772出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2019.2956259
关键词
Covariance matrices; Estimation error; Noise measurement; Sensor phenomena and characterization; Mathematical model; Correlated noise (CN); cross-covariance matrix; distributed fusion filter (DFF); multisensor multirate (MSMR) system; packet dropout (PD) compensation
资金
- National Natural Science Foundation of China [61573132]
- Basic Research Fund of Heilongjiang University [KJCX201807, RCYJTD201806]
- Key Laboratory of Information Fusion Estimation and Detection in Heilongjiang Province, China
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.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据