A survey on distributed filtering, estimation and fusion for nonlinear systems with communication constraints: new advances and prospects

Abstract

In this paper, some recent results on the distributed filtering, estimation and fusion algorithms for nonlinear systems with communication constraints are reviewed. First, some network-induced phenomena and communication protocols in the networked environment are recalled. Second, the recent advances of distributed fusion algorithms for nonlinear systems are discussed, which include distributed fusion extended Kalman filtering, distributed fusion unscented Kalman filtering, distributed fusion cubature Kalman filtering, distributed fusion particle filtering. Subsequently, some new distributed filtering, estimation and fusion algorithms for nonlinear systems subject to communication constraints are summarized and discussed. Finally, conclusion is given and several possible research topics on distributed filtering, estimation and fusion for nonlinear networked systems are pointed out.

Distributed Fusion Estimation in Network Systems Subject to Random Delays and Deception Attacks

“…Next, to facilitate obtaining Equations ( 23)-( 27), we determine an expression for the one-stage observation predictor of y (j) s based on the observations of sensor i, which will be denoted by y (j/i) s/s−1 . This expression is easily obtained from (7), taking into account the model assumptions and (11) for x (j) s/s−1 and x…”

“…Consequently, this method usually has stronger fault tolerance and lower calculation burdens than the centralised approach. Accordingly, the distributed fusion estimation problem has been of particular interest to researchers, and a large number of distributed fusion estimation algorithms have been developed, using different techniques, in the context of WSNs (see [1][2][3][4][5][6][7] and references therein).…”

Non-fragile distributed state estimation over sensor networks subject to DoS attacks: the almost sure stability

“…Sensor networks consisting of lots of nodes are commonly utilised to monitor physical or environmental conditions, and also serve the requirements of control, estimation and fault detection of networked systems (Chen, Ding, et al, 2019;Hu et al, 2020;Ma et al, 2018Ma et al, , 2019. In recent years, benefiting from the enhancement of sensing and communication capabilities of sensor nodes, they have a more wide-scope domain of applications such as environment monitoring, health care applications, intelligent transportation, robotics, and industrial and manufacturing automation (Ding et al, 2020).…”

Distributed Fusion Estimation with Sensor Gain Degradation and Markovian Delays

“…Distributed fusion estimation, on the other hand, does not generally provide optimal estimators, but reduces the computational load and is usually more suitable for large-scale sensor networks with random transmission failures, because of its parallel structure. Due to these advantages, the use of distributed fusion estimation in multiple-sensor systems has attracted considerable interest in recent years, with various approaches being taken; for detailed information, see [1][2][3][4][5][6][7] and the references therein.…”

“…(i) k,S = 0, S ≥ k. Next, we derived expression(21) for Φ (ij) k,S . For S = k − 1, k, k + 1, expression (21) of Φ (ij) k,S , is clear from (3) taking into account that J x(ij) S−1,S = E O x(ij) S−1 µ (j)T S .In order to calculate Φ (ij) k,S for S ≥ k + 2, we use(7) for x To determine the first expectation in (A4), we use (2) for y (j)S and taking into account that by the OPL E x (ji)T S−a,S−2 , and, again, from (7) for x To compute the second expectation in (A4), first, from (6), we write the observation predictor as y using expression (7) of the local smoother, after some manipulation, we obtain E x…”