Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

Abstract

In this paper, some recent advances on the estimation, filtering and fusion for networked systems are reviewed. Firstly, the network-induced phenomena under consideration are briefly recalled including missing/fading measurements, signal quantization, sensor saturations, communication delays, and randomly occurring incomplete information. Secondly, the developments of the estimation, filtering and fusion for networked systems from four aspects (linear networked systems, nonlinear networked systems, complex networks and sensor networks) are reviewed comprehensively. Subsequently, some recent results on the estimation, filtering and fusion for systems with the network-induced phenomena are reviewed in great detail. In particular, some latest results on the multiobjective filtering problems for time-varying nonlinear networked systems are summarized. Finally, conclusions are given and several possible research directions concerning the estimation, filtering, and fusion for networked systems are highlighted.

Multiple Model Ballistic Missile Tracking With State-Dependent Transitions and Gaussian Particle Filtering

“…From the algorithm development aspect, we will investigate the combination of the state-dependent model switching-based multiple model framework with other filtering techniques to deal with the particle loss problem, such as the particle flow algorithm as in [29] or exploiting various numbers of particles in every mode for filtering. Finally, we will consider a more challenging scenario as in [30] and [31], to track the BM by a sensor-networked system considering the possible network-induced phenomena such as missing/fading measurements, sensor saturations, communication delays, and randomly occurring incomplete information.…”

A survey on set-membership filtering for networked control systems under communication protocols

“…Such network-induced phenomena include, but are not limited to, signal quantization (Dong, Wang, Ding, & Gao, 2015;Wang, Dong, Shen, & Gao, 2013), missing/fading measurements (Ding, Wang, Lam, & Shen, 2015;Ding, Wang, Shen, & Dong, 2015a,1;Wang, Ding, Dong, & Shu, 2013), communication delays (Hu, Chen, & Du, 2014;Shen, Wang, & Tan, 2017;Wang, Liu, Shen, Alsaadi, & Abdullah, 2017), variable sampling/transmission intervals, sensor saturations and out-of-sequencemeasurement updates (Hu, Wang, Chen, & Alsaadi, 2016;Shen et al, 2009). In particular, a class of emerging network-induced phenomenon (randomly occurring incomplete information) has obtained preliminary research interest in the field of signal processing and control.…”

Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

“…However, in a network context, usually the restrictions of the physical equipment or the uncertainties in the external environment, inevitably cause problems in both the sensor outputs and the transmission of such outputs, that can worsen dramatically the quality of the fusion estimators designed without considering these drawbacks [6]. Multiplicative noise uncertainties and missing measurements are some of the random phenomena that usually arise in the sensor measured outputs and motivate the design of new estimation algorithms (see e.g., [7][8][9][10][11], and references therein).…”