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Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey

Derui Ding; Qing-Long Han; Xiaohua Ge

Kybernetika (2020)

  • Volume: 56, Issue: 1, page 5-34
  • ISSN: 0023-5954

Abstract

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Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research.

How to cite

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Ding, Derui, Han, Qing-Long, and Ge, Xiaohua. "Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey." Kybernetika 56.1 (2020): 5-34. <http://eudml.org/doc/297098>.

@article{Ding2020,
abstract = {Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research.},
author = {Ding, Derui, Han, Qing-Long, Ge, Xiaohua},
journal = {Kybernetika},
keywords = {distributed filtering; sensor networks; non-Gaussian noises; network-induced phenomena; communication protocols},
language = {eng},
number = {1},
pages = {5-34},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey},
url = {http://eudml.org/doc/297098},
volume = {56},
year = {2020},
}

TY - JOUR
AU - Ding, Derui
AU - Han, Qing-Long
AU - Ge, Xiaohua
TI - Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey
JO - Kybernetika
PY - 2020
PB - Institute of Information Theory and Automation AS CR
VL - 56
IS - 1
SP - 5
EP - 34
AB - Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research.
LA - eng
KW - distributed filtering; sensor networks; non-Gaussian noises; network-induced phenomena; communication protocols
UR - http://eudml.org/doc/297098
ER -

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