Journal ArticleDOI
Distributed particle filtering in agent networks: A survey, classification, and comparison
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TLDR
A survey, classification, and comparison of various DPF approaches and algorithms available to date are presented, with emphasis on decentralized ANs that do not include a central processing or control unit.Abstract:
Distributed particle filter (DPF) algorithms are sequential state estimation algorithms that are executed by a set of agents. Some or all of the agents perform local particle filtering and interact with other agents to calculate a global state estimate. DPF algorithms are attractive for large-scale, nonlinear, and non-Gaussian distributed estimation problems that often occur in applications involving agent networks (ANs). In this article, we present a survey, classification, and comparison of various DPF approaches and algorithms available to date. Our emphasis is on decentralized ANs that do not include a central processing or control unit.read more
Citations
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Journal ArticleDOI
Resampling Methods for Particle Filtering: Classification, implementation, and strategies
TL;DR: The state of the art of resampling methods was reviewed and the methods were classified and their properties were compared in the framework of the proposed classifications to provide guidelines to practitioners and researchers.
Journal ArticleDOI
A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems
TL;DR: A review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models is presented and some challenges are raised to guide the future research.
Journal ArticleDOI
Distributed Event-Triggered Estimation Over Sensor Networks: A Survey
TL;DR: A survey of recent advances in distributed event-triggered estimation for dynamical systems operating over resource-constrained sensor networks, including distributed grid-connected generation systems and target tracking systems is provided.
Journal ArticleDOI
Stability of consensus extended Kalman filter for distributed state estimation
Giorgio Battistelli,Luigi Chisci +1 more
TL;DR: It is shown that the considered family of distributed Extended Kalman Filters enjoys local stability properties, under minimal requirements of network connectivity and system collective observability.
Journal ArticleDOI
Distributed Localization and Tracking of Mobile Networks Including Noncooperative Objects
TL;DR: This work proposes a Bayesian method for distributed sequential localization of mobile networks composed of both cooperative agents and noncooperative objects that provides a consistent combination of cooperative self-localization (CS) and distributed tracking (DT).
References
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Fundamentals of statistical signal processing: estimation theory
TL;DR: The Fundamentals of Statistical Signal Processing: Estimation Theory as mentioned in this paper is a seminal work in the field of statistical signal processing, and it has been used extensively in many applications.
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A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
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Consensus and Cooperation in Networked Multi-Agent Systems
TL;DR: A theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, time-delays, and performance guarantees is provided.
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Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
Journal Article
Optimal Filtering
TL;DR: This book helps to fill the void in the market and does that in a superb manner by covering the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation.