Distributed Kalman and Particle Filtering

Ali H. Sayed; Petar M. Djurić; Franz Hlawatsch

doi:10.1016/B978-0-12-813677-5.00006-7

Distributed Kalman and Particle Filtering

Ali H. Sayed
, Petar M. Djurić
, Franz Hlawatsch

Electrical Engineering

Swiss Federal Institute of Technology Lausanne
TU Wien

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

10 Scopus citations

Abstract

This chapter discusses distributed Kalman and particle filtering algorithms for state estimation in decentralized multiagent networks. It is assumed that the spatially distributed agents acquire local measurements with information about a time-varying state described by some underlying state-space model. The agents seek to estimate the time-varying state in a decentralized manner. They are only allowed to interact locally by sharing data or estimates with their immediate neighbors. It is shown how the agents can construct local estimates of the state trajectory through a cooperative process of interactions. Both diffusion- and consensus-based strategies are presented.

Original language	English
Title of host publication	Cooperative and Graph Signal Processing
Subtitle of host publication	Principles and Applications
Publisher	Elsevier
Pages	169-207
Number of pages	39
ISBN (Electronic)	9780128136782
ISBN (Print)	9780128136775
DOIs	https://doi.org/10.1016/B978-0-12-813677-5.00006-7
State	Published - Jun 20 2018

Keywords

Diffusion
Distributed Kalman filtering
Distributed particle filtering
Distributed proposal adaptation
Distributed sequential estimation
Likelihood consensus
Target tracking
Wireless agent network

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10.1016/B978-0-12-813677-5.00006-7

Cite this

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title = "Distributed Kalman and Particle Filtering",

abstract = "This chapter discusses distributed Kalman and particle filtering algorithms for state estimation in decentralized multiagent networks. It is assumed that the spatially distributed agents acquire local measurements with information about a time-varying state described by some underlying state-space model. The agents seek to estimate the time-varying state in a decentralized manner. They are only allowed to interact locally by sharing data or estimates with their immediate neighbors. It is shown how the agents can construct local estimates of the state trajectory through a cooperative process of interactions. Both diffusion- and consensus-based strategies are presented.",

keywords = "Diffusion, Distributed Kalman filtering, Distributed particle filtering, Distributed proposal adaptation, Distributed sequential estimation, Likelihood consensus, Target tracking, Wireless agent network",

author = "Sayed, \{Ali H.\} and Djuri{\'c}, \{Petar M.\} and Franz Hlawatsch",

year = "2018",

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TY - CHAP

T1 - Distributed Kalman and Particle Filtering

AU - Sayed, Ali H.

AU - Djurić, Petar M.

AU - Hlawatsch, Franz

PY - 2018/6/20

Y1 - 2018/6/20

N2 - This chapter discusses distributed Kalman and particle filtering algorithms for state estimation in decentralized multiagent networks. It is assumed that the spatially distributed agents acquire local measurements with information about a time-varying state described by some underlying state-space model. The agents seek to estimate the time-varying state in a decentralized manner. They are only allowed to interact locally by sharing data or estimates with their immediate neighbors. It is shown how the agents can construct local estimates of the state trajectory through a cooperative process of interactions. Both diffusion- and consensus-based strategies are presented.

AB - This chapter discusses distributed Kalman and particle filtering algorithms for state estimation in decentralized multiagent networks. It is assumed that the spatially distributed agents acquire local measurements with information about a time-varying state described by some underlying state-space model. The agents seek to estimate the time-varying state in a decentralized manner. They are only allowed to interact locally by sharing data or estimates with their immediate neighbors. It is shown how the agents can construct local estimates of the state trajectory through a cooperative process of interactions. Both diffusion- and consensus-based strategies are presented.

KW - Diffusion

KW - Distributed Kalman filtering

KW - Distributed particle filtering

KW - Distributed proposal adaptation

KW - Distributed sequential estimation

KW - Likelihood consensus

KW - Target tracking

KW - Wireless agent network

UR - https://www.scopus.com/pages/publications/85062225412

U2 - 10.1016/B978-0-12-813677-5.00006-7

DO - 10.1016/B978-0-12-813677-5.00006-7

M3 - Chapter

AN - SCOPUS:85062225412

SN - 9780128136775

SP - 169

EP - 207

BT - Cooperative and Graph Signal Processing

PB - Elsevier

ER -

Distributed Kalman and Particle Filtering

Abstract

Keywords

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