Distributed estimation and tracking for radio environment mapping

Ruofan Kong; Wenlin Zhang; Yi Guo

doi:10.1109/ACC.2014.6859200

Distributed estimation and tracking for radio environment mapping

Ruofan Kong, Wenlin Zhang, Yi Guo

Department of Electrical and Computer Engineering

Western Digital

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

3 Scopus citations

Abstract

We study distributed estimation and tracking for radio environment mapping (REM). Comparing to existing REM using centralized methods, we provide a distributed solution eliminating the central station for map construction. Based on the random field model of the REM with shadow fading effects, we adopt consensus-based Kalman filter to estimate and track the temporal dynamic REM variation. The unknown parameters of REM temporal dynamics are estimated by a distributed Expectation Maximization algorithm that is incorporated with Kalman filtering. Our approach features distributed Kalman filtering with unknown system dynamics, and achieves dynamic REM recovery without localizing the transmitter. Simulation results show satisfactory performances of the proposed method where spatial correlated shadowing effects are successfully recovered.

Original language	English
Title of host publication	2014 American Control Conference, ACC 2014
Pages	464-470
Number of pages	7
DOIs	https://doi.org/10.1109/ACC.2014.6859200
State	Published - 2014
Event	2014 American Control Conference, ACC 2014 - Portland, OR, United States Duration: 4 Jun 2014 → 6 Jun 2014

Publication series

Name	Proceedings of the American Control Conference
ISSN (Print)	0743-1619

Conference

Conference	2014 American Control Conference, ACC 2014
Country/Territory	United States
City	Portland, OR
Period	4/06/14 → 6/06/14

Keywords

Control of communication networks
Emerging control applications
Networked control systems

Access to Document

10.1109/ACC.2014.6859200

Cite this

@inproceedings{0905ce154e764b33946501047d704771,

title = "Distributed estimation and tracking for radio environment mapping",

abstract = "We study distributed estimation and tracking for radio environment mapping (REM). Comparing to existing REM using centralized methods, we provide a distributed solution eliminating the central station for map construction. Based on the random field model of the REM with shadow fading effects, we adopt consensus-based Kalman filter to estimate and track the temporal dynamic REM variation. The unknown parameters of REM temporal dynamics are estimated by a distributed Expectation Maximization algorithm that is incorporated with Kalman filtering. Our approach features distributed Kalman filtering with unknown system dynamics, and achieves dynamic REM recovery without localizing the transmitter. Simulation results show satisfactory performances of the proposed method where spatial correlated shadowing effects are successfully recovered.",

keywords = "Control of communication networks, Emerging control applications, Networked control systems",

author = "Ruofan Kong and Wenlin Zhang and Yi Guo",

year = "2014",

doi = "10.1109/ACC.2014.6859200",

language = "English",

isbn = "9781479932726",

series = "Proceedings of the American Control Conference",

pages = "464--470",

booktitle = "2014 American Control Conference, ACC 2014",

note = "2014 American Control Conference, ACC 2014 ; Conference date: 04-06-2014 Through 06-06-2014",

}

TY - GEN

T1 - Distributed estimation and tracking for radio environment mapping

AU - Kong, Ruofan

AU - Zhang, Wenlin

AU - Guo, Yi

PY - 2014

Y1 - 2014

N2 - We study distributed estimation and tracking for radio environment mapping (REM). Comparing to existing REM using centralized methods, we provide a distributed solution eliminating the central station for map construction. Based on the random field model of the REM with shadow fading effects, we adopt consensus-based Kalman filter to estimate and track the temporal dynamic REM variation. The unknown parameters of REM temporal dynamics are estimated by a distributed Expectation Maximization algorithm that is incorporated with Kalman filtering. Our approach features distributed Kalman filtering with unknown system dynamics, and achieves dynamic REM recovery without localizing the transmitter. Simulation results show satisfactory performances of the proposed method where spatial correlated shadowing effects are successfully recovered.

AB - We study distributed estimation and tracking for radio environment mapping (REM). Comparing to existing REM using centralized methods, we provide a distributed solution eliminating the central station for map construction. Based on the random field model of the REM with shadow fading effects, we adopt consensus-based Kalman filter to estimate and track the temporal dynamic REM variation. The unknown parameters of REM temporal dynamics are estimated by a distributed Expectation Maximization algorithm that is incorporated with Kalman filtering. Our approach features distributed Kalman filtering with unknown system dynamics, and achieves dynamic REM recovery without localizing the transmitter. Simulation results show satisfactory performances of the proposed method where spatial correlated shadowing effects are successfully recovered.

KW - Control of communication networks

KW - Emerging control applications

KW - Networked control systems

UR - http://www.scopus.com/inward/record.url?scp=84905715403&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84905715403&partnerID=8YFLogxK

U2 - 10.1109/ACC.2014.6859200

DO - 10.1109/ACC.2014.6859200

M3 - Conference contribution

AN - SCOPUS:84905715403

SN - 9781479932726

T3 - Proceedings of the American Control Conference

SP - 464

EP - 470

BT - 2014 American Control Conference, ACC 2014

T2 - 2014 American Control Conference, ACC 2014

Y2 - 4 June 2014 through 6 June 2014

ER -

Distributed estimation and tracking for radio environment mapping

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