Treffer: Distributed Estimation for Vector Signal in Linear Coherent Sensor Networks
Title:
Distributed Estimation for Vector Signal in Linear Coherent Sensor Networks
Authors:
Source:
IEICE transactions on communications. 95(2):460-465
Publisher Information:
Tokyo: Communications Society, 2012.
Publication Year:
2012
Physical Description:
print, 14 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Codage, codes, Coding, codes, Télécommunications, Telecommunications, Systèmes, réseaux et services de télécommunications, Systems, networks and services of telecommunications, Transmission et modulation (techniques et équipements), Transmission and modulation (techniques and equipments), Services et terminaux de télécommunications, Services and terminals of telecommunications, Télémesure. Télésurveillance. Téléalarme. Télécommande, Telemetry. Remote supervision. Telewarning. Remote control, Accès multiple, Multiple access, Acceso múltiple, Allocation puissance, Power allocation, Asignación potencia, Canal multiple, Multiple channel, Canal múltiple, Codage linéaire, Linear coding, Codificación lineal, Détection signal, Signal detection, Detección señal, Erreur quadratique moyenne, Mean square error, Error medio cuadrático, Estimation canal, Channel estimation, Estimación canal, Estimation paramètre, Parameter estimation, Estimación parámetro, Evaluation performance, Performance evaluation, Evaluación prestación, Programmation convexe, Convex programming, Programación convexa, Réseau capteur, Sensor array, Red sensores, Réseau sans fil, Wireless network, Red sin hilo, Signal aléatoire, Random signal, Señal aleatoria, Simulation numérique, Numerical simulation, Simulación numérica, Traitement réparti, Distributed processing, Tratamiento repartido, Télécommunication sans fil, Wireless telecommunication, Telecomunicación sin hilo, Télédétection, Remote sensing, Teledetección, Vecteur aléatoire, Random vector, Vector aléatorio, Approche multimodèles, Multiple models approach, convex optimization, distributed estimation, power allocation, wireless sensor network
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical and Control Engineering, National Chiao Tung University, 1001 University Road, Hsinchu 300, Tawain, Province of China
ISSN:
0916-8516
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.25489096
Database:
PASCAL Archive
Weitere Informationen
We introduce the distributed estimation of a random vector signal in wireless sensor networks that follow coherent multiple access channel model. We adopt the linear minimum mean squared error fusion rule. The problem of interest is to design linear coding matrices for those sensors in the network so as to minimize mean squared error of the estimated vector signal under a total power constraint. We show that the problem can be formulated as a convex optimization problem and we obtain closed form expressions of the coding matrices. Numerical results are used to illustrate the performance of the proposed method.