• Power Allocation Strategies fo...

Result: Power Allocation Strategies for Target Localization in Distributed Multiple-Radar Architectures

Title:
Power Allocation Strategies for Target Localization in Distributed Multiple-Radar Architectures
Authors:
GODRICH, Hana, PETROPULU, Athina P, POOR, H. Vincent
Source:
IEEE transactions on signal processing. 59(7):3226-3240
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2011.
Publication Year:
2011
Physical Description:
print, 28 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, Gestion ressources, Resource management, Gestión recursos, Algorithme, Algorithm, Algoritmo, Allocation puissance, Power allocation, Asignación potencia, Allocation ressource, Resource allocation, Asignación recurso, Antenne, Antenna, Antena, Décomposition domaine, Domain decomposition, Descomposición dominio, Détection cible, Target detection, Detección blanco, Erreur quadratique moyenne, Mean square error, Error medio cuadrático, Estimation paramètre, Parameter estimation, Estimación parámetro, Evaluation performance, Performance evaluation, Evaluación prestación, Inégalité Cramer Rao, Cramer Rao inequality, Desigualdad Cramer Rao, Localisation, Localization, Localización, Méthode décomposition, Decomposition method, Método descomposición, Métrique, Metric, Métrico, Optimisation, Optimization, Optimización, Programmation non convexe, Non convex programming, Programación no convexa, Radar multistatique, Multistatic radar, Radar multiestático, Relaxation, Relajación, Système MIMO, MIMO system, Sistema MIMO, Traitement réparti, Distributed processing, Tratamiento repartido, Traitement signal, Signal processing, Procesamiento señal, Cramer-Rao bound (CRB), multiple-input multiple-output (MIMO) radar, multistatic radar, nonconvex optimization, resource allocation, target localization
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, United States
Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, United States
ISSN:
1053-587X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=24285486
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
Notes:
Telecommunications and information theory
Accession Number:
edscal.24285486
Database:
PASCAL Archive

Further Information

Widely distributed multiple radar architectures offer parameter estimation improvement for target localization. For a large number of radars, the achievable localization minimum estimation mean-square error (MSE), with full resource allocation, may extend beyond the predetermined system performance goals. In this paper, performance driven resource allocation schemes for multiple radar systems are proposed. All available antennas are used in the localization process. For a predefined estimation MSE threshold, the total transmitted energy is minimized such that the performance objective is met, while keeping the transmitted power at each station within an acceptable range. For a given total power budget, the attainable localization MSE is minimized by optimizing power allocation among the transmit radars. The Cramer-Rao bound (CRB) is used as an optimization metric for the estimation MSE. The resulting nonconvex optimization problems are solved through relaxation and domain decomposition methods, supporting both central processing at the fusion center and distributed processing. It is shown that uniform or equal power allocation is not necessarily optimal and that the proposed power allocation algorithms result in local optima that provide either better localization MSE for the same power budget, or require less power to establish the same performance in terms of estimation MSE. A physical interpretation of these conclusions is offered.