Treffer: Robust Adaptive Beamforming for General-Rank Signal Model With Positive Semi-Definite Constraint via POTDC

Title:
Robust Adaptive Beamforming for General-Rank Signal Model With Positive Semi-Definite Constraint via POTDC
Authors:
KHABBAZIBASMENJ, Arash, VOROBYOV, Sergiy A
Source:
IEEE transactions on signal processing. 61(21-24):6103-6117
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2013.
Publication Year:
2013
Physical Description:
print, 37 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, Algorithme, Algorithm, Algoritmo, Critère optimalité, Optimality criterion, Criterio optimalidad, Désadaptation, Mismatching, Desadaptación, Etat actuel, State of the art, Estado actual, Evaluation performance, Performance evaluation, Evaluación prestación, Fonction convexe, Convex function, Función convexa, Formation voie, Beam forming, Formación haz, Matrice covariance, Covariance matrix, Matriz covariancia, Modèle 1 dimension, One dimensional model, Modelo 1 dimensión, Méthode cas pire, Worst case method, Método caso peor, Méthode itérative, Iterative method, Método iterativo, Optimisation, Optimization, Optimización, Optimum global, Global optimum, Optimo global, Programmation convexe, Convex programming, Programación convexa, Programmation non convexe, Non convex programming, Programación no convexa, Relaxation, Relajación, Solution optimale, Optimal solution, Solución óptima, Temps polynomial, Polynomial time, Tiempo polinomial, Traitement signal, Signal processing, Procesamiento señal, Difference-of-convex functions (DC) programming, general-rank signal model, non-convex programming, polynomial time DC (POTDC), robust adaptive beamforming, semi-definite programming relaxation
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada
Department of Signal Processing and Acoustics, Aalto University, 00076 AALTO, Finland
ISSN:
1053-587X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28150181
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.28150181
Database:
PASCAL Archive

Weitere Informationen

The robust adaptive beamforming (RAB) problem for general-rank signal model with an additional positive semi-definite constraint is considered. Using the principle of the worst-case performance optimization, such RAB problem leads to a difference-of-convex functions (DC) optimization problem. The existing approaches for solving the resulted non-convex DC problem are based on approximations and find only suboptimal solutions. Here, we aim at finding the globally optimal solution for the non-convex DC problem and clarify the conditions under which the solution is guaranteed to be globally optimal. Particularly, we rewrite the problem as the minimization of a one-dimensional optimal value function (OVF). Then, the OVF is replaced with another equivalent one, for which the corresponding optimization problem is convex. The new one-dimensional OVF is minimized iteratively via polynomial time DC (POTDC) algorithm. We show that the POTDC converges to a point that satisfies Karush-Kuhn-Tucker (KKT) optimality conditions, and such point is the global optimum under certain conditions. Towards this conclusion, we prove that the proposed algorithm finds the globally optimal solution if the presumed norm of the mismatch matrix that corresponds to the desired signal covariance matrix is sufficiently small. The new RAB method shows superior performance compared to the other state-of-the-art general-rank RAB methods.