Result: A novel Parzen probabilistic neural network based noncoherent detection algorithm for distributed ultra-wideband sensors

Title:
A novel Parzen probabilistic neural network based noncoherent detection algorithm for distributed ultra-wideband sensors
Authors:
BIN LI, ZHENG ZHOU, DEJIAN LI, WEIXIA ZOU
Source:
Journal of network and computer applications. 34(6):1894-1902
Publisher Information:
Kidlington: Elsevier, 2011.
Publication Year:
2011
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Intelligence artificielle, Artificial intelligence, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Electronique, Electronics, Circuits électriques, optiques et optoélectroniques, Electric, optical and optoelectronic circuits, Réseaux neuronaux, Neural networks, 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, Application militaire, Military application, Aplicación militar, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Capteur mesure, Measurement sensor, Captador medida, Complexité algorithme, Algorithm complexity, Complejidad algoritmo, Détection non cohérente, Non coherent detection, Detección no coherente, Détection signal, Signal detection, Detección señal, Estimateur, Estimator, Estimador, Extraction caractéristique, Feature extraction, Modèle 2 dimensions, Two dimensional model, Modelo 2 dimensiones, Propagation trajet multiple, Multipath propagation, Propagación trayecto múltiple, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Réseau capteur, Sensor array, Red sensores, Réseau neuronal, Neural network, Red neuronal, Réseau sans fil, Wireless network, Red sin hilo, Signal large bande, Wide band signal, Señal banda ancha, Système réparti, Distributed system, Sistema repartido, Taux convergence, Convergence rate, Relación convergencia, Traitement signal, Signal processing, Procesamiento señal, Télécommunication sans fil, Wireless telecommunication, Telecomunicación sin hilo, Télédétection, Remote sensing, Teledetección, Ultra large bande, Ultra wide band, Banda ultraancha, Vitesse convergence, Convergence speed, Velocidad convergencia, 0707D, Bayesian optimality, Characteristic spectrum, Distributed sensor networks, Noncoherent detection, Parzen window, Probabilistic neural networks, Ultra-wideband
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Key Lab of Universal Wireless Communications, MOE, Wireless Network Laboratory, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China
ISSN:
1084-8045
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=24554766
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:
Computer science; theoretical automation; systems

Electronics

Telecommunications and information theory
Accession Number:
edscal.24554766
Database:
PASCAL Archive

Further Information

Ultra-wideband (UWB) has been widely recommended for significant commercial and military applications. However, the well-derived coherent structures for UWB signal detection are either computationally complex or hardware impractical in the presence of the intensive multipath propagations. In this article, based on the nonparametric Parzen window estimator and the probabilistic neural networks, we suggest a low-complexity and noncoherent UWB detector in the context of distributed wireless sensor networks (WSNs). A novel characteristic spectrum is firstly developed through a sequence of blind signal transforms. Then, from a pattern recognition perspective, four features are extracted from it to fully exploit the inherent property of UWB multipath signals. The established feature space is further mapped into a two-dimensional plane by feature combination in order to simplify algorithm complexity. Consequently, UWB signal detection is formulated to recognize the received patterns in this formed 2-D feature plane. With the excellent capability of fast convergence and parallel implementation, the Parzen Probabilistic Neural Network (PPNN) is introduced to estimate a posteriori probability of the developed patterns. Based on the underlying Bayesian rule of PPNN, the asymptotical optimal decision bound is finally determined in the feature plane. Numerical simulations also validate the advantages of our proposed algorithm.