IMPROVING DIRECTION OF ARRIVAL ESTIMATION BASED ON DIRECTIVITY PATTERN ANALYSIS AND ADAPTIVE CASCADED CLASSIFIERS

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/51550

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51550

Title:	IMPROVING DIRECTION OF ARRIVAL ESTIMATION BASED ON DIRECTIVITY PATTERN ANALYSIS AND ADAPTIVE CASCADED CLASSIFIERS
Authors:	Chen,BW;Wang,JF;Wang,JC
Contributors:	資訊工程學系
Keywords:	BLIND SOURCE SEPARATION;PASSIVE ARRAYS;DOA ESTIMATION;LOCALIZATION;TRACKING;SYSTEMS;NETWORK;SIGNALS
Date:	2010
Issue Date:	2012-03-27 18:55:59 (UTC+8)
Publisher:	國立中央大學
Abstract:	Direction of arrival (DOA) estimation via directivity pattern analysis (DPA) has been proposed for years, in order to locate signal sources. It uses directional nulls existing in directivity patterns to approximate angles. However, traditional directivity pattern analysis often causes biased estimation, which is usually derived from ambiguous patterns. In this study, we convert the DOA problem from the signal domain into the visual domain, so that pattern analyses and recognition techniques are applicable. A novel architecture, composed of adaptive cascaded separators and a neural network, is presented to minimize the effects of obscure directional nulls. We also employ an adaptive algorithm for collecting the refined information generated by the neural network and updating the separators automatically. The experimental results show that this system is less susceptible to the effects of inappropriate patterns than other systems. Simulations were performed to compare results between the conventional approaches and our proposed method.
Relation:	JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	362	View/Open

社群 sharing

Loading...