DocumentCode :
669854
Title :
AR-model-based data extension to improve the Performance of MUSIC
Author :
Shimamura, Tetsuya ; Yokose, Takeshi
Author_Institution :
Grad. Sch. of Sci. Eng., Saitama Univ., Saitama, Japan
fYear :
2013
fDate :
12-15 Nov. 2013
Firstpage :
458
Lastpage :
461
Abstract :
In this paper, we propose an improved version of the Multiple-Signal-Classification (MUSIC) method, which uses AR model based data extension. MUSIC is excellent as a super resolution DOA estimation method and applied on any array configuration. However, the performance of MUSIC degrades in severe environments. Especially for the case of small number of snapshots, MUSIC often fails in making spectrum peaks that lead to accurate DOA estimation. We employ data extension by using the AR model and try to estimate DOAs by increasing the number of snapshots virtually. Experimental results show that the proposed method provides better performance than the standard MUSIC method.
Keywords :
array signal processing; direction-of-arrival estimation; signal classification; AR model based data extension; DOA estimation method; MUSIC performance; data extension; multiple-signal-classification method; Arrays; Data models; Direction-of-arrival estimation; Estimation; Multiple signal classification; Signal to noise ratio; Standards; AR model; DOA; MUSIC method; RMSE; data extension;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Signal Processing and Communications Systems (ISPACS), 2013 International Symposium on
Conference_Location :
Naha
Print_ISBN :
978-1-4673-6360-0
Type :
conf
DOI :
10.1109/ISPACS.2013.6704593
Filename :
6704593
Link To Document :
بازگشت