|Table of Contents|

Research on MUSIC-DOA estimation method based onSVM steered response power(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2019年02期
Page:
237-
Research Field:
Publishing date:

Info

Title:
Research on MUSIC-DOA estimation method based onSVM steered response power
Author(s):
Guo Yecai12Wang Chao1
1.Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science & Technology,Nanjing 210044,China; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technol
Keywords:
wideband signal support vector machine direction of arrival estimation steered response power
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2019.43.02.017
Abstract:
Aiming at the problem that the performance of estimation of the traditional MUSIC algorithm on the direction of arrival(DOA)degrades under the low signal to noise ratio(SNR)or strong reverberation,the steered response power(SRP)MUSIC algorithm based on the support vector machine(SVM)is proposed here. Firstly,the proposed algorithm performs fast Fourier transform on the wideband signals,and then uses the MUSIC algorithm to estimate the DOA. Finally,results of the DOA estimation of the each sub-band signal are classified by using the SVM,and the more accurate sub-band signals are selected after the classification to make the fusion and to get the broadband signal DOA estimation. The method in this paper solves the problem that the MUSIC normalization algorithm has low precision under the low signal to noise ratio effectively,and improves the stability of the algorithm.

References:

[1] Chen J C,Yao K,Tung T L,et al. Source localization and tracking of a wideband source using a randomly distributed beamforming sensor array[J]. International Journal of High Performance Computing Applications,2002,16(3):259-272.
[2]Huang Qinghua,Zhong Qiang,Zhuang Qilei. Source localization with minimum variance distortionless response for spherical microphone arrays[J]. Advances in Manu facturing,2011,15(1):21-25.
[3]Mennill D J,Battiston M,Wilson D R,et al. Field test of an affordable,portable,wireless microphone array for spatial monitoring of animal ecology and behaviour[J]. Methods in Ecology & Evolution,2012,3(4):704-712.
[4]Salvati D,Canazza S. Incident signal power comparison for localization of concurrent multiple acoustic sources[J]. The Scientific World Journal,2014(6):582397.
[5]Rowell C R,Fee D,Szuberla C A L,et al. Three-dimensional volcano-acoustic source localization at Karymsky Volcano,Kamchatka,Russia[J]. Journal of Volcanology & Geothermal Research,2014,283(15):101-115.
[6]Salvati D,Drioli C,Foresti G L. Incoherent frequency fusion for broadband steered response power algorithms in noisy environments[J]. IEEE Signal Processing Letters,2014,21(5):581-585.
[7]盛卫星,方大纲,杨正龙,等. 一般非综合孔径雷达方位超分辨研究[J]. 南京理工大学学报,2000,24(4):289-295.
Sheng Weixing,Fang Dagang,Yang Zhenglong,et al. Angular super-resolution for real aperture radars[J]. Journal of Nanjing University of Science and Technology,2000,24(4):289-295.
[8]Zue V W. Automatic speech recognition and understanding[M]. Boston,US:Massachusetts Institute of Technology,1990:185-200.
[9]Ribeiro F,Zhang C,Ba D E. Using reverberation to improve range and elevation discrimination for small array sound source localization[J]. IEEE Transactions on Audio Speech & Language Processing,2010,18(7):1781-1792.
[10]Salvati D,Canazza S,Rodà A. A sound localization based interface for real-time control of audio processing[C]//Proceedings of the 14th International Conference on Digital Audio Effects. Paris,France:The National Center for Scientific Research,2011:177-184.
[11]Salvati D,Canazza S. Adaptive time delay estimation using filter length constraints for source localization in reverberant acoustic environments[J]. IEEE Signal Processing Letters,2013,20(5):507-510.
[12]Li Y,Ho K C,Popescu M. A microphone array system for automatic fall detection[J]. IEEE Transactions on Biomedical Engineering,2012,59(5):1291-1301.
[13]Krim H,Viberg M. Two decades of array signal processing research:the parametric approach[J]. IEEE Signal Process Magazine,1996,13(4):67-94.
[14]谭颖,殷福亮,李细林. 改进的SRP-PHAT声源定位方法[J]. 电子与信息学报,2006,28(7):1223-1227.
Tan Ying,Yin Fuliang,Li Xilin. Sound localization method using modified SRP-PHAT algorithm[J]. Journal of Electronics & Information Technology,2006,28(7):1223-1227.
[15]张小飞. 阵列信号处理的理论和应用[M]. 北京:国防工业出版社,2010:78-83.
[16]余正涛,邹俊杰,赵兴,等. 基于主动学习的最小二乘支持向量机稀疏化[J]. 南京理工大学学报,2012,36(1):12-17.
Yu Zhengtao,Zou Junjie,Zhao Xing,et al. Sparseness of least squares support vector machines based on active learning[J]. Journal of Nanjing University of Science and Technology,2012,36(1):12-17.
[17]Salvati D,Drioli C,Foresti G L. A weighted MVDR beamformer based on SVM learning for sound source localization[J]. Pattern Recognition Letters,2016(84):15-21.
[18]Salvati D,Drioli C,Foresti G L. Frequency map selection using a RBFN-based classifier in the MVDR beamformer for speaker localization in reverberant rooms[C]//Proceedings of The 16th Annual Conference of the International Speech Communication Association. Dresden,Germany:Interspeech,2015:3298-3301.
[19]Platt J C. Saquential minimal optimization:A fast algorithm for training support vector machines[J]. Journal of Information Technology,1998,2(5):1-28.
[20]Dibiase J H,Silverman H F,Brandstein M S. Robust localization in reverberant rooms[J]. Micropone Arrays Signal Processing Techniques & Applications,2001(3):157-180.

Memo

Memo:
-
Last Update: 2019-04-26