DocumentCode :
3079112
Title :
Spatial wavelet packet denoising for improved doa estimation
Author :
Sathish, R. ; Anand, G.V.
Author_Institution :
Dept. of Electr. Commun. Eng., Indian Inst. of Sci., Bangalore
fYear :
2004
fDate :
Sept. 29 2004-Oct. 1 2004
Firstpage :
745
Lastpage :
754
Abstract :
The performance of direction-of-arrival (DOA) estimation techniques such as MUSIC degrades progressively with decreasing signal-to-noise ratio (SNR). The DOA estimation performance may be improved by employing a pre-processor that enhances the SNR, before performing the DOA estimation. In this paper, a denoising technique based on the use of wavelet packet transform in the spatial domain is proposed for enhancing the output SNR of a uniform linear array of sensors receiving narrowband signals in the form of plane waves from different directions. The technique involves the use of a spatial wavelet packet transform (SWPT) followed by a block thresholding scheme based on the norm of SWPT subvectors in different spatial frequency subbands. This method has the advantage of not requiring the high sampling rates demanded by the temporal wavelet denoising techniques. It is shown through simulations that SWPT denoising (SWD) requires a sampling rate that is just 2-4 times the signal frequency, whereas temporal wavelet denoising (TWD) requires a much higher sampling rate for achieving a comparable SNR gain. Consequently, at lower sampling rates, the DOA estimation performance indices, such as bias, mean square error and resolution, achieved by SWD are much superior to those achieved by TWD or by undenoised data
Keywords :
array signal processing; direction-of-arrival estimation; mean square error methods; signal denoising; signal sampling; wavelet transforms; DOA estimation; SNR; block thresholding scheme; direction-of-arrival estimation; mean square error; narrowband signals; sampling rate; signal-to-noise ratio; spatial wavelet packet denoising; spatial wavelet packet transform subvector; uniform linear sensor array; Degradation; Direction of arrival estimation; Frequency; Multiple signal classification; Noise reduction; Sampling methods; Sensor arrays; Signal to noise ratio; Wavelet packets; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing, 2004. Proceedings of the 2004 14th IEEE Signal Processing Society Workshop
Conference_Location :
Sao Luis
ISSN :
1551-2541
Print_ISBN :
0-7803-8608-4
Type :
conf
DOI :
10.1109/MLSP.2004.1423041
Filename :
1423041
Link To Document :
بازگشت