DocumentCode :
3332761
Title :
Joint detection and high resolution ML estimation of multiple sinusoids in noise
Author :
Macleod, M.D.
Author_Institution :
Dept. of Eng., Cambridge Univ., UK
Volume :
5
fYear :
2001
fDate :
2001
Firstpage :
3065
Abstract :
Harmonic analysis, the analysis of signals which consist of a sum of sinusoids (or complex sinusoids) with additive white or colored noise, is a much studied problem, with many important applications. Nevertheless, existing approaches have significant limitations. In many, the model order (number of sinusoids) is assumed known, and in most cases additive white Gaussian noise (AWGN) is assumed. We present a method for jointly determining the model order and estimating the sinusoid parameters in white or colored noise. It uses the notch periodogram in an iterative detection and estimation algorithm. It uses an explicit detection test based on an estimate of the noise power density spectrum (PDS), which is obtained by smoothing the logarithm of the notch periodogram
Keywords :
AWGN; harmonic analysis; iterative methods; maximum likelihood estimation; optimisation; signal detection; signal resolution; spectral analysis; AWGN; additive white Gaussian noise; colored noise; detection test; harmonic analysis; high resolution ML estimation; iterative detection algorithm; iterative estimation algorithm; iterative optimization; joint detection; model order; multiple sinusoids; noise power density spectrum; notch periodogram; signal analysis; sinusoid parameters estimation; AWGN; Additive noise; Additive white noise; Colored noise; Harmonic analysis; Iterative algorithms; Maximum likelihood estimation; Parameter estimation; Signal analysis; Signal resolution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
ISSN :
1520-6149
Print_ISBN :
0-7803-7041-4
Type :
conf
DOI :
10.1109/ICASSP.2001.940305
Filename :
940305
Link To Document :
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