Title :
System identification at an extremely low SNR using energy density in DCT domain
Author :
Ferdousi, Fahmida ; Connie, Ashfiqua Tahseen ; Sharmin, Mehtaz ; Khan, M. Rezwan
Author_Institution :
Dept. of Electr. & Electron. Eng., Bangladesh Univ. of Eng. & Technol., Dhaka, Bangladesh
fDate :
4/1/2005 12:00:00 AM
Abstract :
This paper presents a new approach for system identification at an extremely low signal-to-noise ratio (SNR) like -10 dB. At such a low SNR, many of the system peaks in the signal spectrum are indistinguishable from the noise peaks that lead to erroneous identification of system poles. In the proposed method, the system properties like energy density in discrete cosine transform (DCT) domain, which is less susceptible to noise even at such a low SNR, has been utilized to estimate autocorrelation function. Successive autocorrelations of this estimated function are taken for sequential identification of system parameters.
Keywords :
correlation theory; discrete cosine transforms; filtering theory; signal processing; DCT domain; autocorrelation function; curve fitting; discrete cosine transform; energy density; extremely low SNR; prefiltering; signal spectrum; signal-to-noise ratio; system identification; Autocorrelation; Discrete cosine transforms; Econometrics; Equations; Frequency estimation; Noise level; Signal processing; Signal to noise ratio; System identification; Wiener filter; Curve fitting; energy density in DCT domain; prefiltering; successive autocorrelations;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2004.842284
