Title :
Determination of data matrix dimensions for subspace-based parameter estimation algorithms
Author :
Ding, Yinong ; Vaccaro, Richard J.
Author_Institution :
Lab. of DSP Syst., Texas Instrum. Corp. Res. & Dev., Dallas, TX, USA
Abstract :
This paper provides an analytical solution to the problem of determining the data matrix dimensions when a subspace-based algorithm is used to estimate parameters from a noisy signal. A unified expression of mean squared errors (MSEs) of signal parameter estimates is given first in terms of singular values and singular vectors. This expression is then generalized so that the MSEs are a function of physical parameters such as the data length, the signal frequency spacing, and the number of rows of the data matrix formed for the use of a subspace-based algorithm. When all the physical parameters except the data matrix dimensions are fixed, the mean squared errors of the signal parameter estimate are a function of the data matrix dimension. The optimal matrix dimensions, which correspond to the minima of the error function, can then be determined. Examples using synthetic data are included to demonstrate the significance of the results
Keywords :
least mean squares methods; matrix algebra; noise; parameter estimation; signal processing; singular value decomposition; data length; data matrix dimensions; mean squared errors; noisy signal; optimal matrix dimensions; signal frequency spacing; singular values; singular vectors; subspace-based parameter estimation algorithms; Algorithm design and analysis; Array signal processing; Digital signal processing; Frequency estimation; Maximum likelihood estimation; Parameter estimation; Signal analysis; Signal processing algorithms; Speech analysis; Time series analysis;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3192-3
DOI :
10.1109/ICASSP.1996.547983