Title :
Block time and frequency domain modified covariance algorithms
Author :
Spanias, Andreas ; Lim, Gim ; Loizou, Philipos ; Deisher, Michael
Author_Institution :
Dept. of Electr. Eng., Arizona State Univ., Tempe, AZ, USA
Abstract :
A block modified covariance algorithm (BMCA) is proposed for autoregressive (AR) parametric spectral estimation. The BMCA can be implemented either in the time or in the frequency domain with the latter being more efficient in high-order AR estimation. Although the BMCA operates on a block of data it allows for both block and sequential (sample-by-sample) updates. The performance of the algorithm is evaluated in terms of computational complexity and convergence characteristics. Results are given for narrowband and broadband processes
Keywords :
frequency-domain analysis; matrix algebra; parameter estimation; spectral analysis; time-domain analysis; AR parametric spectral estimation; block modified covariance algorithm; block update; broadband processes; computational complexity; convergence characteristics; frequency domain modified covariance algorithms; high-order AR estimation; narrow-band process; sequential update; time domain; Convergence; Finite impulse response filter; Frequency domain analysis; Least squares approximation; Least squares methods; Narrowband; Power harmonic filters; Predictive models; Spectral analysis; State estimation;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0532-9
DOI :
10.1109/ICASSP.1992.226566
