Title :
Recursive two dimensional spectral estimation based on an AR model excited by a T-distribution process using QR decomposition approach
Author :
Sanubari, J. ; Tokuda, Keiichi
Author_Institution :
Dept. of Comput. Sci., Nagoya Inst. of Technol., Japan
Abstract :
In this paper a robust two dimensional spectral estimation based on an AR model is proposed. The robustness of the method is obtained by assuming that the output or the residual signal has an independently and identically distribution with a t probability density function. By doing so, the effect of large amplitude residuals can be reduced. To reduce the calculation burden, the optimal solution is recursively calculated by incorporating the QR-decomposition method. The simulation results show that the obtained spectral estimate after 100 by 100 pixels iterations by using small α; i.e. α=3; is more accurate than that obtained by using large α; i.e. α=∞. The plots of the mean square error (MSE) to the iteration number show that by using small α we can obtain a smaller MSE than that by using large α
Keywords :
autoregressive processes; matrix decomposition; parameter estimation; probability; spectral analysis; AR model; MSE; QR decomposition method; T-distribution process; mean square error; probability density function; recursive 2D spectral estimation; two dimensional spectral estimation; Computer science; Density functional theory; Mean square error methods; Performance analysis; Recursive estimation; Robustness; Signal analysis; Signal processing;
Conference_Titel :
Circuits and Systems, 1998. IEEE APCCAS 1998. The 1998 IEEE Asia-Pacific Conference on
Conference_Location :
Chiangmai
Print_ISBN :
0-7803-5146-0
DOI :
10.1109/APCCAS.1998.743806