DocumentCode :
3368525
Title :
ARMA processes in multirate filter banks with applications to radar signal classification
Author :
Eom, Kie B. ; Chellappa, Rama
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., George Washington Univ., Washington, DC, USA
fYear :
1994
fDate :
25-28 Oct 1994
Firstpage :
136
Lastpage :
139
Abstract :
Considers stochastic modeling in a scale space defined by multirate filter banks using autoregressive moving average (ARMA) models. The authors show that signals at coarser scales in analysis filter banks follow ARMA models if the signal at a finer scale is an ARMA process. The model for a coarser scale signals can be identified from the model of a finer scale signal. Reconstruction of a finer scale signal from a partial set of decomposed signals at a coarser scale is considered as an optimal estimation problem. The authors developed a recursive minimum mean square error (MMSE) estimation algorithm for reconstruction of a finer scale signal. The stochastic modeling approach in scale space is applied to classification of radar signals
Keywords :
autoregressive moving average processes; least mean squares methods; radar signal processing; recursive estimation; recursive filters; signal reconstruction; ARMA processes; analysis filter banks; autoregressive moving average models; coarser scales; decomposed signals; finer scale; multirate filter banks; optimal estimation problem; partial set; radar signal classification; reconstruction; recursive minimum mean square error estimation; scale space; stochastic modeling; Autoregressive processes; Estimation error; Filter bank; Mean square error methods; Radar applications; Recursive estimation; Signal analysis; Signal processing; Spaceborne radar; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Time-Frequency and Time-Scale Analysis, 1994., Proceedings of the IEEE-SP International Symposium on
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-7803-2127-8
Type :
conf
DOI :
10.1109/TFSA.1994.467345
Filename :
467345
Link To Document :
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