DocumentCode :
2974969
Title :
Design of low rank estimators for higher-order statistics based on the second-order statistics
Author :
Bradaric, Ivan ; Petropulu, Athina P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA, USA
fYear :
1999
fDate :
1999
Firstpage :
66
Lastpage :
69
Abstract :
Higher-order statistics (HOS) are well known for their robustness to additive Gaussian noise and their ability to preserve phase. HOS estimates on the other hand, have been criticised for high complexity and the need of long data in order to maintain low variance. Rank reduction offers a general principle for reduction of estimator variance and complexity. In this paper we consider the problem of designing low-rank estimators for third-order statistics (TOS). We propose a method for choosing the rank reduced transformation matrix based on the second-order statistics of the signal. Results indicate that the proposed approach significantly reduces the mean square error associated with the TOS estimates. Simulation results are presented to also demonstrate the advantages of using low rank TOS estimates for blind system estimation
Keywords :
Gaussian noise; higher order statistics; matrix algebra; parameter estimation; signal processing; HOS; TOS; additive Gaussian noise; blind system estimation; estimator complexity; estimator variance; higher-order statistics; low rank estimators; mean square error; rank reduced transformation matrix; second-order statistics; signal processing; third-order statistics; Additive noise; Algebra; Approximation algorithms; Error analysis; Gaussian noise; Higher order statistics; Mean square error methods; Noise robustness; Phase detection; Phase noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Higher-Order Statistics, 1999. Proceedings of the IEEE Signal Processing Workshop on
Conference_Location :
Caesarea
Print_ISBN :
0-7695-0140-0
Type :
conf
DOI :
10.1109/HOST.1999.778694
Filename :
778694
Link To Document :
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