Study of Weak Signal Detection in Chaos Based on Volterra Filter

Author

Du, Jun ; Hou, Chu-Lin

Author_Institution

Naval Dept. of Armament, PLA Navy, Beijing, China

fYear

2009

fDate

20-22 Sept. 2009

Firstpage

1

Lastpage

4

Abstract

Based on the reconstruction of the chaotic dynamic space and the updating capability of adaptive techniques with nonlinearity of Volterra series. A third-order Volterra filter which is used to detect weak target signal in chaos is investigated. And its SVDPARAFAC approach is proposed. The method of using the singular value decomposition (SVD) and parallel factor (PARAFAC) decomposition to factor second and third order kernels is introduced in detail. The experimental results show this method has much better prediction performance for chaotic flow than nonlinear normalized least mean square (NLMS) adaptive Volterra filter and can detect out a very weak target signal in chaos when SNR gets to-70 dB.

Keywords

Volterra series; chaos; nonlinear filters; signal detection; signal reconstruction; singular value decomposition; SVDPARAFAC approach; Volterra series; chaos; parallel factor decomposition; signal reconstruction; singular value decomposition; third order kernel; third-order Volterra filter; weak target signal detection; Adaptive filters; Adaptive signal detection; Chaos; Hydrogen; Kernel; Nonlinear systems; Signal detection; Singular value decomposition; Taylor series; Tensile stress;

fLanguage

English

Publisher

ieee

Conference_Titel

Management and Service Science, 2009. MASS '09. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4638-4

Electronic_ISBN

978-1-4244-4639-1

Type

conf

DOI

10.1109/ICMSS.2009.5303085

Filename

5303085