A neural implementation of robust broadband adaptive array

Author

Qiang, Guo

Author_Institution

Nanjing Marine Radar Inst., China Ship Res. & Dev. Acad., Nanjing, China

fYear

1996

fDate

8-10 Oct 1996

Firstpage

371

Lastpage

374

Abstract

The computational complexity of robust adaptive array with quadratic constraints is a critical problem in real time implementation. For coping with this problem, the Chua´s (1988) nonlinear programming recurrent neural network is explored, which is used to solve the optimal solution of the robust adaptive array with quadratic constraints. The present approach converges within several times of the circuit time constant, thus particularly suitable to real time applications

Keywords

adaptive signal processing; array signal processing; computational complexity; convergence of numerical methods; direction-of-arrival estimation; nonlinear programming; recurrent neural nets; circuit time constant; computational complexity; convergence; neural implementation; nonlinear programming recurrent neural network; optimal solution; quadratic constraints; real time applications; real time implementation; robust broadband adaptive array; Adaptive arrays; Circuits; Covariance matrix; Delay effects; Delay lines; Error correction; Phased arrays; Recurrent neural networks; Robust control; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Radar, 1996. Proceedings., CIE International Conference of

Conference_Location

Beijing

Print_ISBN

0-7803-2914-7

Type

conf

DOI

10.1109/ICR.1996.574465

Filename

574465