Title :
A neural implementation of robust broadband adaptive array
Author_Institution :
Nanjing Marine Radar Inst., China Ship Res. & Dev. Acad., Nanjing, China
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;
Conference_Titel :
Radar, 1996. Proceedings., CIE International Conference of
Conference_Location :
Beijing
Print_ISBN :
0-7803-2914-7
DOI :
10.1109/ICR.1996.574465
