Title :
Blind robust neural network beamformer
Author :
Chen, Yuxin ; He, Zhenya
Author_Institution :
Dept. of Radio Eng., Southeast Univ., Nanjing, China
Abstract :
Many blind beamforming algorithms, such as C-CAB, use the signal characteristics to estimate the steering vector. The conventional LCMV algorithm is then adapted to obtain the optimum solution. However, the LCMV-like methods are sensitive to the mismatch. In this paper, the cause of this mismatch is discussed in detail. A robust blind beamforming algorithm is presented. Using a neural network structure the algorithm can decrease the computational complexity and make it possible to realize the method in real time. Results of computer simulations are included to support our analysis
Keywords :
Hopfield neural nets; computational complexity; optimisation; signal detection; Hopfield neural network; blind beamforming; computational complexity; cyclostationary signals; optimisation; steering vector; Algorithm design and analysis; Array signal processing; Computational complexity; Computer simulation; Digital signal processing; Helium; Interference constraints; Neural networks; Noise robustness; Signal analysis;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.836198
