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Volume 17 Issue 3
May  1995
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Lin Sheng, Yin Qinye. SIMPLE ANALYSIS OF THE NEURAL OPTIMIZATION METHOD FOR DOA ESTIMATION[J]. Journal of Electronics & Information Technology, 1995, 17(3): 232-237.
Citation: Lin Sheng, Yin Qinye. SIMPLE ANALYSIS OF THE NEURAL OPTIMIZATION METHOD FOR DOA ESTIMATION[J]. Journal of Electronics & Information Technology, 1995, 17(3): 232-237.

SIMPLE ANALYSIS OF THE NEURAL OPTIMIZATION METHOD FOR DOA ESTIMATION

  • Received Date: 1994-02-01
  • Rev Recd Date: 1994-07-12
  • Publish Date: 1995-05-19
  • This paper gives a simple analysis of the method of using the Hop-field s optimization neural network to solve the DOA estimation problem. Although the method can avoid the eigendecomposition of data autocorrelation matrix and the orthogonality search of parameter space, theoretical analysis and simulation results show that the construction of the DOA cost function is incorrect on the condition that there is no constraint on the number of outputs of the network.
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  • Schmidt R O. IEEE Trans. on AP, 1986, Ap-34(3): 276-280.[2]Stoica P, Nehorai A. IEEE Trans. on ASSP, 1989, ASSP-37(5): 720-741.[3]Stoica P, Nehorai A. MUSIC[4]Maximum Likelihood, and Cramer-Rao Bound: Further Results[5]and Comparisons, IEEE Proc. ASSP, 1989, 2605-2608.[6]Kung S Y, Diamantaras K I. A Neural Network Learning Algorithm for Adaptive Principal Component Extraction (APEX), Proc. ICASSP, 1990, Vol. 2, 861-861.[7]Lin Sheng, Yin Qin-Ye. Adaptive Principal Component Analysis Via Neural Networks. IEEE[8]International Conference on Signal Processing, China: 1993, 1224-1227.[9]Jha S, Durrani T. IEEE Trans. on SMC, 1991, SMC-2I(5): 1192-1201.[10]Sharman K C. Maximum Likelihood Parameter Estimation by Simulated Annealing. Proc. ICASSP. 1988, 2741-2744.[11]林盛,殷勤业.信号处理学报,1994,10(2): 66-74.[12]林盛,殷勤业.电子科学学刊,1994,17(1): 1-6.
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