馮大政

馮大政

馮大政,男,1959年12月生,工學博士,西安電子科技大學教授,碩士生導師,博士導師。中國電子學會高級會員,美國IEEE學會會員。他長期從事信號與信息處理研究, 發表期刊學術論文九十多篇,其中國際期刊論文三十多篇,IEEE會刊論文十多篇。他獲得過教育部跨世紀人才基金, 獲得省部級科技進步二等獎三項。 在信號處理領域,他已經有一定國際知名度。近年來,在自適應信號處理,盲信號處理,機載雷達信號處理,MIMO雷達信號處理和InSAR等研究達到國際先進水平。

基本介紹

  • 中文名:馮大政
  • 國籍:中國
  • 出生日期:1959年12月
  • 性別:男
研究領域,科研項目,教學情況,研究成果,

研究領域

1、自適應信號處理;
2、雷達成像與後處理技術;
3、陣列信號處理;
4、智慧型信息處理(現主要從事從結構和功能上仿大腦信息處理);
5、新體制雷達信號處理;

科研項目

主要科研項目:
1、主持項國家自然科學基金1項;
2、主持國防預研基金2項;
3、主持橫向課題1項;
4、參加重要項目3項。

教學情況

《現代信號處理》(碩士生和博士生課程);
《盲信號處理》》(碩士生課程)。

研究成果

他發表的有代性表論文(與SCI檢索有關的論文)如下:
[1] Da-Zheng Feng et al., “Fast approximate inverse-power iteration algorithm for adaptive FIR filtering,” IEEE Trans. Signal Processing, No. 10, pp. 4032-4039, 2006.
[2] Nan Wu, and Da-Zheng Feng, “A locally adaptive filter of interferometric phase images,” IEEE Geoscience and Remote Sensing Letters, Vol. 3, No. 1, pp. 73-77, 2006.
[3] Yi Zhou, and Da-Zheng Feng, “A novel algorithm for two-dimensional frequency estimation,” Signal Processing, In Press, Corrected Proof, Available online , June 2006.
[4] Da-Zheng Feng, and Wei-Xing Zheng, “Fast RLS-type algorithm for unbiased equation-error adaptive IIR filtering based on approximate inverse-power iteration,” IEEE Trans. Signal Processing, No. 11, 2005.
[5] Da-Zheng Feng, Wei-Xing Zheng, and Ying Jia, “Neural network learning algorithms for tracking minor subspace in high dimensional data stream,” IEEE Trans. Neural Networks, No.3, 2005.
[6] Dong-Xia Chang, and Da-Zheng Feng et al., “A Fast recursive total least squares algorithm for adaptive IIR filtering,” IEEE Trans. Signal Processing, No. 3, 2005.
[7] Da-Zheng Feng, Xian-Da Zhang, and Zheng Bao, “A neural network learning for adaptively extracting cross-correlation features between two high dimensional data streams,” IEEE Trans. Neural Networks, Vol. 15, No. 6, pp. 1541-1554, Nov. 2004.
[8] Da-Zheng Feng et al., “A Fast recursive total least squares algorithm for adaptive FIR filtering,” IEEE Trans. Signal Processing, Vol. 52, No.10, pp. 2729-2737, Oct. 2004.
[9] Lei Huang, Shun-Jun Wu, Da-Zheng Feng, and Lin-Rang Zhang, “Low complexity method for signal subspace fitting,” Electronics Letters, Vol. 40, No. 14, pp. 847-848, July 2004.
[10] Da-Zheng Feng et al. “Neural network learning for principal component analysis: A multistage decomposition approach,” Chinese Journal of Electronics No. 1, Jan. 2004.
[11] Da-Zheng Feng, et al. “An efficient multistage decomposition approach for independent components,” Signal Processing, Vol. 83, pp. 181-197, Jan. 2003.
[12] Da-Zheng Feng, et al. “Multistage decomposition algorithm for blind source separation,” Progress in Natural Science, No. 5, May 2002.
[13] Da-Zheng Feng, et al. “A bi-iteration instrumental variable noise-subspace tracking algorithm,” Signal Processing, Vol. 81, pp. 2215-2221, 2001.
[14] Da-Zheng Feng, et al. “A cross-associative neural network for SVD of non-squared data matrix in signal processing,” IEEE Trans. Neural Networks, No. 5, pp. 1215-1221, Sept. 2001.
[15] Da-Zheng Feng, et al. “An extended recursive least-squares algorithm,” Signal Processing, Vol. 81, pp. 1075-1081, 2001.
[16] Da-Zheng Feng, et al. “A Cross-Associative Neural Network Used as SVD of Non-Square Data Matrix or Cross-correlation matrix in signal Processing,” Chinese Journal of Electronics No. 1, Jan. 2001.
[17] Da-Zheng Feng, et al., “Two-dimensional phase unwrapping based on the finite element method and FFT’s,” Chinese Journal of Electronics, No. 3, July 2000.
[18] Da-Zheng Feng, et al. “Modified RLS algorithm for unbiased estimation of FIR system with input and output noise,” IEE Electronics Letters, No. 3, pp. 273-274, Feb. 2000.
[19] Da-Zheng Feng, et al., “Total least mean squares algorithm,” IEEE Trans. Signal Processing, No. 8, pp. 2122-2130, Aug. 1998.
[20] Da-Zheng Feng, et al., “Cross-correlation neural network models for the smallest singular component of general matrix,” Signal Processing, Vol. 64, pp. 333-346, Feb. 1998.
[21] Da-Zheng Feng et al., “Modified Cross-Correlation neural networks,” Chinese Journal of Electronics, No. 2, May 1997.

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