期刊文献+

一种盲信号恢复的特征向量算法 被引量:2

Algorithm for blind signal recovery based on EVA
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摘要 信号恢复是雷达声纳目标检测和通信信号处理的一项重要任务。基于特征向量估计(EVA)的信号恢复算法可以在未知信道和源信号先验知识的情况下,在较短的时间内实现信号的盲恢复。它是将均衡信道参数估计问题变为与交叉累积量矩阵、自相关矩阵有关的特征向量求解问题,并保证解的唯一性和收敛性。将该算法应用于单源-单接收元(SISO)的恒模变相信号、两种声学水池信号妁恢复,取得了较好的恢复效果。 Signal recovery is a very important task for detection of radar sonar target and communication signal processing. A signal recovery algorithm based on EVA is proposed. The given algorithm can realize blind recovery of signal within shorter time in condition of unknown prior knowledge of channel source signal and change the problem of channel parameter estimation into eigenvector solution with relative to crossover cumulant matrix and autocorrelation matrix. The solution of the problem is uniqne and convergent. Better recovery results are obtained by applying the algorithm to constant modulus variable phase signals and two kinds of underwater acoustic signal in SISO system.
出处 《系统工程与电子技术》 EI CSCD 北大核心 2003年第12期1492-1494,共3页 Systems Engineering and Electronics
基金 国家自然科学基金(60072049)
关键词 声纳目标 盲源分离 盲均衡 信号处理 特征向量 盲信号恢复 sonar target blind source separation signal processing algorithm
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参考文献5

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同被引文献21

  • 1白自权,李道本.基于EVA的改进型盲均衡算法[J].北京邮电大学学报,2005,28(4):10-13. 被引量:1
  • 2张建明,林亚平,吴宏斌,杨格兰.独立成分分析的研究进展[J].系统仿真学报,2006,18(4):992-997. 被引量:31
  • 3王亮,王永利.超高斯与亚高斯混合信号的盲分离研究[J].科学技术与工程,2006,6(18):2841-2844. 被引量:2
  • 4朱丽敏,赖惠成.一种含噪混合信号的盲分离方法[J].科学技术与工程,2007,7(22):5771-5775. 被引量:1
  • 5Shun-lchi, Andrzej Ciehoeki. Adaptive Blind Signal Processing- Neural Network Approaches[ C ]. Proceeding of the IEEE, 1998, 86(10) :2026-2048.
  • 6Kiyotaka Kohno, Yujiro Inouye, Mitsuru Kawamoto and Tetsuga Okamoto. Adaptive Super Exponentional Algorithms for Blind De- convolution of MIMO Systems [ J ]. IEEE. ISCAS, 2004 : 680 - 683.
  • 7Zhi Ding, Tuan Nguyen. Stationary points of a Kurtosis maximiza- tion algorithm for blind signal separation and antenna beam-form- ing [ J ]. IEEE. Trans. on Signal Processing, 2000,48 ( 6 ) : 1587- 1596.
  • 8ZHOU X,ZHOU C,KEMP I J. An improved methodol- ogy for application of wavelet transform to partial dis- charge measurement denoising [ J ]. Dielectrics and E- lectrical Insulation, IEEE Transactinns on, 2005, 12 (3) :586-594.
  • 9LUO G,ZHANG D,KOH Y,et al. Time-Frequency En- tropy-Based Partial-Discharge Extraction for Nonintru- sire Measurement [ J ]. IEEE Transactions on Power Delivery, 2012,27 ( 4 ) : 1919-1927.
  • 10AIBADOUR F, SUNAR M,CHEDED L. Vibration a- nalysis of rotating machinery using time-frequency a-nalysis and wavelet techniques [ J]. Mechanical Sys- tems and Signal Processing,2011,25 ( 6 ) : 2083-2101.

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