期刊文献+

噪声中非监督的多正弦信号检测 被引量:1

Recursive multi-sinusoidal signal detection in noise
下载PDF
导出
摘要 就被测信号的强弱差异对使用常规DFT谱峰搜索的信号检测影响,基于非监督的递归算法,通过选择适当的门限参数,提出了一种适合于强弱信号并存的多信号检测方法。该方法通过在检测过程中不断剔除强信号的影响来提高对弱信号的检测能力,给出了检测过程中门限参数选择的原则。计算机仿真实验验证了此方法的有效性。 The method is limited when the conventional DFT-based techniques are used in sinusoid detecting in the presence of strong signal and weak signal in noise. A simple unsupervised recursive algorithm which overcomes the difficulties by choosing the proper threshold is presented. The computer simulation is made to illustrate the effectiveness of the proposed algorithm.\;
出处 《系统工程与电子技术》 EI CSCD 北大核心 2004年第5期575-577,共3页 Systems Engineering and Electronics
关键词 正弦信号 递归算法 检测 门限 sinusoidal signal recursive algorithm detection threshold
  • 相关文献

参考文献5

  • 1Stoica P, Li H. Amplitude Estimation of Sinusoidal Signals: Survery,New Results, and an Application[J]. IEEE Trans. on Signal Processing, 2000, 148: 338-352. 被引量:1
  • 2林云松,黄勇,肖先赐.实正弦信号的快速相位差分频率估计方法[J].电子科技大学学报,1999,28(2):120-123. 被引量:9
  • 3Bresler Y, Macovski A. Exact Maximum Likelihood Parameter Estimation of Superimposed Exponential Signals in Noise[J]. IEEE Trans. on ASSP, 1986, 134: 1081-1089. 被引量:1
  • 4Li Yongmei, Kazunori, Sugahara. On the Frequency Estimation of Signal in the Noisy Circumstance. SICE, 2001, 17: 25-27. 被引量:1
  • 5Ge Feng-xiang, Wan Qun, Peng Ying-ning. Super-Resolution Frequency Estimation of the Sinusoidal Signals with Unknown Lowpass Envelopes[C]. Signal processing 2002 6th International Conference, 2002, 1:166- 169. 被引量:1

二级参考文献1

共引文献8

同被引文献14

  • 1周喜庆,赵国庆,王伟.实时准确正弦波频率估计综合算法[J].西安电子科技大学学报,2004,31(5):657-660. 被引量:20
  • 2焦李成.神经网络系统理论[M].西安:西安电子科技大学出版社,1996.. 被引量:115
  • 3Stoica P, Li H, and Li J. Amplithde estimation of sinusoidal signals: Survey, new results, and an application. IEEE Trans.on Signal Processing, 2000, 48(2): 338-352. 被引量:1
  • 4Karl K, Bernd S, and Kirti S. Solving optimization problems by lmrallel recombinative simulated annealing on a parallel computer an application to standard cell placement in VLSI design. IEEE Trans. on Systems, Man, and Cybernetics,Part B: Cybernetics, 1998, 28(3): 454-461. 被引量:1
  • 5Liu H C and Huang J S. Pattern recognition using evolution algorithms with fast simulated annealing. Pattern Recognition Letters, 1998, 19(5-6): 403-413. 被引量:1
  • 6Carnevali P, Coletti L, and Pararnello S. Image processing by simulated anneaing. IBM Journal of Research and Development, 1985, 29(6): 569-579. 被引量:1
  • 7Chen S and Luk B L. Adaptive simulated annealing for optimization in signal processing applications. Signal Processing, 1999, 79(1): 117-128. 被引量:1
  • 8Jeong I K and Lee J J. Adaptive simulated annealing geneticalgorithm for control applications. International Journal of Systems Science, 1996, 27(2): 241-253. 被引量:1
  • 9Brooks S P, Friel N, and King R. Classical model selection via simulated annealing. Journal Royal Statistical Society B, 2003,65(2): 503-520. 被引量:1
  • 10Copsey K, Gordon N, and Marrs A. Bayesian analysis of generalized frequency-modulated signals. IEEE Trans. on Signal Processing, 2002, 50(3): 725-735. 被引量:1

引证文献1

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部