期刊文献+

一种射频信号测量的频谱分析及仿真 被引量:4

Radio frequency signal spectrum analysis and simulation
下载PDF
导出
摘要 介绍了一种射频信号测量和频谱分析过程中误差的成因及减小误差的主要方法。在信号测量过程中由于测量环境和条件不同,测量的要求不同,测试者对测量方法的理解不同,得到的测量结果存在偏差。对得到的连续信号进行时域采样并截取以得到有限长的离散序列,利用计算机DFT实现信号频谱分析,通过与理论值的对比,发现了测量及分析过程中产生的各种干扰及误差,对其进行了分析及提出修正办法,进行了基于MATLAB环境下的仿真效果。 This paper describes the causes of errors in an RF signal measurement & spectrum analysis process and a method of reducing errors.In the signal measurement process as the environmental,conditions,measurement methods,the measure requirements and the testers' comprehension are all different,the measurement results obtained are different.Sampling the continuous signal obtained in time domain and intercepting it to obtain a discrete sequence of finite length,then using computer DFT signal spectrum analysis and comparing with the theoretical value,we get kinds of interference and error analysis in the measurement& analysis process.We analysis and propose amendments to approach and simulation the results based on MATLAB environment.
机构地区 [
出处 《电子测量技术》 2013年第10期27-30,共4页 Electronic Measurement Technology
关键词 射频测量 频谱分析 DFT RF measurements spectrum analysis DFT
  • 相关文献

参考文献7

二级参考文献41

  • 1董秀成,李芹,许强.BP算法在电力系统短期负荷预测中的应用[J].仪器仪表学报,2003,24(z2):379-381. 被引量:6
  • 2史真惠,朱守真,郑竞宏,王光,曲祖义,王刚.改进BP神经网络在负荷动静比例确定中的应用[J].中国电机工程学报,2004,24(7):25-29. 被引量:37
  • 3LEUTHARDT E, SCHALK G, WOLPAW J, et al. A brain-computer interface using electrocorticographic signals in humans[J]. Neural Eng, 2004,1:63-71.
  • 4LAL T N, HINTERBERGER T, WIDMAN G, et al. Methods towards invasive human brain computer interfaces [J ]. Advances in Neural Information Processing System (NIPS), 3)05,17:737-744.
  • 5GRAINMANN B, HUGGINS J, LEVINE S, et al. Towards a direct brain interface based on human subdural recordings and wavelet packet analysis [ J ]. IEEE Trans. Biomed. Eng, 2004,51:954-962.
  • 6SCHALK G, MILLER K J, ANDERSON N R, et al. Two- dimensional movement control using electrocurticographic signals in humans [ J ]. Journal of Neural Engineering, 2008,5 : 75 -84.
  • 7PISTOHL T, BALL T, SCHULZE-BONHAGE A, et al. Prediction of arm movement trajectories from ECoG-reeordings in humans [ J]. Journal of Neuroseienee Methods, 2008,167 : 105-114.
  • 8WEI,Q G, MENG F, WANG Y J, et al. Feature combination for classifying single-trial ECoG during motor imagery of different sessions[J]. Progress in Natural Science, ~007 ,17 :851- 858.
  • 9SHENOY P, MILLER K J, OJEMANN J G, et al. Generalized features for electrocorticographic BCIs [ J]. IEEE Trans. Biomed. Eng, 2008, 55: 273-280.
  • 10GUYON 1, WESTON J, BARNHILL S. Gene selection for cancer classification using support vector machines [J]. Machine Learning, 2002, 46:389-422.

共引文献116

同被引文献17

引证文献4

二级引证文献30

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