| 用高阶模糊函数算法分析欠采样信号 |
| |
| 引用本文： | 陶然,单涛,周思永,王越.用高阶模糊函数算法分析欠采样信号[J].北京理工大学学报(英文版),1999,8(2):175-180. |
| |
| 作者姓名： | 陶然 单涛 周思永 王越 |
| |
| 作者单位： | 北京理工大学电子工程系,北京,100081 |
| |
| 摘 要： | 目的寻找一种快速有效的分析欠采样信号的算法. 方法高阶模糊函数(HAF)算法的优势是从相位降阶的角度分析多项式相位信号. 利用此特点,在满足一定条件下,从估计欠采样信号的参数入手来解决频率模糊问题. 结果与结论以欠采样线性调频信号的分析为例,用仿真结果证明了HAF算法的有效性.与时频分析法相比,大大降低了计算量,边界条件要求松且无边界效应.
|
| 关 键 词： | 欠采样信号 频率估计 时频分布 |
| 收稿时间： | 1998/11/3 0:00:00 |
Analyzing Undersampled Signals Using High Order Ambiguity Function Algorithm |
| |
| Tao Ran,Shan Tao,Zhou Siyong and Wang Yue.Analyzing Undersampled Signals Using High Order Ambiguity Function Algorithm[J].Journal of Beijing Institute of Technology,1999,8(2):175-180. |
| |
| Authors: | Tao Ran Shan Tao Zhou Siyong and Wang Yue |
| |
| Affiliation: | Department of Electronics Engineering, Beijing Institute of Technology, Beijing 100081;Department of Electronics Engineering, Beijing Institute of Technology, Beijing 100081;Department of Electronics Engineering, Beijing Institute of Technology, Beijing 100081;Department of Electronics Engineering, Beijing Institute of Technology, Beijing 100081 |
| |
| Abstract: | Aim To find an effective and fast algorithm to analyze undersampled signals. Methods The advantage of high order ambiguity function(HAF) algorithm is that it can analyze polynomial phase signals by phase rank reduction. In this paper, it was first used to analyze the parameters of undersampled signals. When some conditions are satisfied, the problem of frequency confusion can be solved. Results and Conclusion As an example, we analyze undersampled linear frequency modulated signal. The simulation results verify the effectiveness of HAF algorithm. Compared with time frequency distribution, HAF algorithm reduces computation burden to a great extent, needs weak boundary conditions and doesn't have boundary effect. |
| |
| Keywords: | undersampled signal frequency estimation time-frequency distribution |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《北京理工大学学报(英文版)》浏览原始摘要信息 |
|
点击此处可从《北京理工大学学报(英文版)》下载全文 |
|
