| 引用本文: | 闫格,刘开华,罗蓬,吕西午.分数阶Fourier变换与新型时频滤波器设计[J].哈尔滨工业大学学报,2012,44(9):138.DOI:10.11918/j.issn.0367-6234.2012.09.025 |
| YAN Ge,LIU Kai-hua,LUO Peng,LU Xi-wu.Fractional Fourier transform and novel time-frequency filter design[J].Journal of Harbin Institute of Technology,2012,44(9):138.DOI:10.11918/j.issn.0367-6234.2012.09.025 |
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| 摘要: |
| 为了无失真地恢复复杂噪声环境中的非平稳信号,提出一种新型分数阶Fourier变换时频滤波器设计方法．该方法先利用Gabor变换得到信号在时频域的分布状况,然后用支撑向量机(SVM)分类算法结合图像分割得到分离时频图像上信号和噪声区域所需的最优分类线,最后用此最优分类线方程确定时频滤波器的阶数和传递函数．在信号和噪声时频域线性不可分的情况下,对SVM分类曲线进行了全局最小二乘分段线性拟合,然后根据拟合生成的方程构造并行多阶滤波器组．为满足实际应用中实时性的要求,对算法的计算复杂度进行了优化．计算机仿真结果验证了该方法的有效性． |
| 关键词: 时频滤波 Gabor变换 图像分割 支持向量机 分数阶Fourier变换 |
| DOI：10.11918/j.issn.0367-6234.2012.09.025 |
| 分类号:TN911.72 |
| 基金项目:天津市科技支撑计划资助项目 (10ZCKFGX03600). |
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| Fractional Fourier transform and novel time-frequency filter design |
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YAN Ge, LIU Kai-hua, LUO Peng, LU Xi-wu
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School of Electronics and Information Engineering, Tianjin University, 300072 Tianjin, China
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| Abstract: |
| To realize the lossless recovery of non-stationary signal in complicated noise environment, a novel design method based on fractional Fourier transform of time-frequency filter is proposed, in which the time-frequency distribution of incident signal is obtained by Gabor transform first, and then based on support vector machine (SVM) and technique of image segmentation, the regions of signal and noise on the time-frequency plane are separated and the optimal separating line is drawn, finally the order number and transfer function of the time-frequency filter can be determined by the optimal separating line equation. For the case of linearly inseparable signal and noise time-frequency distribution, the piecewise linear fitting based on global least square criterion is performed to the separating curved line, and the parallel filter banks are constructed from the linear fitting equation. To meet the real-time requirement in engineering application, the computational complexity was optimized, and the simulation results demonstrated the validity of this method. |
| Key words: time-frequency filtering Gabor transform image segmentation support vector machine fractional Fourier transform |
