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非线性调频信号的自适应时频滤波算法 被引量:2

Adaptive Time-Frequency Filter Method of Nonlinear Frequency Modulation Signal
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摘要 针对传统的时域或频域滤波算法对非线性调频信号滤波去噪效果不好的问题,本文提出了一种时频域内非线性调频信号的自适应滤波去噪算法。首先对原信号进行广义S变换获得其时频分布,接下来利用有效信号时频分布特性选取时频通域,构造区域滤波算子并去除掉时频通域外的噪声分量的时频分布;然后利用有效信号分量的时频聚集性构造自适应时频滤波算子,对含有随机噪声的有效信号分量进行滤波处理,得到滤波去噪后的信号的时频分布;最后利用广义S逆变换将处理后的时频分布变换到时间域,得到滤波去噪后的信号。通过仿真实验的结果可知,本文提出的算法在非线性调频信号的滤波去噪和有效特性保持方面取得了较好的效果。 In order to solve the problem that time domain or frequency domain filtering is not effective in denoising for nonlinear frequency modulation signal,a novel adaptive time-frequency (TF)filtering method based on generalized S-trans-form is proposed.Firstly,the TF distribution spectrum of the signal is generated by applying generalized S-transform. Then,based on the TF distribution characteristics of the effective signal component,the TF pass region of the signal is identified,outside of which,the TF distribution of the noise is removed.In the next step,an adaptive TF filter is construc-ted using the TF concentration of the effective signal component,to suppress the noise within the effective signal component to obtain filtered TF distribution of the nonlinear modulation signal,which is then converted to time domain using inverse generalized S transform,to generate the filtered signal.Simulation results demonstrate that the proposed algorithm provides satisfactory performance in noise suppression and improves the signal-to-noise ratio.
出处 《信号处理》 CSCD 北大核心 2015年第3期356-363,共8页 Journal of Signal Processing
基金 公安部科技强警基础工作专项项目(2014GABJC024) 陕西省教育厅专项科研计划项目(14JK1680)
关键词 非线性调频信号 时频域滤波 广义S变换 时频聚集性 nonlinear frequency modulation signal time-frequency filtering generalized S-transform time-frequency con-centration
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  • 1Ju H, Lee S, Kim M Y Complexity Reduction in Karhunen-Loeve Transform based Speech Coder for Voice Transmission[J]. IEEE Transactions on Consumer Electronics, 2014, 60(1): 130-136.
  • 2Sandra M, Valery N, Us A, et al. Automatic detection of optic disc based on PCA and mathematical morphology[J]. IEEE Transactions on Medical Imaging, 2013, 32(4): 786-796.
  • 3Akdeniz M R, Liu Y, Samimi M K, et al. Millimeter wave channel modeling and cellular capacity evaluation[J]. Selected Areas in Communications IEEE Journal on, 2014, 32(6): 1164-1179.
  • 4Sheng S P, Liu M, Saigal R. Data-driven channel modeling using spectrum measurement[J]. IEEE Transactions on Mobile Computing, 2015, 14(9): 1794-1805.
  • 5Burkhardt F, Eberlein E, Jaeckel S, et al. MIMOSA-a dual approach to detailed land mobile satellite channel modeling[J]. International Journal of Satellite Communications & Networking, 2014, 32: 309-328.
  • 6Baktash E, Dehghani M J, Nasab M R F, et al. Shallow water acoustic channel modeling based on analytical second order statistics for moving transmitter/receiver[J]. IEEE Transactions on SignalProcessing, 2015, 63(10): 2533-2545.
  • 7Kim Y, Kang M. Predictive modeling of channel potential in 3-d nand flash memory[J]. IEEE Transactions on Electron Devices, 2014, 61(11): 3901-3904.
  • 8Kovalyov I P, Kuzikova N I, Ponomarev D M. New approach to wireless channel modeling based on representing fields in the scattering medium as the sum of resonance oscillation fields[J]. Wireless Networks, 2015: 1-17.
  • 9Pop M F, Beaulieu N C. Limitations of sum-of-sinusoids fading channel simulators[J]. IEEE Trans- actions on Communication, 2001, 49(4): 699-708.
  • 10庞希斌,徐进,卢小兵,张爱辉.地质雷达在机场跑道缺陷检测中的应用——以“5·12”汶川地震后九寨黄龙机场检测为例[J].西南民族大学学报(自然科学版),2008,34(6):1096-1100. 被引量:6

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