RF Signal Denoising by Combining Wavelet Transform and SVD
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NETL
NSTL
维普
万方数据
射频信号因具备空间远距离传输特性被广泛应用于航天测控等领域,但空间磁场、信道、设备元器件等会引入大量噪声干扰,对射频信号的传播及后续分析处理造成影响.而目前关于射频信号消噪的研究很少,针对此问题,对比研究了传统滤波器消噪、小波阈值法及SVD(Singular Value Decomposition,奇异值分解)法在射频信号消噪方面的应用,仿真分析了各算法对有用射频信号的提取效果,从而发现3种方法均能起到噪声抑制的作用,但是,滤波器法明显会降低信号能量,小波变换法易使重构信号失真,而SVD法则运算时间较长.为此,提出将小波阈值法与SVD相结合,用于射频信号消噪.再通过比对各方法消噪的性能指标,验证了将2种方法结合,可有效提高运算效率及噪声抑制能力.
Radio Frequency (RF)signals are widely used in fields such as aerospace Tracking,Telemetry and Com-mand (TT&C)for its far distance transmission characteristics.However,noise and interference would be brought in through space magnetic field,channels,equipment components and so on,and they would negatively affect the spreading,analysis and processing of RF signals.There is little research about RF signal denoising at pres-ent.Traditional filter denoising method,wavelet threshold method and Singular Value Decomposition (SVD) method were studied comparatively.Simulation results and extraction effects of useful RF signals were analyzed with different methods.Finally,a method combining wavelet threshold and SVD was put forward and its effectiveness and superiority of noise suppression were verified by denoising performance metrics.
radio frequency signalnoise suppressionwavelet transformSingular Value Decomposition (SVD)
NETL
NSTL
维普
万方数据
