In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose freq...In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose frequencies have overlapped regions in Fourier transform domain and even have crossed points in time-frequency distribution (TFD) so that the proposed TVBF seems like a “soft-cutter” that cuts the frequency domain to snaky slices with rational physical sense. First, the Hilbert transform based decomposition is analyzed for the analysis of nonstationary signals. Based on the above analysis, a hypothesis under a certain condition that AM-FM components can be separated successfully based on Hilbert transform and the assisted signal is developed, which is supported by representative experiments and theoretical performance analyses on a error bound that is shown to be proportional to the product of frequency width and noise variance. The assisted signals are derived from the refined time-frequency distributions via image fusion and least squares optimization. Experiments on man-made and real-life data verify the efficiency of the proposed method and demonstrate the advantages over the other main methods.展开更多
In this paper we have accomplished one of the tasks of cognitive radio i.e. dynamic spectrum sensing by using wavelet based Synchrosqueezing transform [1], a novel technique, which was proposed to analyze a signal in ...In this paper we have accomplished one of the tasks of cognitive radio i.e. dynamic spectrum sensing by using wavelet based Synchrosqueezing transform [1], a novel technique, which was proposed to analyze a signal in time-frequency plane. The distinctive feature of this transform compared to other techniques is that it enables us to decompose amplitude and frequency modulated signals and allows individual reconstruction of these components. The objective is also to separate the occupied band into amplitude modulated and frequency modulated bands.展开更多
The demodulation algorithm for AM-FM sinusoids proposed in reference[1]canonly deal with complex exponential sequences.In real applications,one needs to con-struct an analytic signal from real samples.Influenced by th...The demodulation algorithm for AM-FM sinusoids proposed in reference[1]canonly deal with complex exponential sequences.In real applications,one needs to con-struct an analytic signal from real samples.Influenced by the Hilbert Transformation,thecomplex noise in the analytic signal is colored.This paper analyzes the colored noise andthe disadvantage caused by the noise in the estimations of the instantaneous frequencyand amplitude factor using the algorithm in[1]and formulates a practical algorithm.展开更多
文摘In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose frequencies have overlapped regions in Fourier transform domain and even have crossed points in time-frequency distribution (TFD) so that the proposed TVBF seems like a “soft-cutter” that cuts the frequency domain to snaky slices with rational physical sense. First, the Hilbert transform based decomposition is analyzed for the analysis of nonstationary signals. Based on the above analysis, a hypothesis under a certain condition that AM-FM components can be separated successfully based on Hilbert transform and the assisted signal is developed, which is supported by representative experiments and theoretical performance analyses on a error bound that is shown to be proportional to the product of frequency width and noise variance. The assisted signals are derived from the refined time-frequency distributions via image fusion and least squares optimization. Experiments on man-made and real-life data verify the efficiency of the proposed method and demonstrate the advantages over the other main methods.
文摘In this paper we have accomplished one of the tasks of cognitive radio i.e. dynamic spectrum sensing by using wavelet based Synchrosqueezing transform [1], a novel technique, which was proposed to analyze a signal in time-frequency plane. The distinctive feature of this transform compared to other techniques is that it enables us to decompose amplitude and frequency modulated signals and allows individual reconstruction of these components. The objective is also to separate the occupied band into amplitude modulated and frequency modulated bands.
文摘The demodulation algorithm for AM-FM sinusoids proposed in reference[1]canonly deal with complex exponential sequences.In real applications,one needs to con-struct an analytic signal from real samples.Influenced by the Hilbert Transformation,thecomplex noise in the analytic signal is colored.This paper analyzes the colored noise andthe disadvantage caused by the noise in the estimations of the instantaneous frequencyand amplitude factor using the algorithm in[1]and formulates a practical algorithm.
