To address the low-resolution imaging problem in relation to traditional Range Doppler(RD)algorithm,this paper intends to propose a new algorithm based on Fractional Fourier Transform(FrFT),which proves highly advanta...To address the low-resolution imaging problem in relation to traditional Range Doppler(RD)algorithm,this paper intends to propose a new algorithm based on Fractional Fourier Transform(FrFT),which proves highly advantageous in the acquisition of high-resolution Synthetic Aperture Radar(SAR)images.The expression of the optimal order of SAR range signals using FrFT is deduced in detail,and the corresponding expression of the azimuth signal is also given.Theoretical analysis shows that,the optimal order in range(azimuth)direction,which turns out to be very unique,depends on the known imaging parameters of SAR,therefore the engineering practicability of FrFT-RD algorithm can be greatly improved without the need of order iteration.The FrFT-RD algorithm is established after an analysis of the optimal time-frequency transform.Experimental results demonstrate that,compared with traditional RD algorithm,the main-lobe width of the peak-point target of FrFT-RD algorithm is narrow in both range and azimuth directions.While the peak amplitude of the first side-lobe is reduced significantly,those of other side-lobes also drop in various degrees.In this way,the imaging resolution of range and azimuth can be increased considerably.展开更多
The ground roll and body wave usually show significant differences in arrival time, frequency content, and polarization characteristics, and conventional polarization filters that operate in either the time or frequen...The ground roll and body wave usually show significant differences in arrival time, frequency content, and polarization characteristics, and conventional polarization filters that operate in either the time or frequency domain cannot consider all these elements. Therefore, we have developed a time-frequency dependent polarization filter based on the S transform to attenuate the ground roll in seismic records. Our approach adopts the complex coefficients of the S transform of the multi-component seismic data to estimate the local polarization attributes and utilizes the estimated attributes to construct the filter function. In this study, we select the S transform to design this polarization filter because its scalable window length can ensure the same number of cycles of a Fourier sinusoid, thereby rendering more precise estimation of local polarization attributes. The results of applying our approach in synthetic and real data examples demonstrate that the proposed polarization filter can effectively attenuate the ground roll and successfully preserve the body wave.展开更多
In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effect...In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.展开更多
Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributio...Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.展开更多
The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improv...The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.展开更多
This paper introduces the localized Radon transform (LRT) into time-frequency distributions and presents the localized Radon-Wigner transform (LRWT). The definition of LRWT and a fast algorithm is derived, the propert...This paper introduces the localized Radon transform (LRT) into time-frequency distributions and presents the localized Radon-Wigner transform (LRWT). The definition of LRWT and a fast algorithm is derived, the properties of LRWT and its relationship with Radon-Wigner transform, Wigner distribution (WD), ambiguity function (AF), and generalized-marginal time-frequency distributions are analyzed.展开更多
With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communicat...With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.展开更多
Non-cooperative communication detection is a key technology for locating radio interfer-ence sources and conducting reconnaissance on adversary radiation sources.To meet the require-ments of wide-area monitoring,a sin...Non-cooperative communication detection is a key technology for locating radio interfer-ence sources and conducting reconnaissance on adversary radiation sources.To meet the require-ments of wide-area monitoring,a single interception channel often contains mixed multi-source sig-nals and interference,resulting in generally low signal-to-noise ratio(SNR)of the received signals;meanwhile,improving detection quality urgently requires either high frequency resolution or high-time resolution,which poses severe challenges to detection techniques based on time-frequency rep-resentations(TFR).To address this issue,this paper proposes a fixed-frame-structure signal detec-tion algorithm that integrates image enhancement and multi-scale template matching:first,the Otsu-Sauvola hybrid thresholding algorithm is employed to enhance TFR features,suppress noise interference,and extract time-frequency parameters of potential target signals(such as bandwidth and occurrence time);then,by exploiting the inherent time-frequency characteristics of the fixed-frame structure,the signal is subjected to multi-scale transformation(with either high-frequency resolution or high-time resolution),and accurate detection is achieved through the corresponding multi-scale template matching.Experimental results demonstrate that under 0 dB SNR conditions,the proposed algorithm achieves a detection rate greater than 87%,representing a significant improvement over traditional methods.展开更多
The linear transmission impairments,such as the timing offset(TO),frequency offset(FO),and chromatic dispersion(CD),are major factors of signal degradations in coherent optical fiber communication systems.The estimati...The linear transmission impairments,such as the timing offset(TO),frequency offset(FO),and chromatic dispersion(CD),are major factors of signal degradations in coherent optical fiber communication systems.The estimation and compensation of such impairments play significant roles in the receiver side digital signal processing(DSP)unit.In this paper,we propose to combat the linear impairments systematically(including TO,FO and CD)with a joint timefrequency signal processing by taking the advantage of fractional Fourier transform(FrFT).In view of geometrical analysis,TO/FO induces a shift in time/frequency coordinate and the CD leads to the rotation in the fractional domain.Both mathematical derivations and geometrical interpretations have been established to unveil the relationships between impairments and linear frequency modulated(LFM)training symbols(TSs).By considering a typical coherent optical orthogonal frequency-division multiplexing(COOFDM)transmission system,three kinds of linear impairments have been jointly estimated by simple geometric calculations using appropriately designed TS based on FrFTs.Simulation and experimental results confirmed the feasibility of time-frequency techniques with better accuracy,less complexity,and improved spectral efficiency.展开更多
基金supported by the 13th Five-Year Plan for Jiangsu Education Science(D/2020/01/22)JSPIGKZ(JSPI19GKZL405)Natural Science Research Projects of Colleges and Universities in Jiangsu Province(19KJB510022).
文摘To address the low-resolution imaging problem in relation to traditional Range Doppler(RD)algorithm,this paper intends to propose a new algorithm based on Fractional Fourier Transform(FrFT),which proves highly advantageous in the acquisition of high-resolution Synthetic Aperture Radar(SAR)images.The expression of the optimal order of SAR range signals using FrFT is deduced in detail,and the corresponding expression of the azimuth signal is also given.Theoretical analysis shows that,the optimal order in range(azimuth)direction,which turns out to be very unique,depends on the known imaging parameters of SAR,therefore the engineering practicability of FrFT-RD algorithm can be greatly improved without the need of order iteration.The FrFT-RD algorithm is established after an analysis of the optimal time-frequency transform.Experimental results demonstrate that,compared with traditional RD algorithm,the main-lobe width of the peak-point target of FrFT-RD algorithm is narrow in both range and azimuth directions.While the peak amplitude of the first side-lobe is reduced significantly,those of other side-lobes also drop in various degrees.In this way,the imaging resolution of range and azimuth can be increased considerably.
基金supported by the National Science and Technology Major Project of China(Grant No.2011ZX05014 and 2011ZX05008-005)
文摘The ground roll and body wave usually show significant differences in arrival time, frequency content, and polarization characteristics, and conventional polarization filters that operate in either the time or frequency domain cannot consider all these elements. Therefore, we have developed a time-frequency dependent polarization filter based on the S transform to attenuate the ground roll in seismic records. Our approach adopts the complex coefficients of the S transform of the multi-component seismic data to estimate the local polarization attributes and utilizes the estimated attributes to construct the filter function. In this study, we select the S transform to design this polarization filter because its scalable window length can ensure the same number of cycles of a Fourier sinusoid, thereby rendering more precise estimation of local polarization attributes. The results of applying our approach in synthetic and real data examples demonstrate that the proposed polarization filter can effectively attenuate the ground roll and successfully preserve the body wave.
基金This work was funded by National Natural Science Foundation of China-（No. 40474044）.
文摘In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.
基金This work was supported by the National Natural Science Foundation of China(91538201)the Taishan Scholar Project of Shandong Province(ts201511020)the project supported by Chinese National Key Laboratory of Science and Technology on Information System Security(6142111190404).
文摘Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.
基金Supported by the National Science Foundation of China(42055402)。
文摘The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.
文摘This paper introduces the localized Radon transform (LRT) into time-frequency distributions and presents the localized Radon-Wigner transform (LRWT). The definition of LRWT and a fast algorithm is derived, the properties of LRWT and its relationship with Radon-Wigner transform, Wigner distribution (WD), ambiguity function (AF), and generalized-marginal time-frequency distributions are analyzed.
基金supported by Natural Science Foundation of China(No.62371231)Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20222001Jiangsu Provincial Key Research and Development Program(No.BE2023027).
文摘With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.
文摘Non-cooperative communication detection is a key technology for locating radio interfer-ence sources and conducting reconnaissance on adversary radiation sources.To meet the require-ments of wide-area monitoring,a single interception channel often contains mixed multi-source sig-nals and interference,resulting in generally low signal-to-noise ratio(SNR)of the received signals;meanwhile,improving detection quality urgently requires either high frequency resolution or high-time resolution,which poses severe challenges to detection techniques based on time-frequency rep-resentations(TFR).To address this issue,this paper proposes a fixed-frame-structure signal detec-tion algorithm that integrates image enhancement and multi-scale template matching:first,the Otsu-Sauvola hybrid thresholding algorithm is employed to enhance TFR features,suppress noise interference,and extract time-frequency parameters of potential target signals(such as bandwidth and occurrence time);then,by exploiting the inherent time-frequency characteristics of the fixed-frame structure,the signal is subjected to multi-scale transformation(with either high-frequency resolution or high-time resolution),and accurate detection is achieved through the corresponding multi-scale template matching.Experimental results demonstrate that under 0 dB SNR conditions,the proposed algorithm achieves a detection rate greater than 87%,representing a significant improvement over traditional methods.
文摘The linear transmission impairments,such as the timing offset(TO),frequency offset(FO),and chromatic dispersion(CD),are major factors of signal degradations in coherent optical fiber communication systems.The estimation and compensation of such impairments play significant roles in the receiver side digital signal processing(DSP)unit.In this paper,we propose to combat the linear impairments systematically(including TO,FO and CD)with a joint timefrequency signal processing by taking the advantage of fractional Fourier transform(FrFT).In view of geometrical analysis,TO/FO induces a shift in time/frequency coordinate and the CD leads to the rotation in the fractional domain.Both mathematical derivations and geometrical interpretations have been established to unveil the relationships between impairments and linear frequency modulated(LFM)training symbols(TSs).By considering a typical coherent optical orthogonal frequency-division multiplexing(COOFDM)transmission system,three kinds of linear impairments have been jointly estimated by simple geometric calculations using appropriately designed TS based on FrFTs.Simulation and experimental results confirmed the feasibility of time-frequency techniques with better accuracy,less complexity,and improved spectral efficiency.
