98 results about "Cross term" patented technology

One embodiment of the present invention relates to an adaptive filtering apparatus comprising first and second real valued adaptive filters, respectively configured to receive an adaptive filter input signal based upon a transmission signal in a transmission path. The first real valued adaptive filter is configured to operate a real valued adaptive filter algorithm on the input signal to estimate a first intermodulation noise component (e.g., an in-phase component) in a desired signal and to cancel the estimated noise. The second real valued adaptive filter is configured to operate a real valued adaptive filter algorithm on the input signal to estimate a second intermodulation noise component (e.g., a quadrature phase component) in the desired signal and to cancel the estimated noise. Accordingly, each filter operates a real valued adaptive algorithm to cancel a noise component, thereby removing complex cross terms between the components from the adaptive filtering process.

A system for embedding auxiliary digital information into a primary digital signal to form a composite digital signal. Auxiliary data bits modulate a pseudo-random (e.g., PN) sequence to provide an auxiliary data sequence used to modify the Least Perceptually Significant Bits (LPSBs) of successive multi-bit samples of the primary signal. In a cross-term compensation embodiment, a correlation between the PN sequence and the sample bits is determined, and compared to the auxiliary data bits to determine whether there is a desired correspondence. The LPSBs in the samples are toggled, if necessary, to provide the desired correspondence. The selection of LPSBs to modify accounts for a desired noise level of the auxiliary data in the primary signal. LPSBs may be modified based on a sparse PN sequence to achieve the desired noise level and conceal the presence of the auxiliary data.

The invention discloses a rolling bearing failure diagnosis method base on vibration temporal frequency analysis. The method comprises the following steps: utilizing a vibration acceleration sensor to collect vibration signals of the rolling bearing under a normal condition and a failure condition; utilizing a modified inherent time scale resolving method to resolve the collected vibration signals, and generating a plurality of inherent time scale components and residual signals; calculating relativity of the time scale components and the vibration signals, selecting the inherent time scale components of which the relativity is ranked top 5 as related components, and rejecting noise signals and false components; calculating Wigner distribution of the related components respectively, and conducting linear stack to obtain the Wigner temporal frequency figure of the original signal; extracting difference fractal box dimensionality of the Wigner temporal frequency figure and the image entropy as failure characteristics; utilizing mahalanobis distance to build mapping relation of the failure characteristics and failure types to realize failure diagnosis. According to the invention, interference of Wigner distribution cross terms is avoided; two kinds of representative failure characteristics of the difference fractal box dimensionality and the image entropy are confirmed.

The invention discloses a time-varying non-stable-signal time-frequency analyzing method. The method comprises the steps of providing definitions of single-frequency signals and i(th)-level local extremum, conducting single-frequency signal decomposition on the time-varying non-stable signals according to the definitions so as to obtain a plurality of single-frequency components, then obtaining a plurality of instantaneous frequency and instantaneous amplitude of the components by Hilbert, further, drawing the amplitude on a time-frequency plane in an amplitude-frequency-time three-dimensional space to obtain a Hilbert amplitude spectrum, and conducting integration on the time through the amplitude spectrum to obtain a Hilbert marginal spectrum. According to the method, the complete adaptability is realized; meanwhile, the method is not restricted by uncertainty principle, and the time and the frequency have high resolution ratio; the decomposition is thorough, so that the respectively independent single-frequency components, namely the substantive characteristics, are obtained, and cross terms among all the components do not exist; and the calculating time is saved as the accelerating method is adopted in the decomposition process.

The invention discloses a seismic spectrum imaging method based on deconvolution generalized S transform, which comprises the steps that a generalized S transform spectrum is acquired by performing two-dimensional convolution on Wigner distribution of original signals and a Gaussian window, a transform spectrum is acquired by performing generalized S transform on seismic data, and time-frequency distribution of the original signal can be acquired through deconvolution when the generalized S transform spectrum and the Wigner distribution of a window function are known. The seismic spectrum imaging method combines advantages of generalized S transform and Wigner-Ville distribution, generation of a cross term of the Wigner-Ville distribution is effectively suppressed through a generalized S transform window, and the generalized S transform spectrum is enabled to acquire high time-frequency aggregation at the same time; and deconvolution generalized S transform can adaptively adjust an analysis window along with variations of a frequency component, is applicable to time-frequency analysis for unstable seismic data, and can acquire high time-frequency resolution; and the seismic spectrum imaging method is applied to detecting the oil-gas possibility of a reservoir, thereby being conducive to improving the reservoir prediction accuracy.

The invention provides a radar signal intra-pulse modulation identification method, which comprises the following steps of performing Cohen type time-frequency distribution processing on an intercepted radar signal to obtain a time-frequency image, preprocessing the time-frequency image, namely adjusting the size and the amplitude, and then grouping and tagging to make a training set and a test set, designing a deep convolutional neural network, adjusting the size and the amplitude of a radar signal of an unknown modulation type, putting the radar signal into the trained deep convolutional neural network, and automatically judging the radar signal type by the network to complete identification. Compared with a kernel function in the Choi-Williams distribution, the time-frequency analysis kernel function provided by the invention has a better effect on inhibiting the cross terms of radar signals, and the signal robustness characteristic is more obvious. The classification network is pre-trained by using the convolutional de-noising auto-encoder, so that the information loss of signal energy caused by time-frequency image preprocessing can be avoided, the classification accuracy of the whole system is improved, and the method is simple to operate and is easier to implement.

The invention relates to random model updating method based on an interval response surface model. The method is characterized by including the steps of firstly, building a second-order polynomial response surface model without cross terms according to experiment design and regression analysis; secondly, using a square completing method to convert a polynomial response surface expression into perfect square; thirdly, substituting interval parameters into the response surface expression to allow the definite response surface model to be changed into the interval response surface model; fourthly, performing interval calculation on the interval response surface model to obtain predicted structural response intervals, and combining the predicted structural response intervals with actual response intervals to build a target function; fifthly, building a optimization inversion problem to identify interval distribution of parameters. By the method, the expansion problem of interval calculation is avoided, fast calculation of structural response intervals is considered, finite element analyzing calculation and sensitivity matrix building during (interval) random model updating are avoided, a large amount of calculation time and cost is saved, and ill-conditioned optimization is avoided as much as possible.

The invention discloses a method for inhibiting cross terms in the time-frequency distribution of multi-component linear frequency modulation (LFM) signals based on subspace decomposition, which belongs to the technical field of signal processing. In the method, a time-frequency distribution matrix comprising noises and the cross terms is decomposed into signal subspaces and noise subspaces by utilizing singular value decomposition (SVD)-based subspace decomposition. For the problem that the linear modulation signals occupy relatively more bandwidths to make singular values are reduced at relatively lower rates and cannot be separated from the noises effectively, an angle of inclination of the time-frequency distribution of the signals can be obtained by utilizing Wigner-Hough transform. The time-frequency distribution of the signals is rotated according to the obtained angle so as to be parallel to a time base. The method is characterized in that: the singular values are rapidly decreased to zero, and then the signal sub-spaces can be separated effectively. By the method, the cross terms and the noises in the time-frequency distribution of the multi-component LFM signals are inhibited without reducing time-frequency resolution; therefore, the method is vast in application prospect.

The invention discloses a traveling wave time-frequency analysis method based on variational mode decomposition and Wigner-ville distribution. The method includes the following steps that: fault traveling wave signals are detected, and Karebauer phase-mode transformation is performed on three-phase voltage traveling wave signals, so that a traveling wave aerial mode component is obtained; variational mode decomposition is performed on the traveling wave aerial mode component, so that K intrinsic mode components are generated; Wigner-Ville analysis is performed on each intrinsic mode component;and the Wigner-Ville distribution of each intrinsic mode component is linearly superimposed, so that the time-frequency domain distribution of original traveling wave aerial mode signals is obtained.With the method of the invention adopted, the interference of cross terms in Wigner-Ville distribution can be effectively suppressed; a good noise suppression effect enabling good VMD (Variational Mode Decomposition) is preserved; time-frequency resolution enabling high Wigner-Ville distribution and good time-frequency aggregation are preserved; traveling wave time-frequency domain information characteristics are truly and accurately represented; and fault traveling waves can be completely observed. The method is of important theoretical and practical significance for the practical application of fault traveling wave protection and positioning.

The invention discloses the estimation of the arrival angle of a narrowband frequency modulation signal based on time frequency analysis during data loss, and overcomes a technical difficulty that the estimation of an arrival angle is not accurate because of data loss. The implementation process of the estimation comprises the steps: obtaining the time frequency distribution of the narrowband frequency modulation signal during data loss; reducing cross terms and inhibition-type noise through a multi-sensor adaptive optimal core function; obtaining another expression of an instantaneous autocorrelation function; carrying out the building of a compressed sensing model, and obtaining the time frequency distribution of a core function; and obtaining the estimation value of the arrival angle of the narrowband frequency modulation signal based on the time frequency analysis during data loss through employing an STF-MUSIC algorithm. The method reduces the cross terms and the inhibition-type noise, enables the average value of time frequency distribution to be more stable, and obtains a higher-resolution estimated arrival angle. The invention provides the more accurate method for estimating the arrival angle of the narrowband frequency modulation signal based on time frequency analysis during data loss, and the method can be widely used in the fields of radar, sonar, communication and biomedicine.

The invention provides a method for analyzing time and frequencies of micro-movement signals of human objects on the basis of terahertz radar echoes. The method includes building human object echo signal models, discrete echo signals and kernel functions of terahertz radar multi-scattering centers; computing bilinear functions and discrete convolution of the bilinear functions and the kernel functions; performing discrete Fourier transform on discrete convolution results at each discrete frequency point m; acquiring distribution time-frequency domain spectrograms B of the echo signals. Movement periods and frequencies of the arms and the legs of the human objects can be observed from the spectrograms, and the gait of the human objects can be recognized. The method has the advantage that problems of severe cross terms of a WD (Wigner distribution) method, decrease of resolution of a PWD (pseudo-Wigner distribution) method and poor energy accumulation performance and insufficient resolution of an STFT (short-time Fourier transform) method when time and frequencies of echo signals of human objects are analyzed can be solved by the aid of the method.

The invention discloses a fast estimation method for a non-uniform rotating target motion parameter. The method comprises specific steps: an echo signal in a single distance unit is modeled into a QFMsignal; a correlation function is constructed, the QFM signal is converted to a time-chirp distribution plane, and minimum entropy criterion-based weighted least squares Radon transform is adopted toestimate secondary chirp; the estimation accuracy and the robustness of the algorithm in a low signal-to-noise ratio environment are improved effectively at the same time; TFD-based weighted least squares Radon transform is adopted to estimate linear chirp, and through filtering, cross-term interference is eliminated; and according to the geometric information in a distance-Doppler domain, the center frequency is directly estimated. The calculation amount of the target motion parameter estimation process can be significantly reduced, the influences of transmission errors on estimation of eachmotion parameter are effectively avoided, and the final target parameter estimation precision and the ISAR imaging quality are ensured.

The invention provides a method for extracting a fluid factor. The method comprises the following steps of: detecting at least one channel seismic data in a work area; setting an initial value of a fractional order number used for fractional order Fourier transform; calculating the fractional order Fourier transform of one channel seismic data in the at least one channel seismic data according tothe fractional order number; calculating an analytic signal of the fractional order Fourier transform, and Wigner-Ville distribution with suppressed cross terms of the analytic signal; calculating Radon transform of a band window of the Wigner-Ville distribution with the suppressed cross terms; reconstructing a time frequency distribution map according to the Radon transform of the band window; and extracting the fluid factor from the time frequency distribution map. According to the method for extracting the fluid factor, the fluid factor can be accurately extracted from seismic waves even though the seismic waves have a dispersion effect.

The invention discloses a digital pre-distortion processing method and device. The method and the device are used for effectively correcting a distortion characteristic of a power amplifier and solving the problem that existing DPD (Digital Pre-Distortion) processing performance is insufficient under a broadband signal. The method comprises the steps of determining a digital pre-distortion model according to an input signal and determining an output signal of the power amplifier according to the digital pre-distortion model, wherein the digital pre-distortion model comprises a conjugate cross term and a nonlinear memory term; and calculating a DPD coefficient according to the output signal.

The invention relates to a non-parametric-search radar maneuvering target long-term coherent integration method and belongs to the technical field of radar signal processing. The method includes the steps of firstly, performing Fourier transform on pulse-compressed radar data along distance to obtain distance frequency-interphase slow time two-dimensional data; performing non-uniform sampling order-reducing operation and time coordinate axis scale transform; thirdly, performing distance frequency inverse Fourier transform and non-uniform sampling slow time Fourier transform to allow a maneuvering target to form a peak value in a two-dimensional plane and achieve long-term coherent integration. The method has the advantages that high-order phase signals are reduced to first-order phase signals through one non-uniform sampling order-reducing operation, cross term influence is lowered as compared with a traditional gradual order-reducing method, and parameter estimation precision is increased; parametric search matching calculation is not needed, operation quantity is lowered, and the method is applicable to engineering; distance and high-order Doppler migration can be compensated atthe same time, long-term coherent integration can be achieved, and the maneuvering target detecting ability of a radar in a strong clutter background is increased.

The invention provides a mixed double-frequency digital pre-distortion model method based on the DDR. The method includes the following steps that a concurrent double-frequency DPD system based on indirect learning is built; an operation sequence is optimized, a one-dimensional LUT achievement model of the 2D-MP is obtained, a one-dimensional LUT achievement model of 2D-MMP is derived, and then aone-dimensional LUT high-frequency concurrent double-frequency DPD achievement scheme and a model are provided; a dynamic deviation function is substituted into a one-order model of the DDR, and a discrete equivalent baseband expression of the one-order DDR model is obtained and extended to be in a synchronic double-frequency mode; a two-dimensional memory polynomial model same-order envelope cross term is introduced, and a new model and a one-dimensional LUT achievement method of the model in an FPGA are obtained. According to the method, the modeling accuracy and the linearization performance better than a simplified two-dimensional memory polynomial model and a traditional DDR model can be obtained; meanwhile, the advantage that achievement can be conveniently conducted through a one-dimensional lookup table is kept, the new model has a good level in the aspects of performance and complexity, and the method is suitable for practical system applying.

The invention provides an ISAR (Inverse Synthetic Aperture Radar) imaging method for a complex moving target. Through polynomial phase optimization estimation on each dominant scatterer distance unitecho signal after translational compensation and polynomial phase signal time frequency decomposition, each signal component obtained by decomposition is a single component only corresponding to one frequency point at any time, the defect that cross terms exist in a non-signal component corresponding to multiple frequency points at one time in the traditional time frequency transform is overcome,each dominant scatterer distance unit echo signal has no any cross term interference and building of time frequency distribution with good time frequency joint resolution is realized finally, and range-instantaneous Doppler imaging is thus obtained. The principle is simple, the operation is convenient, bad influences of cross term interference in the classical time frequency analysis method and losses of the time frequency joint resolution are overcome effectively, the quality and the benefits of nonstationary polynomial phase signal time frequency analysis are effectively enhanced, and a target image with good quality and good resolution is obtained.

The invention relates to a radar high maneuvering target phase-coherent accumulation detection method based on time-reversed non-uniform sampling, and belongs to the technical field of radar signal processing and detecting. The radar high maneuvering target phase-coherent accumulation detection method comprises the steps that first, radar echo pulse pressure is performed, time reversal is matchedwith Fourier transform, and slow time second-order phase compensation is performed; fast time dimension Fourier transform is performed to obtain distance frequency-slow time data, and frequency second-order phase compensation is performed; then, non-uniform sampling order reduction and variable scale scaling change are performed; inverse Fourier transform and the Fourier transform are performed inthe distance frequency and time dimension separately to realize long-term phase-coherent accumulation; and finally, the detection statistics are constructed to detect and estimate a maneuvering target. The radar high maneuvering target phase-coherent accumulation detection method can effectively accumulate maneuvering target signals with a high-order phase, compensate the distance and Doppler migration and improve the detection capability of the radar maneuvering target; meanwhile, the influence of a cross term of a traditional successive order reduction method is reduced, and the parameter estimation accuracy is improved; and no multi-dimensional motion parameter search matching calculation is needed, the amount of calculation is reduced, and the radar high maneuvering target phase-coherent accumulation detection method is suitable for engineering application.

Provided is a Wigner higher-order spectrum seismic signal spectral decomposition method based on matching pursuit. The method is characterized by, to begin with, reading a seismic section and selecting the atom type; then, selecting seismic data; carrying out complex seismic trace analysis of the signal and carrying out global search on the scale factor to determine an initial parameter set of atoms; carrying out local search on the parameter set to find out the atom most matched with the signal; calculating the diagonal slice spectrum of the Wigner higher-order spectrum of the most matched atom; calculating the residual error of the signal projected in the direction of the most matched atom, and taking the residual error as a new decomposition signal; and carrying out summation on the diagonal slice spectrums of the Wigner higher-order spectrums of the atoms obtained through decomposition, taking the sum as Wigner higher-order spectrum time-frequency spectrum of the seismic data, and cutting a single frequency slice; and obtaining spectral decomposition result by carrying out the same method on all seismic data. The cross terms of the Wigner higher-order spectrum are removed by utilizing the matching pursuit method, so that the earthquake spectral decomposition result having higher time-frequency aggregativeness can be obtained; and the method can provide more accurate information for the follow-up seismic reservoir prediction and fluid identification.

The invention discloses a high resolution linear time-frequency analysis method. An iteration self-adaption method is adopted to obtain a high resolution instantaneous frequency estimation result, compared with a linear time-frequency analysis method, the time-frequency two-dimensional resolution ratio is substantially improved, and the two-dimensional distinction degree of multiple signals in a time-frequency domain can be remarkably improved; and linear transformation is adopted to perform analysis and processing on the signals, and compared with a nonlinear time-frequency analysis method, influence of cross terms is effectively eliminated, and a time-frequency analysis and processing effect of multiple time-frequency aliasing signals can be further improved. A convergent time-frequency analysis result can be obtained through a few iterations, and has good performance of analyzing and processing linear frequency modulation signals and nonlinear frequency modulation signals, and compared with other improved type time-frequency analysis method, the calculated amount is substantially reduced and the applicability is stronger.

The invention provides a polynomial phase signal adaptive time frequency transformation method based on genetic optimization. Time frequency decomposition of polynomial phase signals can be finished,every signal component obtained by decomposition only corresponds to a single component of a frequency point at any time, then values are obtained by the various signal components and instantaneous frequency at any time, signal frequency distribution corresponding to the corresponding time is directly calculated and generated by only retaining a main lobe responded Sinc function, the shortcoming that cross terms exist due to the fact that one time corresponds to non-single components of a plurality of frequency points in the traditional time frequency transformation is overcome, and thus, timefrequency distribution which does not have any cross term interference and has excellent time frequency combined resolution is output finally. The polynomial phase signal adaptive time frequency transformation method based on genetic optimization is simple in principle and convenient to operate; adverse impact of cross term interference of a classic time frequency analysis method and loss of timefrequency combined resolution can be overcome effectively, and the quality and benefit of non-stable polynomial phase signal time frequency analysis can be improved effectively.

The invention provides a method for suppressing large-amplitude ionosphere phase diameter disturbance of a high-frequency radar, and the method comprises the following steps of establishing a pollution model of broadened sea clutter by taking the high-frequency sky-ground wave radar as a background, and providing a method for suppressing large-amplitude ionosphere phase diameter disturbance basedon a generalized parametric time-frequency analysis (GPTF) algorithm, and further verifying the effectiveness of the proposed method through simulation. Theoretical analysis and simulation experimentsshow that the processing method can effectively suppress the large-amplitude ionospheric phase diameter disturbance, and the method for suppressing large-amplitude ionosphere phase diameter disturbance of the high-frequency radar has the advantages of high resolution and no cross terms.

The invention discloses an ISAR imaging method based on time-frequency analysis of deep learning. Firstly, an ISAR imaging model is set up, and echo data is generated by simulation; secondly, WVD transformation and short-time Fourier transformation are carried out on the echo data to generate training data; thirdly, a network model is set up, and the network model is trained with the generated training data; and finally, the trained network model and the ISAR imaging model are combined for imaging. The method provided by the invention improves the frequency resolution of the time-frequency distribution map, can suppress cross-terms, and improves the resolution of ISAR imaging.