79 results about "Frequency axis" patented technology

The invention relates to a bridge structure vibration monitoring and analysis method based on artificial intelligence, and the method specifically comprises the following steps: setting an acceleration sensor network at a key part of a bridge, collecting and marking a vibration signal sample, and forming a data set; a high-resolution time-frequency matrix of sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to spectrum kurtosis, and the time-frequency matrix is enhanced by the resonance frequency band subjected to adaptive gain enhancement; dividing a matrix frequency axis multi-scale binary tree, calculating and normalizing sub-band average energy, quantifying energy distribution uniformity through information entropy, and splicing multi-scale entropy values into feature vectors; then constructing and training a bridge detection data model; and finally, preprocessing new data, inputting the preprocessed new data into the trained model, and automatically analyzing to obtain a bridge health monitoring result. Through time-frequency optimization, multi-scale entropy and deep learning technologies, the problems of large noise, difficult feature extraction and low automation and accuracy of traditional monitoring can be solved.

The invention discloses an unmanned aerial vehicle radio frequency fingerprint identification method and device, a medium and equipment, and relates to the technical field of computers. According to a time-frequency graph corresponding to a radio frequency signal, time domain convolution is carried out by sliding along a time axis of the time-frequency graph, time domain features of the time-frequency graph are extracted, frequency domain convolution is carried out by sliding along a frequency axis of the time-frequency graph, frequency domain features of the time-frequency graph are extracted, and heterogeneous combined convolution kernels with different sizes are adopted in the time domain convolution and the frequency domain convolution, so that the time-frequency graph is more accurate. Therefore, time-frequency anisotropic convolution is realized, and short-time dynamic evolution and local energy distribution characteristics are respectively captured. According to the method, the uniform operation defect of traditional isotropic convolution is avoided, parameter redundancy is reduced and feature discrimination is enhanced through physically interpretable dimension decoupling, and the accuracy of radio frequency fingerprint identification of the unmanned aerial vehicle is improved.

The invention relates to the technical field of data processing, and discloses a signal intra-pulse modulation pattern recognition method and system, and the method comprises the steps: receiving a radar pulse signal; performing short-time Fourier transform on the radar pulse signal; in the time-frequency matrix, calculating an energy distribution gradient of each time point along a frequency axis; according to the energy distribution gradient of each time point, identifying gradient abrupt change points; determining a filtering bandwidth boundary based on the gradient catastrophe point by adopting a negative feedback regulation mechanism of bandwidth and signal-to-noise ratio; based on the filtering bandwidth boundary, performing bandwidth filtering on the radar pulse signal; calculating an instantaneous frequency, a first-order differential frequency and a second-order differential frequency of the filtered signal through Hilbert transform, and forming a feature vector; predicting a cluster number range through a long and short term memory neural network according to the feature vectors; according to the feature vector, through a K-Means clustering algorithm, carrying out clustering analysis in a clustering number range; and outputting a modulation style label according to a clustering analysis result.

The invention discloses a mirror image rejection method and device based on spectrum entropy, and belongs to the technical field of radio frequency signal processing. Multiple groups of frequency spectrums are collected by moving a local oscillator, the frequency spectrums are mapped to a unified reference frequency axis to achieve frequency alignment, a spectrum entropy sequence of the same frequency point after alignment is calculated, and the problem that the spectrum entropy fluctuation of the same frequency point is large due to nonlinear deviation of the local oscillator frequency of a mirror image signal is solved. And accurate identification and suppression of the mirror image signal are realized due to the difference of spectrum entropy stability caused by linear offset of the useful signal. According to the method, multiple groups of frequency spectrums are mapped to a unified reference axis through frequency alignment, high-precision mirror image suppression is effectively realized by utilizing the characteristics of large fluctuation of mirror image signals and stable useful signals in a spectrum entropy sequence of the same frequency point, the distinguishing capability in a complex scene is improved, and the problems of high cost and insufficient robustness of an existing mirror image suppression method are solved.

The invention discloses a fault diagnosis method, system, equipment and medium for a wind power gear box, and relates to the technical field of fault detection, and the method comprises the steps: collecting a vibration signal of the wind power gear box, and obtaining a time-frequency diagram of the vibration signal; capsule feature tensors in the time-frequency graph are obtained, a time-frequency two-dimensional network is formed in space, and corresponding query tensors are extracted from a time axis and a frequency axis of the time-frequency two-dimensional network by adopting different convolution kernels; dot product matching is carried out on the corresponding query tensors and a preset shared key value pair, attention distribution of each query tensor is obtained, and aggregation output in the directions of the time axis and the frequency axis is fused through the attention distribution; and performing class activation mapping on the aggregated output, determining a fault category of the wind power gear box, generating a class activation graph focused on the key time-frequency region through the fault category, and displaying a discrimination basis. The method can focus on a key time-frequency region with clear physical significance, and obviously improves the discrimination transparency and engineering credibility of the model.

The invention discloses a time-frequency analysis method based on local entropy optimization synchronous demodulation reassignment transformation, relates to the technical field of signal processing, and solves the problem that a traditional time-frequency analysis method is not easy to carry out when complex non-stable vibration signals generated by rotating machinery and the like are processed. The problems of difficulty in balancing time and frequency resolution, easiness in energy diffusion, difficulty in multi-component signal separation and the like exist; according to the method, zero padding is carried out on the two ends of a target signal, a discrete Gaussian window is constructed to generate a sliding windowing signal matrix, a projection angle sequence is generated in an angle space, a time axis and a frequency axis are discretized, a demodulation function containing undetermined parameters is constructed to generate a complex number time-frequency representation matrix, the Renyi entropy value of time-frequency sub-blocks is calculated to determine an optimal demodulation parameter, and the optimal demodulation parameter is obtained. And setting a frequency difference threshold to construct a frequency redistribution operator to generate an optimized time-frequency matrix. The method is mainly used for time-frequency analysis of complex non-stationary vibration signals in the fields of rotating machinery and the like, and high-precision frequency modulation feature extraction and enhancement processing are achieved.

The invention relates to the technical field of power equipment state monitoring, and discloses a partial discharge detection method and system based on synchronous compression wavelet-synchronous extrusion. The method comprises the following steps: preprocessing a partial discharge signal; calculating a coefficient through continuous wavelet transform and estimating an instantaneous frequency and a chirp rate; synchronous compression is carried out to compensate the phase, and scale remarking is carried out; energy is reallocated to a frequency axis through synchronous extrusion, and a high-aggregation-degree time-frequency diagram is generated; extracting a peak domain based on the saliency spectrum, demodulating along a ridge line, and estimating event parameters; and finally, robust judgment and auditing output are completed by using the joint statistics. According to the method, the sensitivity and robustness of partial discharge signal detection under the conditions of low signal-to-noise ratio and strong interference are remarkably improved.

The invention relates to the technical field of impedance measurement, in particular to an impedance measurement method and system for a common-mode inductor, and the method comprises the steps: obtaining voltage and current signals of the common-mode inductor in a test circuit, and obtaining frequency domain response atlas data containing impedance characteristics through time-frequency transformation; carrying out non-linear segmentation division on the frequency axis to form a plurality of frequency segmentation regions; extracting the phase amplitude of each segmented region, carrying out clustering segmentation, and recognizing an effective impedance frequency band; analyzing phase synchronization characteristics in the effective impedance section, and determining a common-mode resonant frequency point through clustering analysis; and fusing multi-section frequency region division, effective impedance section and resonance point information to finish accurate measurement of the impedance characteristic of the common-mode inductor. According to the method, through non-linear scanning and phase information joint analysis, the recognition precision of impedance response key characteristics in a broadband range is improved, the capturing capacity of parasitic parameters and resonance behaviors is enhanced, and efficient and high-precision comprehensive evaluation of the impedance characteristics of the common-mode inductor is achieved.

The application discloses a rotating machinery characteristic frequency enhancement extraction method based on an optimal weighted envelope spectrum, and comprises the following steps: (1) collecting vibration or noise data of the rotating machinery as a monitoring signal and calculating a spectrum correlation function; (2) normalizing the spectrum correlation function and constructing an enhanced envelope spectrum; (3) determining a basic modulation frequency; (4) performing slice processing on the spectrum correlation function and estimating a carrier-to-noise ratio; (5) constructing a cyclic stationary measure function of the basic modulation frequency and harmonic components thereof; (6) taking the carrier-to-noise ratio as an initial weighting function and searching for an optimal weighting function by using a cuckoo optimization algorithm; (7) performing weighted processing on the spectrum correlation function by using the optimal weighting function to obtain an optimal weighted spectrum correlation; and (8) integrating the optimal weighted spectrum correlation along a spectrum frequency axis to obtain an optimal weighted envelope spectrum. The application can effectively extract the modulation frequency components in the rotating machinery vibration noise signal under complex and strong noise interference.

A communication device according to an embodiment includes a memory and one or more processors coupled to the memory. The one or more processors are configured to: receive an OFDM signal and convert the OFDM signal into a time-axis waveform signal of a baseband; extract a part of the time-axis waveform signal, and calculate a correlation value between the extracted signal and a known signal; execute FFT on the time-axis waveform signal; extract a part of a frequency-axis waveform signal on which the FFT is executed, and calculate a degree of similarity between the extracted signal and a known signal; and estimate a switching timing between uplink communication and downlink communication in the communication device based on a calculation result of the similarity degree.

This application proposes a high-precision detection method and device for stationary human targets based on a radar life detector. The method involves performing an inverse Fourier transform on the radar's fast time to convert the frequency axis into the range axis, iteratively updating the low-rank matrix and the sparse matrix, updating the dual variables and modifying the penalty parameters, and obtaining the separation result between the target and the background after convergence. The background information is stored in the low-rank matrix, while the signal containing life characteristic information is stored in the sparse matrix, thereby achieving high-precision detection of stationary human targets.

The invention discloses an equipment anomaly monitoring method based on audio signal analysis, which comprises the following steps of: S1, generating a logarithmic Mel spectrogram through time-frequency transformation, generating a gating vector by utilizing a self-adaptive gating module based on median, root mean square and variance, and after weighting fusion with an original spectrogram, splicing supplementary audio features to form a multi-channel fusion feature; and S2, the multi-channel fusion features are partitioned along a time axis and a frequency axis, parallel time domain and frequency domain branches are respectively input, a selective state space model is adopted, the time domain branches capture long-time-history steady-state features and trend anomalies, and the frequency domain branches analyze cross-band energy distribution and a harmonic structure. And S3, performing linear alignment on the two branch features, adding the two branch features element by element to obtain a fusion feature, training a model through a self-supervised ID classification task in combination with an angle interval loss function, and taking a negative logarithmic probability of a target ID category as an abnormal score during reasoning, thereby improving the robustness, generalization ability and accuracy of detection.

The application relates to a fast demodulation system and method for a large-scale microcavity array on a chip, and the system comprises a frequency-sweeping laser, a first fiber coupler, a microcavity array transmission signal arm, an auxiliary interference reference arm, a same-frequency trigger arm and a signal processing device. The first fiber coupler is used for dividing a light source into a first light source and a second light source. The microcavity array transmission signal arm is used for receiving the first light source of the frequency-sweeping laser, and the first light source enters a large-array microcavity sensor device after polarization processing, and is converted into an electric signal after receiving the disturbance of an external sensing signal. The auxiliary interference reference arm is used for receiving the second light source of the frequency-sweeping laser, and two beams of coherent light with opposite phases are obtained after processing the second light source, and then the coherent light is converted into an electric signal. The same-frequency trigger arm is used for transmitting a same-frequency trigger signal emitted by the frequency-sweeping laser. The signal processing device is used for signal acquisition and processing. The application effectively suppresses the frequency-axis distortion and spectral line broadening caused by the frequency-sweeping nonlinearity of the laser, and can be applied to the accurate demodulation of a large-scale, high-Q microcavity sensing array.

The application provides a cross-domain small sample wideband signal detection and recognition method based on a visual basic model, and relates to the cross technical field of electromagnetic signal processing and computer vision. The application reduces the interference of background noise in the wideband time-frequency graph on the candidate area by performing double condition screening based on target confidence and aspect ratio features on the preliminary candidate frame, and combining the field of view expansion in the frequency axis direction, and improves the incomplete coverage problem of the slender signal candidate frame. By extracting multiple intermediate layer features and the final layer feature map of the visual basic model for fusion in the channel dimension, the problem that the local texture information in the wideband time-frequency graph is smoothed or covered in the layer-by-layer abstraction process is solved. The parameters of the visual segmentation model and the visual basic model are kept frozen, and only the small sample fine tuning of the front region proposal network is performed, so that the calculation overhead caused by full fine tuning of the visual basic model during cross-domain small sample adaptation is avoided, and the method is suitable for deployment on devices with limited computing resources.

The invention provides a multi-modulation signal robust identification method based on a time-frequency diagram and data enhancement. Original baseband signals of the traditional modulation mode and the novel modulation mode are generated and preprocessed; performing data set division according to a double sampling mode; performing STFT on the data to generate a time-frequency graph; performing enhancement operations of time axis offset, frequency axis offset, noise injection and tiny rotation on the time-frequency graph of the training set; a ResNet-18 model adaptive to single channel input is constructed; and training the model and carrying out a performance test of unknown signal modulation mode identification. According to the method, the novel data set comprising a traditional modulation mode and an emerging modulation technology is constructed, the defect that most of the current modulation data sets are the traditional modulation mode is overcome, a data basis closer to a future communication scene is provided for modulation recognition in a complex scene, multi-dimensional enhancement is performed on data, data diversity is improved, and the method is suitable for being applied to modulation recognition in a complex scene. The model generalization ability is significantly improved, and the identification precision of the modulation signal is also effectively improved.

The invention relates to the technical field of oil and gas exploration and development, and particularly discloses a robust mixed norm constraint high-dimensional seismic data reconstruction method and device, and the method comprises the steps: carrying out the Fourier transformation of five-dimensional data along a time axis, and obtaining the Fourier transformation data; unfolding 4D data of each frequency slice of the Fourier transform data along a mode-(m, n) to obtain a near-square matrix; a non-convex Frobenius / Nuclear mixed norm and L1 norm combined regularization constraint is applied to the near-square matrix, and a target functional of observation seismic data and reconstruction data is constructed; and alternately solving the target functional by adopting an ADMM algorithm, and carrying out inverse Fourier transform on a solving result along a frequency axis. According to the method, a high-dimensional tensor completion method under the robust Frobenius / Nuclear mixed norm constraint is introduced, accurate representation of nonlinear high-dimensional seismic data is achieved, the seismic data reconstruction and denoising precision is improved, and therefore regularized reconstruction and abnormal noise suppression of the seismic data with the low signal-to-noise ratio are achieved.

The application discloses a GIS device partial discharge wide-frequency electromagnetic signal characteristic analysis method and system, and relates to the technical field of partial discharge detection. The method comprises the following steps: collecting a GIS partial discharge wide-frequency electromagnetic signal, and adopting wavelet transform processing to obtain a time-frequency distribution matrix; uniformly dividing the time-frequency distribution matrix according to a frequency axis to obtain initial sub-frequency bands; calculating the effectiveness indexes of the sub-frequency bands and adaptively aggregating the sub-frequency bands to obtain candidate characteristic intervals; and performing collaborative contribution degree analysis on the candidate characteristic intervals to obtain optimal characteristic intervals. The technical problem that, in the existing GIS partial discharge wide-frequency electromagnetic signal characteristic extraction process, frequency band division relies on experience and lacks an adaptive optimization mechanism, resulting in inaccurate characteristic interval selection and weak anti-interference capability is solved, and the technical effect that the accuracy and anti-interference capability of partial discharge characteristic extraction are improved through adaptive sub-frequency band division of the wide-frequency electromagnetic signal and collaborative analysis of the characteristic intervals is achieved.

The application discloses a kind of railway bed moisture content quantitative detection methods based on low high-frequency energy ratio, comprising: setting multiple transverse detection channels to collect ground penetrating radar signal;The radar signal of each transverse detection channel is continuously wavelet transformed, and the frequency axis in the time-frequency transform result is divided into low frequency band and high frequency band according to preset boundary frequency, and the low high-frequency energy ratio is calculated;The low high-frequency energy ratio mean of each transverse detection channel is calculated in each test area, and the low high-frequency energy ratio mean of multiple transverse detection channels in multiple test areas forms joint sample;According to joint sample, regression model for representing the nonlinear mapping relationship between water content severity index and low high-frequency energy ratio is constructed;The digital determination threshold of different water content grades is calculated according to regression model using interclass geometric midpoint method, establishes bed moisture state grading standard, and outputs moisture state grade.The application can realize accurate quantitative detection and grading of railway bed moisture content.

The invention discloses a method and a system for generating a tunable flat-topped spectrum based on a Flokey singular point. The method comprises the following steps: forming two types of supermodes alternately arranged on a frequency axis by using a pair of coupled optical ring resonators; applying periodic dynamic complex modulation including real and imaginary components to the resonator; configuring the modulation frequency to be matched with the super-mode interval so as to construct a periodically quenched non-Hermite frequency lattice on the synthetic frequency dimension; and adjusting modulation parameters, and driving the quasi-energy band of the crystal lattice to present a linear dispersion relationship at the singular point, thereby generating the flat-topped spectrum. According to the method, the intensity and the broadening speed of the flat-topped spectrum are linearly controlled by adjusting the imaginary part modulation amplitude, and the flat-topped frequency comb formed by a single supermode is obtained by extracting signals at a specific moment in a modulation period. According to the method, flexible electric control tuning of the spectrum form, the broadening speed and the modal component is realized, the system can work under the weak non-Hermite condition, and the practicability and integration of the system are improved.

The application discloses a kind of wind power gear box fault diagnosis method, system, equipment and medium, it is related to fault detection technical field, including steps: the vibration signal of wind power gear box is collected, and the time-frequency diagram of vibration signal is obtained;Capsule feature tensor in time-frequency diagram is obtained, and a time-frequency two-dimensional network is constituted in space, and different convolution kernels are used to extract corresponding query tensor in time axis and frequency axis of time-frequency two-dimensional network;The dot product matching of corresponding query tensor and preset shared key-value pair is carried out respectively, the attention distribution of each query tensor is obtained, and the aggregation output in time axis and frequency axis direction is fused by attention distribution;Class activation mapping is carried out to aggregation output, determines the fault category of wind power gear box, and generates class activation map focusing on key time-frequency region by fault category, shows discrimination basis.The application can focus on key time-frequency region with clear physical meaning, significantly improve the discrimination transparency and engineering credibility of model.

The application relates to a synthetic aperture radar imaging method based on ultra-wideband, and steps are as follows: S1, fixing an ultra-wideband radar on a moving device, collecting ultra-wideband radar baseband data, and acquiring background noise generated by antenna energy leakage; S2, collecting the same moving direction ultra-wideband radar baseband data, and then performing pretreatment; S3, performing two-dimensional fast Fourier transform processing, so that the ultra-wideband radar data is converted from the time domain to the frequency domain; S4, setting a reference distance, calculating a reference function, calculating phase compensation, and focusing a target at the reference distance; S5, realizing stolt interpolation through mapping or bending of a distance frequency axis, and obtaining a basic imaging result after two-dimensional inverse fast Fourier transform; S6, setting a proportional factor function on a depth axis, combining the proportional factor function with the basic imaging result, realizing depth-based energy compensation on a plane image, and finally constructing an environment model contour through extreme value selection. The precision of environment model construction is improved.

The present invention provides an electromagnetic noise analysis device that: is capable of performing electromagnetic noise analysis of an arbitrary observation point on a circuit during electromagnetic noise analysis, of a frequency axis, that takes into consideration various operation conditions, such as current dependency and control, which have conventionally been difficult to express except by using a time axis; and is capable of achieving both shortening of analysis time and improvement of analysis accuracy. The present invention is characterized by comprising: a noise spectrum analysis unit for executing frequency axis noise analysis in which each switch is expressed by a voltage source or a current source of the frequency axis for an arbitrary observation point of a power conversion circuit in which at least two switches are connected between power sources; a voltage source / current source noise source parameter generation unit for generating a parameter table for expressing a switching operation by a voltage source or a current source of the frequency axis, the switching operation corresponding to an operation condition including the power source voltage, the gate resistance of each switch, or a current; a worst condition extraction unit for extracting a current amount and a phase relationship in which a noise amount can be a worst condition during an operation on the time axis of each switch as based on control; and a worst condition noise source model generation unit for setting a noise source parameter for the worst condition using an extraction result by the worst condition extraction unit.

The invention discloses a numerical control machine tool machining error modeling method and system, and the method comprises the steps: collecting temperature rise time sequence data at a key part of a main shaft, representing machining error frequency domain features through workpiece surface topography power spectral density (PSD), and constructing a thermal spectrum neural operator network model; and learning mapping between the temperature rise evolution and the PSD of the surface of the processed workpiece and outputting a prediction result of the PSD of the surface of the workpiece. The network adopts a four-stage structure of continuous time coding, frequency registration, optimal transmission measurement and energy calibration which are respectively used for adapting to non-uniform sampling and dynamic working conditions, modeling spectrum peak drift and frequency axis dislocation, depicting spectrum shape overall migration and local deformation, and ensuring PSD energy consistency through Parseval energy constraint. According to the method, intermediate frequency energy change, spectral peak drift and spectral shape distortion can be accurately described, interpretable support is provided for error traceability and process compensation, and precision and cross-working-condition generalization ability are improved.

The invention relates to a rotary machine modulation feature extraction method based on an adaptive Hoyer-L moment envelope spectrum, and the method comprises the steps: firstly collecting vibration or radiation noise data of a rotary machine as a monitoring signal, calculating a spectrum coherence function of the monitoring signal, and obtaining a double-variable spectrogram; dividing a Nyquist frequency band along a frequency axis by adopting frequency resolution; performing spectral integration on each sub-band to generate an enhanced envelope spectrum, and quantifying the modulation intensity of each sub-band based on a comprehensive evaluation index of an L moment and a Hoyer operator; then adaptively optimizing the sub-band with high modulation intensity based on a golden section method; and selecting a sub-band with high modulation intensity to perform spectrum integration along a frequency axis to obtain a demodulation spectrum of the signal. According to the invention, the demodulation performance of the rotating machine in a complex environment can be significantly improved.

The invention discloses a micro-motion exploration frequency dispersion curve extraction method, device and medium, and relates to the technical field of micro-motion exploration, and the method comprises the following steps: S1, calculating the average cross-correlation spectrum of each station pair according to the recorded data of a plurality of stations in an observation array; s2, aiming at the average cross-correlation spectrum of each station pair, identifying a first intersection point frequency of the average cross-correlation spectrum and a frequency axis; s3, based on the first intersection point frequency, performing piecewise fitting on the average cross-correlation spectrum to extract a phase velocity: for a frequency band not greater than the first intersection point frequency, performing fitting by adopting a zero-order Bessel function J0; and for a frequency band of which the frequency is greater than the frequency of the first intersection point, performing fitting by adopting a first-class zero-order Hankel function H0 (1). In a single method, the contradiction of artifact interference in a high frequency band and insufficient detection depth in a low frequency band in the prior art is overcome at the same time, the accuracy and reliability of frequency dispersion curve extraction under the sparse array condition are improved, and the detection depth of micro-motion exploration is effectively expanded.

The application relates to a kind of beam transverse operating point measurement method and device, method includes: in time sequence to Schottky signal sampling;Current frame power spectrum is obtained based on current frame data;Unified physical frequency axis under current frame target power spectrum is obtained based on current frame power spectrum;Adaptive smoothing filtering is carried out to obtain the smoothed target power spectrum of current frame;The frequency coordinate of the smoothed target power spectrum of current frame is normalized to dimensionless operating point coordinate to obtain the normalized power spectrum of current frame;The normalized power spectrum of current frame is resampled to obtain the resampled power spectrum of current frame;Determine the reference power spectrum of current frame and the reference transverse operating point of current frame;Determine the candidate transverse operating point of current frame;The reference transverse operating point and candidate transverse operating point of each frame in frame sequence are determined by current frame, the preceding several frames of the current frame, and the optimal estimation value of the transverse operating point of the current frame is determined.

The invention discloses a signal modulation method and system, and the method comprises the following steps: defining basic parameters of a signal, including a sampling rate, signal duration and a generation time vector; generating a plurality of signals through the basic parameters of the signals; random frequency offsets are added to the frequencies of the different modulation signals; by setting the length of a window time period, windows are moved to the open end position of a signal and the center position of the windows for windowing processing, and the overlapping proportion between the windows is set; fourier transform is carried out on each segment of sample signal to obtain a time-frequency analysis chart of each segment of sample signal, each segment of time-frequency analysis chart is sorted according to time, and a time-frequency characteristic chart of the underwater acoustic signal on a time-frequency axis is obtained; and extracting image features from the time-frequency feature map. According to the method, the one-dimensional signal is subjected to visualization processing through time-frequency analysis, and the two-dimensional image with obvious features is obtained, so that a subsequent neural network can conveniently extract the image features.

The application discloses a kind of non-stationary wind field high-efficiency simulation method based on dynamic interpolation setting point strategy, including determining simulation point number and its space position;According to the time-varying fluctuation power spectrum model of typhoon, the evolution power spectrum density matrix is calculated;Determine the time range and frequency range of simulation, and it is evenly dispersed into P and Q nodes on time axis and frequency axis respectively;In these discrete nodes, the interpolation node coordinates are selected by dynamic interpolation setting point strategy;By Cholesky decomposition to the evolution power spectrum at interpolation node, the spectral decomposition matrix at the place is obtained, then combined with interpolation method, the spectral decomposition matrix at non-interpolation node is obtained;According to the formula of harmonic superposition method, generate fluctuating wind speed sample.The application is based on classical harmonic superposition method, constructs a kind of dynamic interpolation setting point strategy, can accurately, conveniently select the interpolation node of frequency dimension, effectively reduces the amount of calculation, significantly improves simulation efficiency.

A radar device includes a transmitting antenna unit, a modulator, a receiving antenna unit, a receiver, and a processor. The transmitting antenna unit includes transmitting antennas. The modulator generates transmission signals respectively provided to the transmitting antennas by executing phase-shift keying through diverging the common signal into diverged signals having the number being equal to the number of the transmitting antennas and rotating respective phases of the diverged signals. The receiving antenna unit includes at least one receiving antenna. The receiver generates a received code of at least one received signal. The received code is acquired by encoding an appearance pattern of a peak of at least one received signal on a Doppler frequency axis. The processor generates information related to an object, according to the received code generated by the receiver. The modulator executes the phase-shift keying by using a linear block code as an assigned code.