25 results about "Cyclic spectrum" patented technology

The invention discloses an unmanned aerial vehicle multi-mode fusion radio positioning and signal identification method and system, and relates to the technical field of radio positioning and detection.The method comprises the steps that an airspace radio environment is continuously monitored in a monitoring area through all receiving stations, a power spectrum sequence and an unmanned aerial vehicle communication signal are obtained, and spectrum features are extracted from the power spectrum sequence; for the same launch event, acquiring a three-dimensional motion track of the unmanned aerial vehicle according to the arrival time of each receiving station; echo signals generated by unmanned aerial vehicle rotor wing and vehicle body movement are obtained through a radar, and micro-Doppler features are extracted from the echo signals; acquiring protocol related modulation features, cyclic spectrum features and protocol features according to the unmanned aerial vehicle communication signals; performing multi-modal feature fusion on the spectrum feature, the micro-Doppler feature, the modulation feature, the cyclic spectrum feature and the protocol feature to obtain a deep joint feature; identifying the type of the unmanned aerial vehicle according to the deep joint feature, and obtaining a type label; and constructing an unmanned aerial vehicle feature signature by using the category label, the three-dimensional motion trail and the depth joint feature. According to the invention, the accuracy, robustness and supervision capability of unmanned aerial vehicle positioning and signal detection in a complex environment can be improved.

A radio frequency fingerprint identification method based on an image transmission signal of an unmanned aerial vehicle comprises the following steps: firstly identifying the image transmission signal of the unmanned aerial vehicle, and then respectively carrying out short-time Fourier transform and cyclic spectrum analysis to respectively obtain a time-frequency diagram and a cyclic spectrum diagram; respectively inputting the time-frequency graph and the cyclic spectrum graph into a time-frequency feature extraction stream based on CNN and deformation convolution and a cyclic spectrum feature extraction stream based on Vision Transform, and extracting a local time-frequency feature and a global cyclic stationary feature of the signal; fusing the local time-frequency features and the global cyclostationary features through a multi-modal feature fusion module to obtain fused features; and inputting the fused features into a classification head, and outputting probability distribution of each unmanned aerial vehicle model. And the limitation of a traditional single feature recognition method is effectively overcome. According to the technology, the problem of low recognition rate of the low-altitude unmanned aerial vehicle in a complex electromagnetic environment and under the condition of low signal-to-noise ratio can be solved.

This invention discloses a thin-layer weak signal enhancement method based on cyclic spectrum enhancement technology. The method first preprocesses the original seismic data using a Gaussian smoothing operator to suppress random noise while preserving the main signal features. Then, even-order derivative operations are introduced to enhance the high-frequency components and detailed information of the seismic signal, highlighting the reflection characteristics of the thin-layer interface. To address the issue of high-order derivatives easily introducing high-frequency noise, a Butterworth low-pass filter is used for directional noise reduction. Finally, an adaptive cyclic termination condition is used to determine the optimal derivative order, and the derivative seismic traces are superimposed onto the original seismic traces to achieve thin-layer weak signal enhancement. Numerical simulations and tests with actual seismic data show that this method can effectively enhance the weak signals of 10m (λ / 4) and 5m (λ / 8) thin layers, improve the continuity and clarity of the phase axis of thin-layer reflected waves, and thus enhance the identification capability of thin-layer reservoirs under complex geological conditions.

The application discloses an electrolyte recovery evaluation method and system based on artificial intelligence, relates to the technical field of intelligent evaluation, and comprises the following steps: projecting a Brillouin frequency shift signal to the first two shear modalities, calculating a modality projection coefficient, performing linear mapping on a cyclic spectrum entropy, generating a discrete point number, folding normalized modality energy flow density into a periodic sequence, and generating a flow state evaluation result; according to the flow state evaluation result, determining a flow adjustment amount, converting the flow adjustment amount into a control signal of a variable frequency pump, and adjusting the control signal. The application enhances the perception ability of the instantaneous change of the electrolyte flow state by calculating the global frequency spectrum energy distribution and combining the Hilbert transform and the cyclic spectrum analysis of the phase signal, so that the accurate monitoring and real-time adjustment of the electrolyte flow under complex working conditions are realized. The power spectrum density and the sound-induced energy density of the phase signal are calculated, and the construction of the sound wave phase modulation term is combined, so that the accuracy and adaptability of the flow state analysis are improved.

The present application relates to a kind of cyclic stationary signal detection method and medium of combining multi-harmonic feature decision smoothing, it is related to signal processing technical field.The method includes: filtering based on the time series signal collected, and the cyclic spectrum of each time point is collected based on the preset acquisition frequency;For each cyclic spectrum, calculate weighted global probability score;For each cyclic spectrum, obtain its decision threshold, generate instantaneous decision result based on weighted global probability score and decision threshold;For the instantaneous decision result time series formed by instantaneous decision result, using sliding window smoothing processing, calculate the proportion of instantaneous decision result in sliding window that is true, judge whether to detect cyclic stationary signal.Compared with prior art, isolated false alarm caused by instantaneous interference can be effectively filtered out, so that a better balance between the reliability of feature and the stability of decision can be achieved, and the reliability of target detection is improved.

The application belongs to the field of electronic countermeasures and satellite communication interference technology, and discloses a kind of star chain satellite communication link intelligent interference method and device based on phased array antenna to solve the problems of insufficient interference beams and poor interference effect in traditional technology;Firstly, the application can generate and control multiple interference beams simultaneously based on analog-digital hybrid phased array antenna, realizing the synchronous coverage and interference of multiple "star chain" satellite links in the upper air;Secondly, through high-precision estimation of OFDM signal parameters based on cyclic spectrum, accurate signal feature guidance is provided for interference;Finally, based on the extracted signal feature parameters, an intelligent decision mechanism is introduced, and through interference waveform library construction and efficiency function optimization, dynamic and adaptive selection of interference waveform and parameters is realized, thus changing the inefficient mode of relying on preset waveform;Therefore, the application significantly improves the pertinence, adaptability and overall efficiency of the interference system.

The invention discloses a frequency modulation differential chaos phase shift keying communication method based on cyclic spectrum analysis and a related device. According to the invention, a block-level processing cyclic shift index and cosine frequency index dual modulation mechanism is introduced into a traditional DCSK framework, cyclic shift operation is carried out on a reference segment, cosine sequence multiplicative weighting is carried out on a frequency modulation emission sequence, and a detectable cyclostationary characteristic is introduced through a controllable cosine normalization frequency parameter, so that the accuracy of the frequency modulation transmission sequence is improved. Additional information bits are mapped to the shift index and the frequency index, so that the spectrum efficiency is remarkably improved; meanwhile, by using codebook coding of index bits, effective load bits are increased, the misjudgment probability is reduced, the communication reliability is effectively improved, and the technical problem that spectrum efficiency and general reliability are difficult to consider at the same time in an existing chaotic communication scheme is solved.

The invention discloses an unmanned aerial vehicle image transmission signal modulation mode identification method and device, equipment and a storage medium, and is used for solving the technical problem of performance deterioration and even failure of a conventional identification algorithm in an impulse noise environment. The method comprises the following steps: receiving an unmanned aerial vehicle image transmission signal; enabling the unmanned aerial vehicle image transmission signal to pass through a pulse noise model to generate a wireless receiving signal; inputting the wireless receiving signal into a pre-trained neural network model, and outputting the modulation type of the image transmission signal of the unmanned aerial vehicle; the neural network model is obtained through training after sample signals are converted into constellation diagrams and hyperbolic tangent cyclic spectrum images.

The invention relates to the field of rotary mechanical equipment vibration signal processing and rotor fault diagnosis, and provides a rotor rub-impact identification method and system based on cyclic spectrum eigen orthogonal decomposition, and the method comprises the steps: continuously collecting vibration signals of a continuously operating gas turbine and a combined cycle unit support bearing at equal intervals and equal length, and carrying out the preprocessing; a cyclic spectrum correlation mapping graph of the preprocessed vibration signals of the equal-length supporting bearing is obtained, a two-dimensional cyclic spectrum correlation mapping graph is obtained through amplitude normalization, and discrete estimation is carried out; constructing a snapshot matrix and performing intrinsic orthogonal decomposition; and determining the number of reconstructed modes according to the change condition of the modal energy ratio of each order, reconstructing an intrinsic orthogonal decomposition result, carrying out full-band integration along the spectral frequency direction to obtain a fault mode demodulation spectrum, and analyzing the correlation between the fault mode demodulation spectrum and the two-dimensional cyclic spectrum coherence mapping graph to determine the fault occurrence time. According to the method, the fault mode can be accurately extracted, the fault occurrence time can be identified, and the industrial field requirement of unit vibration monitoring in a continuous monitoring scene is met.

The invention provides a cutting tool residual life online detection method based on multi-source sensing data fusion, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting a sensor signal in a cutting machining process, carrying out the intrinsic mode decomposition based on Hilbert-Huang transformation, and obtaining the natural vibration mode of the sensor signal; analyzing the instantaneous frequency spectrum, extracting a drift component, eliminating the influence of the drift component by adopting a self-compensation mechanism, and obtaining a temperature-compensated sensor signal; second-order cyclic spectrum analysis is carried out, coupling frequency components among the sensors are identified, a decoupling mapping matrix is constructed, and independent wear characteristics of the sensors are separated; and constructing a time sequence phase spectrum matrix, carrying out singular value decomposition on the time sequence phase spectrum matrix to obtain a feature importance degree sequence, carrying out probability density modeling on the sorted features by adopting a kernel density estimation method, and calculating the residual life of the cutting tool. The problem of non-linear coupling among signals of multiple sensors is solved, and accurate prediction of the residual life of the tool under the complex cutting working condition is achieved.

The application discloses a low signal-to-noise ratio direct spread signal detection method based on noise cancellation, and belongs to the field of communication spectrum sensing. The application adopts a minimum mean square error-based adaptive noise cancellation method, can track and eliminate non-stationary noise interference in real time, automatically optimizes filter parameters in an unknown channel environment, and enables a signal detection system to adapt to different background noise conditions. By using a cyclic spectrum analysis method, the cyclic stationary characteristics of the direct spread signal are extracted, and robust signal detection is realized in a low signal-to-noise ratio environment. The existence of the signal is determined through a characteristic spectrum peak on a non-zero cycle frequency, and the carrier frequency and pseudo code rate are further estimated, so that more accurate signal identification and parameter extraction are realized, the influence of noise uncertainty on the detection performance is avoided, and the robustness and reliability of detection are improved. Welch smoothing processing and short-time Fourier transform are combined, the variance of spectrum estimation is reduced when the cyclic spectrum is calculated, and the robustness of detection is improved.

The invention discloses a rolling bearing fault diagnosis method and system based on a product envelope spectrum optimization diagram, and the method comprises the steps: obtaining a vibration signal of a rolling bearing, carrying out the cyclic spectrum coherence analysis, and obtaining a normalized vibration signal cyclic spectrum coherence value; correcting and adjusting the normalized cyclic spectrum coherence value of the vibration signal to obtain a fault signal-to-noise ratio index of a corrected envelope spectrum; and sorting and analyzing the fault signal-to-noise ratio indexes of the corrected envelope spectrum, and determining the fault of the rolling bearing. The method can improve the diagnosis precision and robustness of the rolling bearing fault under the conditions of low signal-to-noise ratio and complex interference. The rolling bearing fault diagnosis method and system based on the product envelope spectrum optimization diagram can be widely applied to the technical field of rolling bearing fault diagnosis.

This invention provides a method for identifying the modulation scheme of mixed satellite signals at the same frequency. It combines signal cyclic spectrum analysis with a neural network to jointly identify the modulation schemes of any two mixed satellite signals. The signal modulation types include bpsk, qpsk, π / 4dqpsk, uqpsk, and 8psk. Maximum probability template matching is then performed to effectively identify any two mixed modulation schemes of commonly used satellite communication signals, including bpsk, qpsk, π / 4dqpsk, uqpsk, and 8psk. The method consists of two phases: a preparation phase and an application phase. The preparation phase generates the neural network dataset and trains the neural network, while also generating the target matrix set. The application phase is used for the modulation identification of the mixed signals.

The invention discloses a sensorless roller chain state monitoring method and device. The method comprises the steps that S1, system state monitoring data are collected from the interior of a motor driver of a driving system; s2, slicing the original torque data of the driver according to a motor position signal in the system state monitoring data to obtain a plurality of torque data segments; s3, cyclic spectrum analysis is carried out on the torque data segment, and frequency domain characteristics representing the periodic operation state of the system are obtained; s4, inputting the frequency domain features into a graph attention network for representation learning and sequence reconstruction to obtain a prediction result; and S5, evaluating the health state of the roller chain according to the difference between the prediction result and the frequency domain feature. Through an innovative sensorless monitoring scheme, the problems that in traditional roller chain state monitoring, sensors are difficult to install, the cost is high, and data are seriously interfered by noise are effectively solved. And by utilizing the existing torque and position signals in the motor driver, the system complexity and the maintenance cost are obviously reduced.

Systems or techniques for cyclic spectrum search of residual ions are provided herein. In various embodiments, the scientific instrument may include a mass spectrometer. In various aspects, the scientific instrument may annotate one or more ion peaks of the lysis spectrum. In various aspects, the scientific instrument may remove one or more annotated ion peaks from the lysis spectrum and resubmit the lysis spectrum for annotation, where one or more of the remaining ion peaks may be annotated.

The invention relates to a cyclostationary signal detection method combining multi-harmonic characteristic decision smoothing and a medium, and relates to the technical field of signal processing. The method comprises the following steps: filtering based on a collected time sequence signal, and collecting a cyclic spectrum of each time point based on a preset collection frequency; calculating a weighted global probability score for each cyclic spectrum; for each cyclic spectrum, obtaining a judgment threshold value of the cyclic spectrum, and generating an instantaneous judgment result based on the weighted global probability score and the judgment threshold value; and for an instantaneous judgment result time sequence formed by the instantaneous judgment result, sliding window smoothing is adopted, the proportion of the instantaneous judgment result being true in a sliding window is calculated, and whether a cyclostationary signal is detected or not is judged. Compared with the prior art, the method has the advantages that isolated false alarms caused by instantaneous interference can be effectively filtered out, so that better balance can be obtained between the reliability of features and the stability of judgment, and the reliability of target detection is improved.

The application provides a non-synchronous measurement method of a cyclostationary sound source, a storage medium and computer equipment. The non-synchronous measurement method of the cyclostationary sound source comprises the following steps: using a microphone array with M channels to measure a to-be-measured sound source at different positions for P times; respectively calculating cross cyclic spectrum matrices of sound signals obtained by the microphone array at different positions; stacking the cross cyclic spectrum matrices according to diagonal lines to obtain a data-missing cross cyclic spectrum matrix with a dimension of MPxMP; constructing a constraint problem based on a completion target of the data-missing cross cyclic spectrum matrix, completing matrix completion of the data-missing cross cyclic spectrum matrix, and forming a completed cross cyclic spectrum matrix. The technical scheme of the application can be widely applied to the technical field of cyclostationary sound source measurement.

The invention discloses a distributed electric vehicle mechanical transmission system planet wheel bearing fault detection method, and belongs to the field of fault diagnosis and signal processing. In order to solve the problems that in distributed electric vehicle mechanical transmission system planet wheel bearing fault detection, the installation position of a sensor is limited, fault impact characteristics are weak and difficult to extract and the like, an encoder signal serves as a signal source, cyclic spectrum coherence is calculated based on an instantaneous angular velocity signal, and the fault detection accuracy is improved. A morphological outlier index is introduced to evaluate the feature significance of a spectral frequency component, and spectral frequencies containing rich fault information are fused through an adaptive multi-spectral proportional fusion method to obtain a fusion spectrum, so that the fault features of the planetary bearing are extracted; the method provided by the invention solves the problem that the fault impact characteristic of the planet wheel bearing is weak and difficult to extract, and can realize fault detection of the planet wheel bearing fault under the variable-speed working condition.

The invention relates to the technical field of cloud cooperative communication, and discloses an electric power inspection real-time data processing system based on edge computing, and the system comprises a data receiving module which is used for receiving electric power inspection data uploaded by an inspection terminal; the signal characteristic analysis module executes cyclic spectrum analysis and Doppler frequency estimation on a millimeter wave signal sequence in the data, and extracts the slope and intercept parameters of parallel oblique line spectral lines on a two-dimensional plane formed by cyclic frequency and Doppler frequency; a transmission strategy determination module calculates the lattice coherence degree according to the extracted tilt cyclic spectrum lattice parameters, and determines an adaptive transmission strategy according to the coherence degree; and the strategy feedback module feeds back the determined transmission strategy to the inspection terminal in real time to guide the terminal to carry out adaptive data transmission in a dynamic channel environment, so that the stability and the real-time performance of power inspection data transmission are remarkably improved.

The invention discloses an electric shock identification method, system and device based on cyclic spectrum characteristic analysis, a medium and a product, and the method comprises the steps: dividing a residual current waveform signal into a pre-electric shock residual current waveform and a post-electric shock residual current waveform through employing an Otsu Otsu algorithm; cyclic spectrum feature extraction is carried out on the residual current waveform before electric shock and the residual current waveform after electric shock based on a cyclic spectrum analysis method, clustering analysis is carried out on the extracted cyclic spectrum features, and cyclic spectrum feature clustering results before and after electric shock are obtained; and constructing a training data set by using the cyclic spectrum feature clustering result before and after the electric shock and the electric shock tag, training the initial machine learning network, and identifying whether the electric shock occurs in the current calculation period according to the residual current waveform signal in the current calculation period through a trained electric shock identification model. Therefore, the accuracy and reliability of electric shock identification are effectively improved.

The invention relates to the field of rotor vibration signal processing and fault diagnosis, and provides a weighted demodulation spectrum generation and fault diagnosis method and system based on distance metrics, and the method comprises the steps: collecting a one-dimensional vibration signal sequence of a supporting bearing of a gas turbine and a combined cycle generator set, and carrying out the pre-whitening of the one-dimensional vibration signal sequence; calculating a two-dimensional time-varying autocorrelation function of the one-dimensional vibration signal sequence, mapping the two-dimensional time-varying autocorrelation function to a two-dimensional cyclic spectrum correlation matrix by adopting two-dimensional Fourier transform, and normalizing to obtain a two-dimensional cyclic spectrum coherence matrix; calculating an average distance between each spectrum component and other spectrum components based on the two-dimensional cyclic spectrum coherence matrix, and normalizing the average distance to obtain a normalized average distance; performing weighted summation on corresponding spectrum components in the two-dimensional cyclic spectrum coherence matrix based on the normalized average distance to obtain a one-dimensional demodulation spectrum; impact components caused by rotor faults are analyzed based on the one-dimensional demodulation spectrum. According to the invention, the fault identification precision can be improved, and industrial field requirements of rotor vibration monitoring of gas turbines and combined cycle units are met.

The application provides a mechanical rotating part acoustic emission fault diagnosis method based on bidirectional weighted cyclic stationarity, which comprises the following steps: collecting an acoustic emission signal to be diagnosed, obtaining a cyclic spectrum coherence function, and obtaining a baseline discrete spectrum coherence mapping and a diagnosis discrete spectrum coherence mapping; based on the baseline discrete spectrum coherence mapping and the diagnosis discrete spectrum coherence mapping, a spectrum frequency weight redistribution vector is obtained; based on the cyclic spectrum coherence function, a difference spectrum coherence matrix is obtained; and based on the spectrum frequency weight redistribution vector and the difference spectrum coherence matrix, a bidirectional reweighted difference spectrum coherence matrix is obtained. The application integrates healthy baseline data as prior information into a cyclic stationarity analysis framework, innovatively combines the difference and weighting ideas, deeply excavates the unique advantages and potential of the acoustic emission technology in the field of fault diagnosis, fully meets the needs of complex and variable industrial application scenarios, and has practical application value.

The invention relates to the technical field of cognitive radio, in particular to a cyclic autocorrelation spectrum sensing method and device based on cognitive radio, and the method comprises the steps: inputting an OFDM (Orthogonal Frequency Division Multiplexing) signal, carrying out windowing processing and convolution solving, averaging the convolution, analyzing the cyclic autocorrelation of the OFDM signal, and obtaining a cyclic spectrum through an FAM (Fast Fourier Transform Accumulation) algorithm. The method comprises the following steps of: constructing a cyclic spectrum, normalizing the cyclic spectrum to form a cyclic autocorrelation gray level image, extracting depth features layer by layer through an improved AlexNet model, training a CNN (Convolutional Neural Network) by using a back propagation (BP) algorithm to perform a spectrum sensing training process and a spectrum sensing test process, and finally inputting a test set to verify the trained CNN model. According to the method, an AlexNet model is adopted to train a CNN, and the model uses more convolutional layers and a larger parameter space to fit a large-scale data set ImageNet. According to the FAM algorithm, the advantages of a cyclic spectrum are utilized, noise and a master user are distinguished, existence of the master user is detected, and better OFDM signal spectrum sensing performance is obtained under the condition of a low signal-to-noise ratio.

The invention relates to a seismic wave signal-combined low-altitude flight target detection method, device and equipment. The method comprises the following steps: calculating a cyclic spectrum correlation function of a sound-induced seismic wave signal of a low-altitude rotor type flight target received by a seismic wave sensor, and carrying out integration on a spectrum frequency in the function to obtain a cyclic frequency detection function; based on an undetermined cyclic frequency in the cyclic frequency detection function, solving the frequency with the maximum energy of each frequency point after down-sampling under the condition of meeting a preset bandwidth through a harmonic-like product spectrum method, determining a target fundamental frequency, and calculating each harmonic frequency of the low-altitude rotor type flight target, and after accumulating the energy of the target fundamental frequency and each harmonic frequency, comparing the energy with a preset target detection threshold to obtain a target detection result. By adopting the method, the fundamental frequency and harmonic characteristics of the low-altitude rotor type flight target can be accurately extracted under the interference of a complex environment, and the accuracy and stability of target detection in a low-signal-to-noise-ratio acoustic seismic wave signal are greatly improved.

The present application relates to a modulation type recognition method and device based on cyclic spectrum slice features, and belongs to the technical field of signal modulation recognition. The specific process of the method is as follows: step 1: extract the cyclic spectrum density of the to-be-recognized modulation signal and the cyclic spectrum density of the modulation signal after center squaring, obtain different sections and projection planes of the two kinds of cyclic spectrum densities as signal cyclic spectrum slices; step 2: extract the feature parameters of the signal cyclic spectrum slices, process the feature parameters, and obtain a cyclic spectrum slice feature data set; step 3: input the obtained cyclic spectrum slice feature data set into a pre-trained classification model, and the classification model outputs the modulation type of the to-be-recognized modulation signal.