17 results about "Gaussian signal" patented technology

The invention discloses a ship equipment vibration test method and system based on frequency domain kurtosis mapping and non-Gaussian signal generation. The ship equipment vibration test method comprises the following steps: collecting an actual ship impact vibration signal and extracting a target power spectral density curve; the method comprises the following steps: identifying an impact energy concentration frequency band based on spectrum energy distribution, carrying out frequency band division by adopting an elliptical filter bank with a second-order section structure, and calculating an envelope kurtosis value of each frequency band to establish a kurtosis-frequency mapping model; after the initial Gaussian driving signal is generated, a non-Gaussian driving signal is generated through a phase randomization and amplitude dynamic scaling algorithm based on a mapping model; a multi-target optimization controller based on Pareto leading edge search is adopted, a power spectral density error and a kurtosis error are taken as optimization targets, synchronous optimization is carried out through fast non-dominated sorting and an adaptive hybrid variation strategy, and a driving signal is output. According to the invention, the broadband and non-Gaussian impact vibration environment of the ship equipment in the actual severe sea condition can be reproduced in a high-fidelity manner, and the authenticity of the test and the fault mode coverage capability are remarkably improved.

The invention discloses a differential processing algorithm for laser pulse ranging echoes, which is characterized in that the echoes of laser pulse signals are differentiated through a mathematical method, so that an extreme point, namely the peak moment of the echoes, is solved. According to the differential processing algorithm of the laser pulse ranging echo, the extreme point is obtained by differentiating the Gaussian signal of the time t. The change of the intensity of the echo does not cause the change of the extreme point moment, i.e., the t0 moment is not changed, so that the method fundamentally eliminates the moment discrimination error delta t of the leading edge discrimination method, naturally does not need to be optimized to obtain the dual-threshold leading edge discrimination method, and also avoids the problems of other methods in use. When the echo size changes, the peak moment can still be accurately identified, the drift error is greatly eliminated, in the pulse laser ranging echo moment identification method, the accuracy is far higher than that of other methods, linear fitting and other secondary error compensation are not needed, the engineering complexity is reduced, the workload is reduced, and the adaptability is improved.

The invention discloses a lithium battery system state-of-charge estimation method based on a Hammerstein model, and the method comprises the steps: constructing a second-order RC circuit equation of a lithium battery, describing a dynamic linear module of the Hammerstein model through a noise transfer function model, and constructing a lithium battery system through a static nonlinear module of an adaptive neural fuzzy network; designing a Gaussian signal, inputting the Gaussian signal into the agent model of the lithium battery system to obtain corresponding Gaussian signal output, and decoupling the static nonlinear module and the dynamic linear block by using the covariance function characteristic of the Gaussian signal; identifying parameters of the noise transfer function model by using a least square method based on a covariance function, solving parameters of the adaptive neural fuzzy network by using a crown porcupine optimization algorithm, and updating the weight of the adaptive neural fuzzy network by using a stochastic gradient algorithm with a forgetting factor; and constructing an OCV-SOC curve of the open-circuit voltage and the state of charge by adopting polynomial fitting, and taking output obtained by the Hammerstein model as input of the polynomial fitting to obtain an estimated value of the state of charge SOC.

The invention discloses a power prediction method for a wind power generation system based on a stacked sparse auto-encoder network Hammerstein model, and aims to solve the problems that only nonlinear mapping is modeled and dynamic characteristics of the system are neglected and parameter coupling of the Hammerstein model leads to complex identification in an existing method, the Hammerstein model is constructed, an ARMAX model is utilized to describe a dynamic linear module, and the dynamic linear module is used to predict the power of the wind power generation system based on the stacked sparse auto-encoder network Hammerstein model. A static nonlinear module is described by stacking the sparse auto-encoder network; designing a zero-mean Gaussian signal input proxy model, realizing series module decoupling based on covariance function characteristics, and eliminating parameter coupling; a self-adaptive multi-strategy grey wolf optimization algorithm is adopted to determine the number of neurons in a network hidden layer, training is performed in combination with a sparse criterion function, and the feature extraction capability is improved. According to the method, synchronous capture of static nonlinear and dynamic linear characteristics is realized, the calculation complexity is reduced, the model identification precision, prediction precision and robustness are improved, and the method is suitable for accurate prediction of the power of a wind power system.

The invention discloses a reconstruction generation method and system of a super Gaussian signal for a vehicle structure vibration test. The method comprises the following steps: S1, a signal acquisition module acquires power spectral density data of vehicle road excitation; s2, the data processing terminal generates a Gaussian vibration signal G (t) according to the received power spectrum density data; s3, according to the target kurtosis Ky required by the vibration test of the vehicle structure, solving through a preset nonlinear equation to obtain an index parameter p, and S4, generating a low-frequency Gaussian vibration signal g (t) by the data processing terminal, and calculating and generating an amplitude modulation signal u (t) according to the solved p value. S5, generating a super Gaussian signal Y (t); and S6, taking the super Gaussian signal Y (t) as an excitation signal required by the vibration test of the vehicle structure. According to the invention, efficient online reconstruction of the super Gaussian signal is realized, and the structure vibration test requirements in the high-reliability field of automobile engineering and the like are met.

The application relates to a non-stationary non-Gaussian random signal generation method based on amplitude and phase joint modulation. A Gaussian signal is obtained by performing inverse Fourier transform on the power spectrum of a non-stationary non-Gaussian vibration signal, a modulation signal is generated based on a frequency range, a non-stationary non-Gaussian signal is obtained according to the Gaussian signal and the modulation signal, corresponding frequency domain amplitude spectrum and frequency domain phase spectrum are obtained according to the Gaussian signal and the non-stationary non-Gaussian signal respectively, the non-stationary non-Gaussian signal is reconstructed according to the frequency domain amplitude spectrum and the frequency domain phase spectrum through inverse Fourier transform, and whether the kurtosis of the reconstructed non-stationary non-Gaussian signal meets a preset target kurtosis is judged. If not, the phase of the frequency domain phase spectrum is modulated until the kurtosis of the reconstructed non-stationary non-Gaussian signal meets the preset target kurtosis, and the reconstructed non-stationary non-Gaussian signal is the final output signal. The method can accurately generate a non-stationary non-Gaussian signal with a specified power spectral density.

The present application belongs to the field of rolling bearing reliability assessment, and particularly relates to a method for predicting the remaining life of rolling bearings based on deep learning and particle filtering. The present application realizes the adaptive construction of rolling bearing degradation indicators, realizes the adaptive construction of degradation indicators by building an efficient deep learning model, builds the model only based on normal data, and predicts the evolution of abnormal states, which is of great significance for the state assessment and remaining life prediction of rolling bearings under the condition of missing fault samples in actual engineering; realizes the adaptive division of the evolution stage of rolling bearing faults, and adaptively divides the evolution stage by unsupervised cluster learning, which is more scientific and accurate than the traditional artificial fuzzy definition method; realizes the prediction of the remaining life of rolling bearings based on particle filtering, and based on the extracted bearing degradation characteristics, uses a particle filtering method that is more suitable for nonlinear and non-Gaussian signal prediction problems to achieve accurate prediction of the remaining life of rolling bearings.

This invention discloses a high-degree-of-freedom, low-coupling sparse array design method based on fourth-order cumulants, comprising: A: constructing a non-Gaussian signal receiving model and constructing a fourth-order differential array of the non-Gaussian signal using fourth-order cumulants; B: constructing a coprime displacement array composed of two coprime displacement subarrays and determining the array parameters; C: constructing an expanded nested array composed of two nested subarrays and determining the array parameters; D: constructing an expanded nested coprime displacement array based on the coprime displacement array and the expanded nested array and determining the expansion factor, ultimately obtaining a high-degree-of-freedom, low-coupling sparse array based on fourth-order cumulants. This invention can achieve stronger anti-coupling capability while maintaining a degree of freedom comparable to existing sparse arrays, and can significantly improve the signal estimation accuracy and direction-of-arrival resolution.

The invention discloses a remote underwater acoustic communication non-sparse channel estimation method for a multi-carrier system, and relates to the technical field of remote underwater acoustic communication. For a non-sparse structure of a remote underwater acoustic communication channel, a maximum likelihood estimation method is provided to estimate dense multipath of the non-sparse channel by approximating a received signal of a multi-carrier system as a Gaussian signal. For the problem of channel indistinguishability caused by dense multipath, a received signal is modeled into Gaussian distribution based on a central limit theorem, and a novel channel estimation algorithm Novel ML is deduced. In a multi-carrier orthogonal frequency division multiplexing (OFDM) system, a received signal obeys normal distribution after modeling, and estimation of the impulse response of a dense multipath channel is realized by constructing a likelihood function and optimizing and solving the likelihood function. Results of numerical simulation and remote offshore experiments show that compared with a traditional channel estimation algorithm, the method provided by the invention can effectively perform accurate estimation of a non-sparse channel.

The application relates to the field of communication and signal processing, in particular to an OFDM signal demodulation method for a wireless optical communication system; the method is completed under the cooperation of a transmitting end and a receiving end; the transmitting end models the probability distribution of a scaled signal by using a Gaussian mixture model; based on the model, the nonlinear distortion is expressed as the weighted sum of the distortions of each Gaussian component, and the corresponding clipping coefficients are calculated for each Gaussian component; finally, the scaled signal and the clipping coefficients are sent to the receiving end; the receiving end uses the received clipping coefficients to perform inverse weighting processing on the received signal to compensate for the nonlinear distortion, and then performs equalization, deprecoding and demodulation to recover the original bit stream; by accurately modeling the non-Gaussian signal characteristics and precomputing the clipping coefficients, the application can effectively suppress the deep clipping distortion of the transmitting signal and significantly reduce the system error rate.

The application discloses a non-Gaussian weather radar signal adaptive spectral moment estimation method based on a clustering algorithm. Firstly, a Gaussian mixture model is used to model a non-Gaussian power spectrum, and an elbow rule is used to calculate the number K of Gaussian power spectrums contained in the non-Gaussian signal. Then, a K-means clustering algorithm is used to cluster sampling points of the power spectrum of the non-Gaussian signal, and the mean and variance of the clustering are used as initial values of spectral moments. Finally, an expectation maximization algorithm is used to further estimate the spectral moments. The application can effectively estimate the spectral moment parameters when the power spectrum of a weather signal is in a non-Gaussian distribution and traditional spectral moment estimation methods fail.

This invention discloses a method, apparatus, equipment, and medium for fatigue evaluation of transport packaging based on non-Gaussian vibration signal decomposition. The steps are as follows: An acceleration sensor is installed at the center of the two rear wheels on the cargo box floor of a vehicle; the original non-Gaussian vibration signal of the vehicle's acceleration is measured when it passes over a test road; the signal is decomposed into multiple non-Gaussian modal components; based on the non-Gaussian probability description and equivalent optimization of higher-order moments, the power spectrum is then equivalently converted into approximate moments of the power spectrum of multiple Gaussian signals; the response output moment under the frequency response function transfer of the transport packaging system is calculated; the equivalent fatigue damage of each Gaussian signal segment is calculated and summed to obtain the total equivalent fatigue damage of the non-Gaussian vibration signal in the transport packaging system. This method improves the decomposition efficiency and accuracy of non-Gaussian vibration signals and can be used for fatigue analysis of product transport packaging under non-Gaussian random vibration.

The invention relates to the field of communication and signal processing, in particular to an OFDM (Orthogonal Frequency Division Multiplexing) signal demodulation method for a wireless optical communication system. The method is completed under cooperation of a transmitting end and a receiving end, and the transmitting end adopts a Gaussian mixture model to model probability distribution of scaled signals; on the basis of the model, representing nonlinear distortion as weighted sum of distortion of each Gaussian component, and calculating a corresponding clipping coefficient for each Gaussian component; and finally, the scaled signal and the clipping coefficient are sent to a receiving end together. And a receiving end performs inverse weighting processing on the received signal by using the received clipping coefficient to compensate nonlinear distortion, and recovers an original bit stream through equalization, pre-coding removal and demodulation. According to the method, the non-Gaussian signal characteristics are accurately modeled, and the clipping coefficient is pre-calculated, so that the deep clipping distortion of the transmitted signal can be effectively inhibited, and the error rate of the system is remarkably reduced.

The application provides a defect positioning method and device of a power cable, a terminal and a storage medium. The method comprises the following steps: measuring the head-end impedance spectrum of the to-be-measured cable, and calculating the head-end reflection coefficient of the to-be-measured cable according to the head-end impedance spectrum; taking a Gaussian signal as an incident signal for measuring the to-be-measured cable, and calculating the target frequency domain signal of the reflection signal according to the incident signal and the head-end reflection coefficient; performing inverse fast Fourier transform on the target frequency domain signal to obtain the target time domain signal of the reflection signal; and calculating the reflection peak barycenter position of the target time domain signal according to the target time domain signal, wherein the reflection peak barycenter position is the defect position of the to-be-measured cable. The application can improve the accuracy of local defect identification and more accurately obtain the positioning of the cable defect.

The embodiment of the present application discloses a non-circular signal DOA estimation method based on a fourth-order sum-difference joint cooperative array, characterized by comprising: S1, a far-field detection scene module locates a far-field tag target to be tracked, comprising Q far-field narrow-band non-circular non-Gaussian signal sources, wherein the azimuth angles of the Q far-field narrow-band non-circular non-Gaussian signal sources are {θ1,...,θ Q S2. The linear radar array system receives the far-field narrowband non-circular, non-Gaussian signal source to obtain a received signal; S3. An augmented fourth-order cumulant matrix is ​​constructed using the received signal; S4. Vectorization technology is applied to the augmented cumulant matrix to obtain a fourth-order sum-difference joint cooperative array; S5. DOA estimation is performed using non-circular phase separation and cooperative array spectrum estimation technology. This application provides a strategy for constructing a sum-difference joint cooperative array using cumulant technology, constructing a novel fourth-order sum-difference joint cooperative array with higher degrees of freedom, facilitating modular processing, and being suitable for GPU parallel computing, thereby improving efficiency.

The present application relates to a kind of modulation methods for multi-reflection chamber noise suppression, belong to gas detection technical field.The method includes: selecting DFB laser with specific wavelength and current modulation rate;Sawtooth wave and sine wave are superimposed to generate original modulation signal;According to the comparison relationship between sawtooth wave scanning spectral range and gas spectral absorption peak, determine the amplitude of Gaussian signal, generate Gaussian signal;The generated Gaussian signal is combined with original modulation signal to obtain variable step modulation signal;Variable step modulation signal is input as driving current into laser, and laser is driven to emit laser signal;Laser signal obtains gas spectrum signal after passing through gas.The present application superimposes Gaussian linear signal similar to gas absorption peak on sawtooth wave signal, current step increases in non-absorption section, and scanning time ratio reduces, current step reduces in absorption section, and scanning time ratio improves, in the case where current scanning range is not changed, effectively improve absorption signal intensity.

The utility model discloses a radiation monitoring system, a host and equipment. The radiation monitoring system comprises a crystal detection module which is used for generating a corresponding charge pulse signal when receiving a radiation signal; the signal conversion module is electrically connected with the crystal detection module, and the signal conversion module is used for converting the charge pulse signal into a Gaussian-like signal; the comparison module is electrically connected with the signal conversion module, and the comparison module is used for classifying and digitizing the Gaussian-like signals and outputting corresponding digital pulse signals; and the acquisition processing module is electrically connected with the comparison module, and the acquisition processing module is used for receiving the digital pulse signal and converting the digital pulse signal into a corresponding radiation dose value. According to the embodiment of the invention, the radiation dose of the wearer can be monitored under the condition that the size of the monitoring system is reduced.