104 results about "Impulse noise" patented technology

The invention relates to a time-varying power high-precision calculation technology of an electric drive system under strong electromagnetic noise interference, and mainly solves the problem of inaccurate power calculation of the electric drive system under strong electromagnetic environment noise and frequency time-varying fluctuation scenes. According to the core technology, a dominant frequency fluctuation curve of a signal is obtained through dynamic tracking of a dominant frequency, and a dynamic band-pass filter changing along with the dominant frequency of the electric drive system is constructed; suppressing pulse noise by combining amplitude self-adaptive filtering of a nonlinear weighting function; a phase demodulation technology fusing zero crossing point correction and Hilbert transform is adopted to eliminate the phase interference problem caused by complex waveforms and weak noise signals; and finally, establishing a time-varying power physical model based on the sliding window. According to the technology, the power calculation accuracy of the electric driving system under the conditions of strong field environmental noise and continuous main frequency conversion is remarkably improved.

The invention discloses a target parameter joint estimation method based on an MCCC criterion in an impulse noise environment, and belongs to the technical field of radars. According to the method, the target function based on the TALS criterion in the PARAFAC algorithm is corrected by adopting the maximum multiple correlation entropy MCCC criterion to enable the target function to be suitable for the impulse noise environment, the PARAFAC algorithm based on the MCCC criterion is derived, the algorithm is applied to bistatic MIMO radar target parameter estimation, dependence of the FLOS method on feature index priori knowledge is broken through, and joint estimation of target parameters is achieved.

The invention relates to the technical field of adaptive filtering, discloses a variable-step robust adaptive filter and a filter network, and aims to solve the technical problem that a traditional adaptive filter cannot effectively consider convergence speed, steady-state precision and impulse noise interference resistance. A non-linear error factor is introduced, large errors caused by impulse noise are effectively suppressed through a generalized function form of the non-linear error factor, and the robustness of the filtering process is remarkably improved; meanwhile, a variable step length mechanism is provided, the theoretically optimal candidate step length is calculated on line by minimizing the mean square deviation of the next moment, and a self-adaptive target variable step length is generated through truncation and time smoothing processing, so that dynamic balance is realized between rapid convergence and low-steady-state maladjustment. According to the invention, the filter is expanded to the distributed network, each node follows a diffusion strategy of first updating and then combining, and global information sharing and performance collaborative optimization are realized by using a combined coefficient.

The invention provides a loudspeaker mask defect identification method and system based on visual inspection, and relates to the technical field of machine visual inspection, and the method comprises the steps: collecting a surface image of a loudspeaker mask through an industrial camera, calculating a median gray value in a pixel neighborhood to effectively filter out impulse noise, carrying out size standardization processing, and obtaining an image of the surface of the loudspeaker mask; a standardized image is obtained; according to the standardized image, a convolutional neural network is adopted, and a parameter learning rate is dynamically adjusted through an adaptive moment estimation mechanism, so that multi-scale feature mapping of mask textures is extracted; according to the multi-scale feature map, candidate defect regions are generated through a region suggestion network; and for a candidate defect area proposal, using a U-Net network and a learning rate to quickly approach an optimal solution, attenuating the learning rate according to an exponential law along with the increase of the number of iterations to stably converge to fine local optimum, and performing pixel-level fine segmentation on a defect boundary to obtain a defect mask image. According to the invention, the detection efficiency of the loudspeaker mask is improved.

The invention discloses a rolling bearing intelligent fault diagnosis method based on data quality leading. The method comprises the following steps: (1) collecting a vibration signal of a rolling bearing; (2) carrying out preprocessing and data enhancement on the collected signals, specifically, (2.1) optimizing a quantitative mapping relation between a sampling length and a fault period number based on a bearing fault characteristic frequency and a periodic impact theory, optimizing a signal length to be 2048 sampling points, and setting a sliding window overlapping rate to be 30%; (2.2) constructing a five-dimensional aggressive data enhancement strategy covering Gaussian noise injection, amplitude scaling, time translation, random flipping and impulse noise disturbance; (3) carrying out fault diagnosis on the rolling bearing; and (4) according to an experiment result, selecting an optimal data quality optimization strategy and a model architecture to carry out rolling bearing fault diagnosis, and outputting a fault diagnosis result. By optimizing the data quality, the accuracy and generalization ability of rolling bearing fault diagnosis are improved, and the problem caused by insufficient attention to the data quality in the prior art is solved.

The invention discloses a GIS partial discharge fault positioning method based on multi-feature fusion and kernel density estimation optimization, and the method comprises the following steps: collecting a partial discharge original pulse signal, carrying out the signal processing, and outputting a first processing signal; performing adaptive robust baseline determination on the first processing signal, detecting a wave head position based on multi-feature fusion and a dynamic threshold voting mechanism, and outputting a wave head arrival time; carrying out time synchronization matching on the wave head arrival time, and calculating and outputting a preliminary positioning result based on a time difference of arrival method; and accumulating a plurality of preliminary positioning results, performing optimization by adopting a kernel density estimation method, and outputting a final partial discharge fault position. According to the method, impulse noise and white noise are effectively suppressed, stable signals are provided for subsequent processing, the detection precision of the wave head arrival time is remarkably improved, the problem that false detection and missing detection are caused by the fact that a traditional fixed threshold value or a single feature is prone to noise interference is solved, the random error of single-time positioning is greatly compressed to be within 0.07 m from 0.33 m on average, and the overall positioning precision is improved by 80%.

The invention belongs to the technical field of radar signals, and particularly relates to an LPI radar signal parameter estimation method under non-Gaussian noise, which is based on a non-Gaussian noise hypothesis and is more in line with actual scene application requirements. For impulse noise interference obeying alpha stable distribution, a deep learning technology is adopted to carry out de-noising processing on a received signal, non-Gaussian noise is effectively suppressed by learning a mapping relation between a time-frequency spectrum and a residual spectrum of a noisy signal, an enhanced time-domain signal is reconstructed, the signal-to-noise ratio is improved, and high-precision parameter estimation is carried out.

The invention provides a distributed anti-interference collaborative optimization method under a mine wireless network based on multi-agent reinforcement learning, and provides a distributed routing and interference suppression collaborative optimization solution for solving the problem that the communication reliability of a wireless sensor network is reduced due to transient electromagnetic interference generated by large-scale electrical equipment under a mine. According to the method, typical mine electromagnetic interference characteristics such as impulse noise and sweep frequency interference are integrated through multi-mode interference, a multi-agent reinforcement learning (MARL) architecture is designed, collaborative optimization of distributed routing and interference suppression is achieved, and interference source positioning, dynamic channel hopping and intelligent rerouting strategies based on spectrum sensing are included. The anti-interference routing protocol is intelligently and dynamically optimized, an interference area is effectively avoided while the shortest path is guaranteed, the anti-interference capability and the communication stability of the mine wireless sensor network are remarkably improved, and an interpretable and extensible intelligent communication solution is provided for a complex industrial environment.

The invention relates to the technical field of image processing, in particular to a hybrid noise removal method and system based on a wavelet framework, and the method comprises the steps: obtaining an original image, preprocessing the original image, converting the original image into a matrix, and decomposing a denoising problem into a plurality of sub-problems based on a denoising model; wherein the denoising model restrains the edge and texture structure of an image through a regular term, captures an abnormal point through a data fidelity term of impulse noise, and suppresses long-tail noise through a data fidelity term of Cauchy noise; each sub-problem is solved, the solving result of each sub-problem is applied to the next sub-problem for iteration, and when the error between the iteration results of the kth step and the (k + 1) th step is smaller than a set value, the iteration result of the last step is the restored image after noise is removed. The image is recovered by taking a summing item of the Cauchy noise and the impulse noise as a data fidelity item of the model and taking a wavelet frame as a regular item.

The invention discloses an earphone defect detection method and device based on voiceprint feature analysis and a storage medium, and relates to the technical field of electroacoustic quality control. According to the method, human ear hearing characteristics are simulated, Mel-cepstrum voiceprint characteristics are extracted from sound response signals, an adaptive weight mechanism based on an equal-loudness curve is introduced, and a voiceprint abnormal deviation index model conforming to human ear subjective perception is constructed; in order to solve the problem that tiny abnormal noise is difficult to recognize, high-order statistics such as kurtosis and skewness are adopted for feature fusion enhancement, and a support vector machine is combined to achieve accurate capture of transient impulse noise. According to the invention, various defect types such as voice coil friction, imbalance of balance degree, diaphragm damage and the like can be fully automatically distinguished, the problems that a traditional test means is disjointed with subjective hearing sense and the omission ratio of hidden defects is high are effectively solved, and the quality inspection efficiency and accuracy of earphone production lines are remarkably improved.

This invention discloses an underwater acoustic anti-interference transmission method and system based on sparse time-frequency feature mapping. The method includes: at the transmitting end, adaptively generating a fractional-order linear frequency-modulated waveform with a specific time-frequency shear slope based on the Doppler state of the underwater acoustic channel, achieving physical-layer focusing of transmitted energy; at the receiving end, constructing a hybrid observation model containing wide-block sparse channel components and narrow-block sparse burst noise components, and using a dual-channel variational Bayesian algorithm to jointly iteratively infer the posterior probability distribution of environmental burst noise in the fractional-order domain; finally, recovering the original signal through soft-threshold interference cancellation and fractional-order channel equalization. This invention effectively solves the communication failure problem caused by high-dynamic Doppler diffusion and marine biological impulse noise interference in underwater acoustics by actively mapping the waveform and using heterogeneous sparse joint inference at the receiving end, significantly improving transmission reliability in harsh underwater acoustic environments.

The present invention discloses a method for direction finding of incoherently distributed sources under strong impulse noise. The method comprises the following steps: first, obtaining snapshot sampling data, performing de-impact preprocessing, and constructing a generalized Capon space spectrum function; second, initializing the quantum position of each arrow and setting parameters to construct a fitness function; third, dividing the arrow group into sub-arrow groups, and performing fitness function evaluation on all arrows. The quantum position of the arrow with the best fitness in each sub-arrow group is selected as the optimal quantum position; fourth, updating the quantum position of each arrow according to the shooting mechanism; fifth, mapping the updated quantum position of each arrow to a position, calculating the updated fitness function value, and updating the optimal quantum position of each sub-arrow group; and sixth, returning to step three, looping and iterating until the maximum number of iterations is reached, outputting the optimal quantum position set, and transforming it into the central azimuth and angular spread estimates of the incoherently distributed source through mapping. The method is robust and high-performance under strong impulse noise conditions.

The utility model discloses a strong pulse noise hearing protection circuit which comprises a signal input end and a signal output end. The pulse amplitude limiting module circuit is connected with the signal input end and the signal output end and is used for carrying out pulse amplitude limiting processing on the input signal; the azimuth sensing circuit is connected with the signal input end and is used for sensing azimuth information of the input signal; the delay control module circuit is connected with the signal input end, the pulse amplitude limiting module circuit and the direction sensing circuit; according to the utility model, through cooperative work of the pulse amplitude limiting module circuit, the orientation sensing circuit and the time delay control module circuit, comprehensive processing of input signals is realized, and the pulse amplitude limiting module circuit ensures that output signals do not exceed a preset maximum amplitude; the azimuth sensing circuit extracts and transmits azimuth information of the input signal; and the delay control module circuit performs delay control on the signal according to the azimuth information and the preset condition, so that the circuit can output the signal meeting the specific requirement, and the requirements of various application scenes are met.

The present invention discloses an adaptive reconstruction method for a band-limited graph signal with unknown signal support. The method comprises: constructing a corresponding observation graph signal model based on an observation signal affected by impulse noise; establishing an optimization problem model based on the observation graph signal model using the l2 norm and the l0 norm to describe the recovery problem; solving the optimization problem model using an alternating optimization method to obtain a reconstructed signal; and performing an inverse graph Fourier transform on the reconstructed signal to obtain a reconstructed graph signal. The present invention reconstructs observation signals affected by impulse noise by exploiting the sparsity of the graph signal in its frequency domain. This method can effectively process graph signals affected by impulse noise, and even when the signal support is unknown, the reconstructed signal still has very high accuracy.

The present invention discloses a method for detecting underwater acoustic reception signals that combines channel estimation, data decoding, and impulse noise estimation. The method first performs impulse noise initialization, then estimates the channel of the 0th sub-block based on training symbols. It then iterates between the combined channel, symbol, and impulse noise estimation modules and the decoding module, ultimately achieving combined channel estimation, data decoding, and impulse noise estimation. This method achieves smaller channel estimation errors and lower coding bit error rates under impulse noise.

The invention discloses an ADC50Hz or 60Hz noise filtering method based on a software algorithm, and the method comprises the steps: carrying out the initialization configuration, and collecting original data; preprocessing and acquiring detection data; an improved Goertzel algorithm is used to acquire frequency spectrum parameters; calculating a three-level extraction factor, and sampling the data output by the ADC to obtain sample data; after forward and reverse compensation is carried out on the sample data, pulse noise suppression of a two-dimensional window is carried out on pulse noise; calculating a transmission quality index, and adjusting the size of a filtering window according to the transmission quality index; and carrying out amplitude calibration on the filtering result by using an improved comb algorithm, and outputting a standard signal after noise reduction processing. On one hand, the noise reduction processing scheme does not need to additionally add a filter circuit, so that the circuit size and the hardware cost are reduced; and on the other hand, the parameters in the method can be adaptively adjusted according to the frequency spectrum parameters, so that the adaptability is improved.

The application relates to the technical field of wireless communication, and discloses a communication method and device for impulse noise suppression based on abnormal point detection, wherein at a receiving end, the method receives a transmission signal, divides the transmission signal into four parts according to a quartile algorithm, determines upper quartiles, a median and lower quartiles, then determines a normal value interval according to the upper quartiles, the median and the lower quartiles, detects abnormal signals disturbed by impulse noise in the transmission signal based on the normal value interval, repairs the abnormal signals, and obtains a de-noised signal.In the embodiment of the application, the transmission signal features and the quartile algorithm are comprehensively utilized to more accurately locate the impulse noise disturbance and improve the suppression effect.In addition, through the quartile-based algorithm, the calculation amount is small, the abnormal signals can be quickly and accurately detected and repaired, the operation is simple, the impulse noise disturbance can be effectively suppressed, the error rate of the system is reduced, and the reliability of data transmission is improved.

This invention relates to the field of smart meter communication data acquisition technology, and discloses a method for anti-interference data acquisition in smart meter communication based on multi-carrier modulation technology. The method includes using multi-carrier modulation to perform frequency division processing on the signal during data transmission, combining channel state perception and dynamic subcarrier allocation strategies to avoid interference frequency bands in real time, and enhancing data robustness through time-frequency joint coding. This application can effectively suppress narrowband interference and impulse noise, improving the communication reliability and data acquisition accuracy of smart meters in complex electromagnetic environments.

The invention discloses a sparse Bayesian out-of-grid orientation estimation method under non-Gaussian noise, which comprises the following steps of: constructing a hierarchical noise prior model based on Student-t distribution, a double-layer sparse signal prior model based on a binary indication variable and the like, and inferring approximate posteriori distribution of iteratively updating parameters by using Bayesian variation; the noise precision weight is adaptively adjusted according to the reconstructed residual energy so as to suppress impulse noise, and random pseudo peaks are effectively removed through a binary indication variable judgment mechanism. And finally, adaptively correcting the grid offset by using the residual gradient information subjected to anti-noise weighting so as to eliminate the off-grid effect. Compared with the traditional method, the method provided by the invention not only can carry out DOA estimation under non-Gaussian noise, but also has higher DOA estimation precision. The method disclosed by the invention still has relatively high performance under the conditions of few snapshots and low signal-to-noise ratio, so that the method has relatively high application value in actual engineering.

The present application relates to the technical field of data preprocessing, and provides a power system frequency measurement data preprocessing method and system, which comprises: obtaining measurement data and dividing into several intervals; for each interval, detecting pulse noise points, determining the size of a first window according to the ratio of the number of pulse noise points to the total number of all data points in the interval, and performing median filtering on the detected pulse noise points in the interval by using the first window according to the size of the first window; selecting a second window, and suppressing Gaussian noise by weighted mean filtering according to the similarity between any data point in the second window and the center point of the window. The three factors of noise reduction effect, feature protection and calculation speed are considered.

The invention provides an industrial energy consumption control method and system. The method comprises the following steps: acquiring energy consumption data through data acquisition equipment; sequentially performing high-frequency noise removal and pulse noise removal on the energy consumption data to obtain denoised energy consumption data; extracting time domain features, frequency domain features and industrial mechanism features from the denoised energy consumption data to form feature data; inputting the feature data into a lightweight model deployed by an edge gateway, and outputting future energy consumption prediction data; when the prediction deviation of the future energy consumption prediction data exceeds an energy consumption threshold value, an equipment regulation and control strategy is automatically generated, the edge executor is controlled to execute the equipment regulation and control strategy, and equipment parameters are dynamically adjusted, so that data noise removal, model training and deployment optimization and real-time performance and automation of equipment regulation and control can be realized.

Systems (100) and methods (200) for impulsive noise detection and mitigation are described. In particular, the system (100) includes a transmitter (102), a communication channel (106), and a receiver (104). The transmitter (102) is configured to encode an input signal, modulate the encoded signal for transmission through the communication channel (106), and transform the modulated signal into a time-domain signal. The communication channel (106) is configured to transmit the time-domain signal and a control signal from the transmitter (102) to a receiver (104). The receiver (104) is configured to transform the time-domain signal into a frequency-domain signal, detect a position of impulsive noise in the frequency-domain signal based at least on identifying a position of null subcarriers in the frequency-domain signal via the control signal, and initiate the suppression of the impulsive noise from the frequency-domain signal.

The application relates to the technical field of wireless communication, and discloses a wireless communication signal transmission system and method based on adaptive modulation, which comprises a likelihood ratio extraction module, a queue monitoring module, a parameter adaptive control module and a constellation mapping module; the likelihood ratio extraction module acquires a log-likelihood ratio sequence; the queue monitoring module determines a buffer queue depth; the parameter adaptive control module adjusts a sliding integral register accumulation period according to the buffer queue depth, and uses the sliding integral register to implement time domain integral processing on the log-likelihood ratio sequence to generate a channel quality representation parameter, and then determines a target modulation order; the application cross-layer correlates the data backlog level with the baseband statistical time scale, reduces the dependence on explicit channel state information feedback, reduces control signaling overhead, smoothes transient impulse noise interference through a dynamic accumulation mechanism, prevents buffer overflow, and improves the transmission net throughput rate of digital information in a high concurrency environment.

This invention provides a method and system for direction-of-arrival (DOA) estimation of minimum redundancy linear arrays based on the quantum giant trevally mechanism, belonging to the field of array signal processing. To address the problem of drastic performance degradation of minimum redundancy linear arrays under harsh conditions of impulse noise, this invention proposes using the mean square error of DOA estimation and the probability of successful estimation as core optimization indicators. This method effectively improves the performance degradation problem of arrays under impulse noise by constructing a mapping relationship between the positions of minimum redundancy linear array elements and DOA estimation performance, significantly enhancing the accuracy and robustness of the direction-finding system. The quantum swarm intelligence optimization method designed in this invention provides a promising solution path for the optimization problem through a global search strategy and fitness function design. This invention not only overcomes the applicability and performance bottlenecks of traditional methods under impulse noise environments but also has significant theoretical and practical application value for promoting the engineering application of array technology in high-precision direction-finding.

The application provides a multimode blind equalization method based on a swallow line function and a variable fractional order gradient. The algorithm uses statistical characteristics of an equalizer output signal and a transmitting end signal to construct a multimode cost function based on a swallow line function, so that phase errors in a channel equalization process can be compensated without an additional carrier recovery loop. Subsequently, a variable fractional order gradient method is used to replace a traditional fixed order gradient updating mode to adaptively update equalizer weight vectors, so that optimal weight vectors are obtained. Compared with the prior art, the application can not only realize channel equalization and phase recovery, but also effectively reduce residual inter-symbol interference and bit error rate in a strong impulse noise environment, while the convergence speed is accelerated, and higher equalization efficiency and robustness are shown.

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 technical field of communication signal processing, and relates to an impulse noise elimination method and device in a power line scene and an intelligent fusion terminal. The pulse noise elimination method in the power line scene comprises the following steps: acquiring a received signal; performing discrete Fourier transform on the received signal to obtain a frequency domain received signal; constructing a noise subcarrier mapping matrix, and projecting the frequency domain receiving signal to a noise subspace according to the noise subcarrier mapping matrix to obtain a noise subspace projection signal; constructing a noise observation matrix according to the noise subcarrier mapping matrix; estimating an impulse noise support set based on the noise observation matrix and the noise subspace projection signal; dimensionality reduction is carried out on the noise observation matrix through the impulse noise support set, and impulse noise estimation is calculated by using a least square algorithm; and eliminating the pulse noise in the received signal based on the pulse noise estimation to obtain the received signal after the pulse noise is eliminated. According to the method, the calculation complexity is reduced on the premise of not influencing the pulse noise estimation accuracy.

The present application relates to the field of artificial intelligence and oilfield separate layer water injection cross technology, specifically to a wireless pressure wave code communication denoising method based on CNN-LSTM hybrid model. The method comprises: constructing a composite simulation signal set containing Gaussian noise, impulse noise, periodic noise and complex frequency band noise; designing a CNN-LSTM hybrid model, the encoder extracts local features of the signal through convolution layer and pooling layer, the bidirectional LSTM layer captures the time sequence dependence, and the decoder reconstructs the signal through upsampling and convolution layer; the model is trained using mean square error loss function and Adam optimizer; when applied, the collected signal is normalized and edge filled, then input into the trained model for denoising, and the output signal is decoded to get the code value. The method can effectively learn the space-time features of the signal, adaptively suppress the complex noise from multiple sources downhole, significantly improve the signal-to-noise ratio and decoding accuracy of the signal, and enhance the reliability of wireless pressure wave communication.

An FMCW (frequency modulated continuous wave) laser radar ranging method based on FOTD-SESTH relates to the technical field of laser radar ranging, solves the problem that signals under impulse noise cannot be accurately represented in an existing time-frequency analysis method, and the like, and adopts a fractional order differential tracker to denoise FMCW laser radar beat frequency signals and suppress the impulse noise; and carrying out parameter estimation on the de-noised beat frequency signal by adopting a synchronous extraction S transformation algorithm based on Hough transformation, and carrying out distance calculation and the like. According to the method, the problem that signals under impulse noise cannot be accurately represented in an existing time-frequency analysis method is effectively solved, the influence of the impulse noise on the signal quality is weakened, a good effect is achieved on beat frequency signal analysis and parameter estimation distance calculation of the FMCW laser radar, and the stability and the calculation performance of the FMCW laser radar are effectively improved.

The application relates to the technical field of power line carrier communication, and discloses a power line carrier signal pulse noise processing method and device and communication equipment, the method comprising the following steps: slidingly buffering a time domain signal collected at a current sampling point into a first buffer window; judging whether there are continuous target time domain signals of a first preset number on any side of a first target buffer position, the target time domain signal being a time domain signal with a signal intensity greater than or equal to a preset threshold; according to a first judgment result, determining whether to slide the time domain signal output by the first target buffer position into a second buffer window, the length of the second buffer window being smaller than the length of the second buffer window; judging whether there are continuous target time domain signals of a second preset number on any side of a second target buffer position, the second preset number being smaller than the first preset number; and according to a second judgment result, processing the time domain signal output by the second target buffer position. The application can reduce misjudgment and improve signal quality.