72 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 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 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 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 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 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.

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 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 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.

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.

The application provides a measurement preprocessing method based on correlation entropy and attention mechanism. The dynamic weight is calculated through the Gaussian kernel correlation entropy of the historical innovation and the current innovation in the sliding window, and the abnormal measurement is adaptively identified and suppressed. The method adopts a feedforward inhibition mechanism to block noise propagation, while retaining the optimality of the KF framework, and realizes efficient anomaly detection with lower complexity. Simulation experiments show that in the Gaussian mixed noise and impulse noise scene, the application significantly reduces the mean square error of KF, the dynamic window mechanism is faster than the noise statistical modeling algorithm in response speed, the tracking accuracy is equivalent to Huber-KF, and the parameter adaptability is stronger, which verifies the robustness and tracking performance of the method in non-Gaussian noise, especially in the impulse noise environment, and provides theoretical support for subsequent expansion to the maneuvering target scene and deep learning fusion.

The invention discloses a method for calculating and formulating a high-speed railway tunnel micro-pressure wave control standard, which adopts a CFD (Computational Fluid Dynamics) numerical simulation method, takes an obtained tunnel portal micro-pressure wave space distribution characteristic as a basis, and combines a pressure impulse noise damage risk standard and a high-speed railway line safety area characteristic to calculate and formulate a high-speed railway tunnel micro-pressure wave control standard. And the simple and reasonable calculation and formulation of the tunnel micro-pressure wave control standard are realized. By the adoption of the technical scheme, on the premise that serious harm of the micro-air-pressure waves to the environment is limited, high-frequency noise and low-frequency vibration are considered at the same time, and simple, rapid and reasonable calculation and formulation of the high-speed railway tunnel micro-air-pressure wave control standard are achieved.

A phase order correction method for multi-frequency cascaded phase unwrapping is proposed. First, the low-frequency unfolded phase is directly calculated using the multi-frequency fringe method, and then the mid-frequency unfolded phase is obtained to acquire the initial fringe order obtained through a small frequency ratio. Subsequently, based on the shifting wrapped phase method, a fringe order misaligned by half a cycle from the initial fringe order is obtained. Accurate phase reconstruction is achieved through fringe order correction. These two methods complement each other to simultaneously address the accuracy degradation caused by pseudo-2-step transitions and impulse noise errors, while preserving phase map details. This invention obtains the correct order corresponding to the high-frequency wrapped phase through a phase recovery method at two fringe frequencies, and uses this to calculate the high-frequency unfolded phase, achieving fast and accurate phase unwrapping.

The present application relates to the technical field of array signal processing and direction of arrival (DOA) estimation, and particularly relates to an end-to-end DOA estimation method and system based on complex correlation entropy under impulse noise; a complex snapshot matrix received by an array is input into a learnable complex correlation entropy layer, impulse noise is adaptively suppressed, and a complex correlation entropy matrix is constructed; then robust spatial features are extracted through a full-link complex value CNN module, and signal amplitude and phase coupling information are completely retained; finally, a DOA estimation value is output through a full-connection regression module, and an end-to-end joint optimization of a noise suppression module and a feature extraction module is realized in the form of a mean square error loss. The present application has obvious advantages in the scene of strong impulse noise and low snapshot number under different characteristic indexes and generalized signal-to-noise ratios, and has the advantages of high precision, strong robustness, wide engineering applicability, etc., and can be used for target positioning in non-Gaussian interference scenes such as radar, sonar, 5G / 6G millimeter wave communication, etc.

The invention discloses a power line pulse noise identification and suppression method and system, and the method comprises the steps: 1, receiving ADC signals which comprise an OFDM signal with effective information and pulse noise and a pulse noise received in a receiving idle state; step 2, setting a sliding window length and a stepping length Lstep for an ADC signal; step 3, identifying pulse noise of the ADC signal in each section of sliding window, and suppressing the pulse noise; and step 4, after stepping for Lstep, repeatedly executing the step 3 until all ADC signals are processed, and obtaining suppressed signals. According to the method and the system, the influence of signal distortion, high bit error rate and stability reduction caused by pulse interference on a subsequent digital system is solved, and the reliability of the subsequent digital system is improved.

The invention relates to the technical field of radio compass angle resolving, in particular to an automatic gain control radio compass angle resolving method, which comprises the following steps of: introducing two-stage automatic gain control through a complete process of signal acquisition, digital-to-analog conversion, frequency domain conversion, two-stage gain control, frequency conversion and azimuth angle calculation; the first-stage gain control realizes feedback control based on a frequency domain judgment result, and narrow-band interference and pulse noise in a frequency domain can be filtered in a targeted manner; the second-stage gain control realizes fine gain adjustment based on time domain signal amplitude judgment, and further stabilizes the signal amplitude; meanwhile, through the synergistic effect of two-stage gain control, subsequent frequency conversion and resolving links can be carried out based on stable and clean signals, the resolving process is simplified, resolving errors caused by signal interference are avoided, high-precision resolving of the azimuth angle is achieved, the overall process logic is clear, engineering implementation is easy, and the method is suitable for large-scale popularization and application. The method is suitable for aviation navigation and other scenes with high reliability requirements.

The invention belongs to the technical field of indoor positioning, and discloses a robust indoor positioning method and system based on a weighted LP norm, and the method comprises the steps: firstly constructing a linearization measurement model based on a TOA distance measurement value; then constructing an outer-layer weight matrix by calculating the covariance of a noise propagation error, and forming a weighted LP norm objective function in combination with an inner-layer weight matrix constructed by a current residual error; and solving the objective function by adopting an iterative optimization algorithm until the final coordinate of the user equipment is obtained through convergence. According to the invention, the influence of impulse noise and outliers is adaptively suppressed through a double-layer weighting mechanism, and high positioning precision and stability can still be kept in a non-Gaussian noise environment. The method does not need to add extra hardware, is high in calculation efficiency, is easy for engineering deployment, is suitable for a UWB system, and can also be popularized to other wireless positioning scenes based on distance measurement.

The application discloses a signal processing method of power carrier communication based on OFDM modulation, relates to the technical field of power carrier communication, and comprises the following steps: S1, cyclic prefix and guard interval design and insertion; S2, improved synchronization algorithm for frequency offset processing; S3, noise suppression optimization of a front-end signal processing module; S4, parallel processing architecture design and task allocation; S5, channel estimation at a receiving end; S6, frequency domain equalization processing; S7, OFDM demodulation processing; and S8, symbol synchronization and data recovery. Through the steps S1 and S2, the effects of effectively resisting power line channel multipath delay, reducing inter-symbol interference and inter-carrier interference are achieved. Through the steps S3, S5 and S6, the effects of accurately identifying power line impulse noise and Gaussian noise characteristics and dynamically suppressing noise are achieved. Through the steps S4 and S8, the effects of improving signal processing efficiency by using a parallel processing architecture and avoiding processor overload are achieved, and meanwhile, the data error correction capability is enhanced by convolution coding or Turbo coding, so that the real-time performance and reliability of high-speed data transmission are ensured.

The application relates to the technical field of adaptive filtering, and discloses a variable-step-size robust adaptive filter and a filter network, which are aimed at solving the technical problem that a traditional adaptive filter cannot effectively balance convergence speed, steady-state accuracy and anti-pulse noise interference. A nonlinear error factor is introduced, a large error caused by pulse noise is effectively suppressed through a generalized function form of the nonlinear error factor, and the robustness of a filtering process is significantly improved; meanwhile, a variable step-size mechanism is proposed, a theoretically optimal candidate step size is calculated online by minimizing a mean square deviation at a next moment, and an adaptive target variable step size is generated through truncation and time smoothing processing, so that a dynamic balance between fast convergence and low steady-state misadjustment is realized. The filter is expanded to a distributed network, each node follows a diffusion strategy of first updating and then combining, and a joint coefficient is used to realize global information sharing and performance collaborative optimization.

This invention relates to a system and method for generating sonar data for seafloor sedimentary layer detection, comprising: configuring simulation parameters; randomly generating a seafloor sedimentary layer model containing various geological features; calculating the reflection coefficient sequence of each layer in the model; generating wavelets based on sound source parameters and convolving them with the reflection coefficients to obtain synthetic sonar data; adding sensor effects including Gaussian white noise, reverberation, and impulse noise; synchronously generating a pixel-level semantic segmentation mask aligned with the data time axis; and finally outputting labeled training samples. This invention solves the problems of existing simulation technologies, such as limited geological models, lack of sensor effect simulation, and insufficient labeled data. It can automatically generate large-scale, high-fidelity, and precisely labeled sonar datasets, effectively supporting the training and algorithm verification of deep learning models.

The invention provides an active protection method and device for impulse noise and a protection earmuff, and the method comprises the steps: collecting a first external sound pressure at a current moment through the protection earmuff under the condition that the protection earmuff employs a transparent mode to carry out the enhanced playback of external sound, and predicting a target sound pressure at a target moment based on the first external sound pressure; wherein the target moment is behind the current moment, and the interval duration between the target moment and the current moment is determined based on the activation starting time of the noise reduction mode; if it is determined that pulse noise exists at the target moment based on the target sound pressure, the transparent mode is switched into a noise reduction mode, and the mode switching time is activation starting time; and controlling the protective earmuffs to offset the external sound in a noise reduction mode. According to the technical scheme of the invention, active protection can be carried out in advance, and mode switching can be completed in advance, so that a high-real-time protection effect can be achieved.

The present application relates to the technical field of communication signal processing, and relates to a power line scene impulse noise elimination method, device and intelligent fusion terminal. The power line scene impulse noise elimination method comprises: obtaining 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 received 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; reducing the dimension of the noise observation matrix through the impulse noise support set, and calculating an impulse noise estimate by using a least square algorithm; and eliminating the impulse noise in the received signal based on the impulse noise estimate to obtain a received signal after the impulse noise is eliminated. The present application reduces the calculation complexity without affecting the accuracy of the impulse noise estimate.