304 results about "Real signal" patented technology

The invention discloses a Beidou strong deception jamming detection processing method and system, and relates to the technical field of satellite navigation anti-jamming. The invention provides a solution based on dynamic gain monitoring and multi-dimensional feature collaborative analysis to solve the problems that a Beidou / GNSS system is prone to being affected by strong deception interference signals in a complex electromagnetic environment to suppress radio frequency link gain, and positioning failure is caused by the fact that deception signals and real signals are difficult to distinguish in a traditional technology. According to the method, radio frequency gain change is tracked in real time through a digital AGC module to generate an interference-to-signal ratio, an equivalent carrier-to-noise ratio is calculated in combination with a signal carrier-to-noise ratio for primary judgment, and accurate recognition is achieved by fusing verification of multi-dimensional characteristics such as code phase consistency, carrier frequency stability and signal strength fluctuation. The system comprises a radio frequency front end, a digital AGC module, a correlator channel and an interference processing module, and precise suppression of strong deception interference is realized through dynamic reference gain learning, interference signal mark elimination and robust positioning resolving mechanisms.

The present invention relates to the field of geothermal resource development technology, specifically a geothermal well monitoring and maintenance management system and method based on the Internet of Things; the method steps are: through a denoising method combining adaptive fuzzy clustering and deep convolutional neural network, the real signal of sensor data is restored; by generating a check mark, the integrity of the collected geothermal well monitoring data is checked; combined with LSTM time series modeling and deep reinforcement learning for fault prediction and optimization, support vector machine is used for fault type identification, and maintenance strategies are formulated according to the prediction results, the appropriate maintenance type and timing are selected, and resource scheduling optimization is performed to generate a detailed maintenance plan; data and maintenance information are visualized and pushed to on-site staff in real time, and the staff performs operations according to the maintenance plan, and automatically records and updates the health status of the equipment. The present invention improves the monitoring and maintenance efficiency of geothermal wells through intelligent data processing and decision support.

The invention relates to the technical field of optical lens defect detection, and particularly discloses an optical lens laser-induced damage on-line detection method and system, a linear polarizer and a narrow-band filter are connected in series in a detection laser light path, non-target polarized light is filtered through polarization direction matching, meanwhile, interference of the environment and scattered light is inhibited through the narrow-band filter, and the laser-induced damage on-line detection system is obtained. According to the method, external source noise such as ambient light and non-target polarized light is reduced through polarization matching and narrow-band filtering, randomness of speckle noise is offset through multi-angle collection and signal fusion in a visual detection mode, exposure is dynamically adjusted, an image is fused to improve the signal-to-noise ratio, noise and real signals are decoupled through self-supervised learning, and the real-time performance of the system is improved. Dust interference is removed in combination with morphological operation; a deep learning reasoning model enhances the damage identification capability in a noise environment, defocusing blur caused by mechanical vibration is eliminated through phase conjugate correction, and the influence of photoelectric conversion noise, dust interference and the like on the detection efficiency and accuracy is remarkably reduced through multi-link linkage.

The invention discloses a full-automatic cable vibration high-precision measurement method based on a machine vision technology, and relates to the technical field of structural vibration measurement. The method comprises the following steps: installing and calibrating equipment, and ensuring that a cable plane coincides with an imaging plane; high-frame-rate video acquisition is carried out, and comparison verification is carried out in combination with an accelerometer; screening effective pixels based on the pixel intensity time sequence variance, and constructing a pixel intensity space-time matrix by using the time sequence of the pixels; sVD decomposition is adopted to extract an accumulated energy ratio gt; 90% of the main modes are reconstructed, and real signals are revealed; a plurality of inhaul cables are automatically positioned and coded through gradient features and Hough transform; sub-pixel-level vertical displacement is calculated by applying an optical flow method, and median filtering is combined to resist noise; fourier transform is carried out to obtain a spectrogram, and a fundamental frequency is identified; and calculating cable force based on a string pulling theorem. According to the method, remote, full-field and multi-target synchronous monitoring is realized, the displacement measurement precision reaches a sub-pixel level, and a high-precision and full-automatic solution is provided for health monitoring of structures such as bridges.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention relates to the technical field of signal analysis, in particular to a GNSS (Global Navigation Satellite System) detection method and system based on machine learning, and the method comprises the steps: receiving a navigation radio-frequency signal containing reality and deception, and carrying out the down-conversion of an RF (Radio Frequency) front end to obtain intermediate-frequency data; gNSS signal capture is completed by searching correlation peaks in frequency and code phases based on intermediate frequency data; in the capturing stage, a motion state, a correlation peak number, a noise value, capturing parameters and peak + / -1 chip sampling features are extracted and input into a machine learning classification model to judge whether cheating exists or not; if so, separating the deception signal from the real signal, and respectively sending the deception signal and the real signal into independent tracking channels; measuring an array element carrier relative phase by an array antenna, establishing a phase center model, and correcting to obtain an effective phase center and a differential baseline; according to the method, a deception azimuth angle and a pitch angle are obtained, a beam weight is calculated, a deception direction is subjected to null setting, a real direction is subjected to main lobe protection, and a weight value is fed back in real time to improve a signal carrier-to-noise ratio and locking stability. According to the invention, the problem that the existing commercial GNSS signal is low in power and easy to be interfered by deception is solved.

The invention provides a wind tunnel structure sensitive reflection settlement position intelligent identification method, and belongs to the technical field of wind tunnels. Vibration sensors and displacement sensors are arranged at key positions of a wind tunnel structure to form a monitoring network, collected signals are preprocessed, and a settlement factor matrix is established; a dynamic load matrix is constructed to describe composite load distribution, a vibration burr identification matrix is established, real signals and noise are separated by adopting wavelet transformation, a slow settlement trend matrix is constructed to extract a long-term change rule, and a least square optimization algorithm is adopted to jointly solve each matrix parameter to establish a settlement position identification function. The settlement three-dimensional position coordinate is determined according to the multi-sensor data fusion result, the structure safety state is evaluated through the settlement risk coefficient, and the technical problems that the wind tunnel structure settlement position recognition precision is insufficient, and a real settlement signal and a noise interference signal cannot be effectively distinguished are solved.

The invention provides a GNSS deception jamming joint detection system and method, and relates to the technical field of GNSS. GNSS navigation signals are received and processed through a baseband signal processing module, and advanced, instant and lagged related function values of an I path and a Q path are generated; a signal layer index unit calculates a moving variance of a code phase difference based on the correlation function value to generate a signal layer index; the information layer index unit calculates a code carrier difference value based on the pseudo-range, the carrier frequency and the integral carrier phase to generate an information layer index; and the spoofing detection module performs data fusion on the signal layer index and the information layer index to generate a joint detection index, and judges a spoofing state through a threshold value. The distortion of an IQ code correlation value integral signal is captured through a signal layer, and on the other hand, the inconsistency of a deception signal carrier, a pseudo-range relative measurement value and a real signal is captured through an information layer, so that the technical effect of rapid complementary detection of the signal layer and the information layer on deception interference is realized.

The invention relates to the technical field of satellite time service, and discloses a Beidou anti-interference deception time service method and system based on multilayer detection, and the method comprises the steps: receiving a multi-band signal of a satellite, and recording a noise parameter; performing time frequency domain feature extraction on the multi-band signal, and performing weighted fusion on the extracted time frequency features to obtain a comprehensive feature vector; and establishing a conjoint analysis model, detecting an abnormal signal according to the comprehensive feature vector, performing time calculation on the detected real signal, and outputting time information. According to the method, efficient discrimination and accurate time service output of abnormal signals are ensured, the time service accuracy and stability of a Beidou system in a complex interference scene are remarkably improved, and a reliable time reference is provided for key industries.

The invention relates to the field of data analysis in electronic warfare simulation, in particular to a multi-target interference effect real-time analysis method and system in electronic warfare simulation, and the method comprises the steps: building a dynamic multi-path channel model through obtaining multi-path propagation parameters of all interference targets in a target region; simulating a time domain superposition process of an interference signal waveform and a real signal waveform at a receiving end according to the dynamic multi-path channel model to obtain a receiving end synthetic signal and extract signal characteristic parameters; carrying out weighted fusion on the signal characteristic parameters to obtain a dynamic interference efficiency value of the interference target; according to a comparison relationship between the dynamic interference efficiency value and a preset interference efficiency threshold value, adjusting an emission parameter; and when the dynamic interference efficiency value of the interference target is greater than or equal to a preset interference efficiency threshold value, outputting the emission parameter as an interference strategy parameter. The problems of signal misalignment, one-sided interference evaluation and the like in a complex electromagnetic environment are solved, and the accuracy of interference effect analysis and battlefield adaptability are remarkably improved.

The invention relates to a bearing unbalanced sample fault diagnosis method based on dynamic modeling and a causal interpretable GAN, and belongs to the technical field of mechanical intelligent detection. Aiming at the problems of low model diagnosis precision caused by data imbalance in the prior art and deficient physical significance and poor interpretability of existing GAN network generation samples, the invention provides a technical scheme of fusing dynamic modeling and an interpretable generative adversarial network. The method comprises the following steps: generating a fault response signal with physical significance through a two-degree-of-freedom dynamic model; constructing a GabcauACGAN network, combining a causal loss function to restrain the relevance between the generated features and fault tags, and extracting time-frequency features by adopting a learnable Gabor filtering layer. The method has the technical effects that the time-frequency similarity SSIM of a generated fault sample and a real signal reaches 99%, and the diagnosis accuracy is improved to 98% or above.

The invention relates to the technical field of underground magnetic resonance detection, in particular to a multi-scale underground nuclear magnetic resonance detection signal denoising method and system. Comprising the following steps: acquiring underground nuclear magnetic signal data, and converting the underground nuclear magnetic signal data into an underground nuclear magnetic signal spectrogram; an encoder is adopted to extract multi-scale features from the underground nuclear magnetic signal spectrogram by dynamically adjusting the size of a convolution kernel, important information features of the multi-scale features are enhanced through a self-attention mechanism, and the multi-scale features are compressed to a potential space after being subjected to residual denoising to form real signals; generating a forged signal from the low-dimensional noise vector in the potential space by adopting a generator; the authenticity of the forged signal is judged by a discriminator and fed back to a generator, and the generator adjusts own parameters according to the feedback, so that the forged signal is close to a real signal; and a de-noised signal is obtained by recovering the space structure of the forged signal through the decoder. The method does not need pure signal pairing, is suitable for a complex underground nuclear magnetic detection environment, and improves the signal quality and the detection precision.

The invention discloses a signal reconstruction and noise suppression method and system for a distributed Raman temperature measurement sensing system, and the method comprises the steps: obtaining original anti-Stokes light and Stokes light time domain signals of the distributed Raman temperature measurement sensing system, and constructing a space-time signal matrix; performing preprocessing and data enhancement on the space-time signal matrix; constructing a dual-path feature fusion network for Raman signal reconstruction and noise suppression, and initializing the network; defining a loss function fusing temperature physical constraints, and training the dual-path feature fusion network; and inputting an original Raman signal to be processed into the trained network, outputting the reconstructed anti-Stokes and Stokes signals with the high signal-to-noise ratio, and carrying out temperature demodulation according to the anti-Stokes and Stokes signals with the high signal-to-noise ratio. While the spatial resolution is maintained or improved, the noise of the Raman temperature measurement system is effectively suppressed, especially the real signal of a temperature abrupt change point can be recovered, the temperature demodulation precision, stability and reliability are improved, and reliable data are provided for infrastructure safety monitoring.

The invention discloses a binocular camera global ultra-sensitive depth direction vibration measurement method, and relates to the technical field of structural vibration measurement. The method comprises the following steps: completing internal reference and external reference calibration of a binocular camera based on a Zhang's calibration algorithm, and ensuring the three-dimensional reconstruction precision; in image processing, effective pixels with high dynamic response are screened by calculating pixel intensity time-history variance, a pixel intensity space-time matrix is constructed, and singular value decomposition is adopted to extract front k-order modals to reconstruct the pixel intensity matrix so as to reveal real signals originally submerged in imaging noise and rounding errors. And multi-direction cost aggregation is carried out by using a semi-global matching algorithm, the parallax calculation robustness in a low-contrast scene is improved, and a high-precision depth map is generated. And depth information is converted into three-dimensional space information in combination with camera parameters, so that non-contact measurement of the vibration time travel curve in the full-field depth direction is realized.

The invention discloses a deception jamming signal identification and interference source positioning method and system, and belongs to the technical field of signal identification and interference source positioning, and the method comprises the steps: receiving intermediate frequency receiving signals of a global satellite navigation system, including a real satellite signal, a forwarding deception jamming signal, a multipath signal and a noise signal; performing multi-component parameter estimation based on a space alternating generalized expectation maximization algorithm to obtain a parameter set comprising time delay, Doppler frequency shift and complex amplitude; according to consistency detection of receiver clock differences and Doppler frequency shift information of a plurality of adjacent base stations, identifying deception jamming components and real signal components from the parameter set; and determining the position of an interference source by adopting a delay inequality positioning algorithm based on the arrival delay inequality of the deception interference component among the plurality of adjacent base stations. According to the method, the GNSS signal characteristics are utilized, space-power time-frequency joint detection is realized with low complexity under the condition of not needing extra hardware cost, and the performance of deception jamming detection in practical application is improved.

The invention relates to the technical field of laser radar wind field inversion, in particular to a coherent Doppler anemometry laser radar-based three-dimensional wind field inversion method, which comprises the following steps of: acquiring original power spectrum data of an anemometry laser radar to be inversed in each direction, and sequentially performing interpolation processing on the original power spectrum data in each direction to obtain an inversion result; obtaining power spectrum data after interpolation in each direction; calculating to obtain a cumulative power spectrum function; and optimizing the obtained vector three-dimensional wind field by using the cumulative power spectrum function so as to calculate the maximum function value of the cumulative power spectrum function, and the three-dimensional wind field corresponding to the maximum function value is the optimal three-dimensional wind field. According to the method, all the power spectrum data after azimuth interpolation are directly processed as a whole, random noise is averaged by using an accumulation process, a real signal is highlighted, and stable and accurate three-dimensional wind field inversion can be realized even when the signal-to-noise ratio is extremely low.

The invention discloses a blind signal source separation method based on a multi-resolution attention separation network, and belongs to the technical field of signal processing. The method comprises the following steps of signal preprocessing, multi-scale feature extraction, attention module training, multi-resolution feature fusion, signal source recognition model training, model performance real-time monitoring and multi-scene adaptation optimization, and microcosmic and macroscopic features of signals are effectively captured through a multi-scale channel feature coding network. The adaptability of the model to non-stationary signals and complex structure signals is enhanced, the separation precision is improved, the attention mechanism can dynamically adjust feature weights, highlight target signal features, suppress background noise and remarkably improve the signal separation quality, meanwhile, calculation resource allocation is optimized, the model reasoning time is shortened, and the method is suitable for large-scale popularization and application. The MRAS-Net adopts a classification-oriented consistency synthesis strategy to ensure that the synthesized virtual signal features and the real signal features are highly consistent in classification tasks, and the generalization ability and the result interpretation of the model are enhanced.

The invention discloses a dynamic fracture source parameter quantitative inversion and fracture cross-scale characterization method and system. The characterization method comprises the steps that waveform signals of the whole rock mass fracture process are collected; calculating a ratio of a short time window to a long time window of the characteristic function to identify real signal characteristics, determining dynamic fracture seismic source coordinates according to a positioning algorithm, and screening to obtain a seismic source coordinate point set with reliable positioning; establishing a displacement discontinuous tensor dynamic fracture source model, constructing an objective function and calculating a dynamic fracture source moment tensor component; according to the moment tensor component, dynamic fracture source parameters are solved, and description of the scale, orientation and mechanical properties of a single dynamic fracture event is achieved; all dynamic fracture source core parameters are summarized and counted, all dynamic fracture source types are counted respectively, a dynamic fracture group data set of space orientation dominant orientation, dissipated energy space distribution, damage tensor main axis direction and magnitude statistical distribution b value is formed, and cross-scale process depiction of rock mass dynamic fracture evolution from single to cluster and from microcosmic to macroscopic is achieved.

The invention provides an industrial single-chip microcomputer automatic control method based on time sequence event triggering, and the method comprises the steps: carrying out the multi-period sampling and time window division of an original signal inputted by an industrial field sensor or a switching value, and extracting the time distribution characteristics of the signal; performing signal stability judgment and pseudo edge elimination based on the signal time distribution characteristics, and outputting a stable edge event set; performing event confirmation delay and multi-level logic judgment on the stable edge event set to generate a preliminary time sequence trigger signal; environment characteristic data are collected, real-time feedback of an actuator is obtained, and system judgment parameters are dynamically corrected; and generating a control command and verifying an execution result based on the preliminary time sequence trigger signal and actuator feedback, the method can dynamically distinguish real signals and noise under vibration interference, simultaneously ensures the real-time performance and trigger reliability of the system, and solves the problems of insufficient edge detection stability and false trigger phenomenon caused by mechanical vibration.

The invention relates to an ultrahigh-frequency signal interference suppression method based on space-time domain joint discrimination, and aims to solve the problem of poor interference suppression effect caused by low discrimination accuracy of interference signals which are different from real ultrahigh-frequency signal transmission paths but have similar time-frequency characteristics in the prior art. Comprising the following steps: constructing a distributed receiving network based on a plurality of signal receiving nodes and a spatial position relationship thereof, and synchronously receiving ultrahigh frequency signals in a set time window; clustering the ultrahigh frequency signals to form associated signal groups and signal feature groups based on the spatial domain, the time domain and the spectrum features of the ultrahigh frequency signals; calculating the positions of the signal sources corresponding to all the signal groups in combination with the spatial position relationship of the signal receiving nodes; and carrying out transmission path modeling and path rationality scoring based on the position of the signal source, then constructing a joint scoring function, discriminating a real signal source and a to-be-suppressed signal source, and carrying out interference suppression on the to-be-suppressed signal source.

The invention provides a loudspeaker speech enhancement method and device, electronic equipment and a storage medium, and belongs to the technical field of speech signal processing, and the method comprises the steps: obtaining an initial spectrum signal and an initial power spectrum signal corresponding to an initial audio signal; inputting the initial power spectrum signal into a feedback elimination network model to obtain a target frequency domain signal; and obtaining a target audio signal based on the target frequency domain signal. The method comprises the following steps: performing mask adjustment on the frequency domain amplitude of an initial frequency spectrum signal by using a real number signal mask to generate a frequency domain estimation signal, and performing mask adjustment on the frequency domain amplitude and phase of the initial frequency spectrum signal by using a complex number signal mask to obtain a frequency domain enhancement signal. And finally, a target frequency domain signal is determined based on the frequency domain estimation signal and the frequency domain enhancement signal, so that the output target frequency domain signal has higher fidelity in the aspects of amplitude and phase, environmental noise and loudspeaker feedback signals can be removed to the greatest extent, and the squeal phenomenon is effectively inhibited.

The invention provides a deception jamming detection method, electronic equipment, a storage medium and a product. The method comprises the following steps: acquiring in-phase channel outputs and orthogonal channel outputs of a plurality of correlators; constructing a first detection quantity of the correlator according to the in-phase channel output and the orthogonal channel output of the correlator; constructing a second detection quantity of the correlator according to the first detection quantity of the correlator within a period of time; and taking the processed second detection quantity of the correlator as the input of a machine learning network model, and training the machine learning network model to obtain a deception jamming detection model. According to the method, the amplitude output of a plurality of correlators of a receiver tracking loop is utilized, the distortion effect of a deception signal invading the tracking loop on a correlation peak can be fully reflected, and the influence of carrier phase difference of the deception signal and a real signal on an algorithm can be relieved; dynamic changes caused by deception jamming can be captured, and the detection sensitivity and accuracy are improved.

The invention discloses a placenta implantation analysis method based on an ultrasonic image, particularly relates to the technical field of medical image analysis, and is used for solving the problem that the placenta implantation condition is difficult to accurately diagnose due to artifact interference caused by complex reflection of an existing placenta area and adjacent blood vessels. The method comprises the following steps: acquiring ultrasonic radio frequency data, generating a two-dimensional gray scale image and a Doppler blood flow image, constructing a dynamic interference distribution diagram, extracting artifact time sequence characteristics, realizing frequency domain separation of an artifact region and generation of a multi-reflection interference pixel set in combination with wavelet transform, and identifying a real blood flow signal through an adaptive threshold algorithm. According to the method, artifacts and real signals are fused to generate an artifact-removed image, morphological processing based on structural elements is carried out on the artifact-removed image, continuous boundary information of the placenta and the uterine wall is extracted, suspicious blood vessel invasion paths are marked in a partitioned mode, and the accuracy of image analysis is improved.

The invention relates to the technical field of fluid ultrasonic analysis, in particular to a quality control method based on a concrete member and production equipment. According to the method, suspected bubble signals of each vibration are screened, the vibration contribution degree is obtained according to the fluctuation degree of the amplitude difference of adjacent data points of each signal section of the suspected bubble signals of each vibration, and then the bubble signals of each vibration are divided into to-be-corrected signals and real signals; according to the vibration contribution degree of each signal section of the to-be-corrected signal in the corresponding signal section in the real signal, the vibration contribution degree of the signal section of the to-be-corrected signal is adjusted, and the vibration influence degree of the suspected bubble signal is obtained; selecting a bubble existence signal of each vibration according to the fluctuation condition of the vibration influence degree; and stopping vibrating the concrete for the next time until the bubble existence signal does not exist in each time of vibration. According to the invention, whether the bubbles in the concrete are completely discharged can be accurately and effectively judged, so that the quality of concrete construction is improved.

The invention relates to the technical field of image processing and extremely weak light imaging, and particularly provides a photon counting image reconstruction method and system, and the method comprises the steps: determining a target threshold value based on the adjustment of a dynamic adjustment coefficient under different illumination or gain conditions based on an initial threshold value; counting a first photon number of a single photon signal in the background image and a second photon number of the single photon signal in the signal image based on a target threshold, and generating noise photon number statistical distribution and signal photon number statistical distribution to determine a dynamic photon number processing interval; and performing time-space joint accumulation on the pixels conforming to the dynamic photon number processing interval, generating a two-dimensional photon accumulation matrix, and converting the two-dimensional photon accumulation matrix into a gray level image to form a single group of reconstructed gray level images. According to the method, real signals can be effectively extracted, the influence of dark noise, shot noise and readout noise is suppressed, zero noise detection is realized, and finally, the dynamic range, the signal-to-noise ratio and the stability of the image are remarkably improved through single-group image reconstruction and multi-group integration processing.

The invention discloses an integrated temperature and pressure signal cooperative compensation transmitting method and device, and relates to the technical field of industrial measurement and control, and the method comprises the steps: constructing a measurement array of an integrated piezoresistive pressure sensor and a platinum resistor temperature sensor; carrying out layout installation and region division according to an application scene; designing a temperature and pressure test parameter table and performing a synchronous test to obtain a parameter flow, a signal flow and a real signal flow; performing compensation coupling analysis based on the test data, and establishing a corresponding number of cooperative compensation channels; and a real-time signal is obtained and self-adaptive compensation and signal transmission are carried out through the compensation channel. The technical problems that in the prior art, due to the spatial difference of separated sensors, measurement errors are large, signals are prone to being interfered, and the high-precision measurement requirement under the complex working condition is difficult to meet are solved, accurate measurement and dynamic compensation of temperature and pressure signals are achieved through the integrated temperature and pressure measurement array and the cooperative compensation channel, and the measurement precision of the temperature and pressure signals is improved. And the measurement precision and the signal stability are improved.

The invention discloses an AI-based power distribution equipment partial discharge online diagnosis and positioning method, which relates to the technical field of equipment detection, and comprises the following steps: acquiring high-frequency signals in power distribution equipment in real time, sequentially calculating a topological inconsistency index, an echoing characteristic response abnormal index and a burst pulse ring chaos index, and calculating a false index based on the three results, so as to realize the partial discharge online diagnosis and positioning of the power distribution equipment. The method is used for judging whether the signal is a real signal generated by partial discharge of equipment. And if the false index is lower than a threshold value, determining that the discharge signal is a real discharge signal, and performing type identification and accurate positioning through a preset AI model. According to the method, non-discharge interference signals can be effectively identified, the fault judgment accuracy is improved, the false alarm rate is reduced, the trust of operation and maintenance personnel on a system diagnosis result is enhanced, the manual inspection pressure is relieved, and the maintenance cost is reduced.

The invention provides a signal pre-distortion processing method and device, electronic equipment and a storage medium, and relates to the technical field of communication, and the method comprises the steps: firstly carrying out the calculation of historical baseband data and historical baseband feedback data through a memory polynomial model, and obtaining a pre-distortion complex number coefficient group, then applying the predistortion complex coefficient group to a baseband signal to generate an in-phase component signal and a quadrature component signal containing amplitude and phase predistortion information, and then respectively carrying out carrier modulation on the in-phase component signal and the quadrature component signal to obtain an in-phase branch signal and a quadrature branch signal; and then calculating according to the in-phase branch signal and the orthogonal branch signal to obtain a pre-lost real signal, and finally carrying out carrier modulation on the pre-lost real signal to obtain an up-conversion signal. By implementing the technical scheme provided by the invention, the technical problem that phase pre-distortion information cannot be reserved in a signal pre-distortion process is solved.

The invention discloses a fast deception detection algorithm based on an autocorrelation amplitude. The fast deception detection algorithm comprises the following steps: step 1, carrying out data preprocessing on an ACF output by a DLL to generate a sample; 2, inputting the real signal and the deception signal into an LSTM network for model training; and step 3, carrying out deception detection by using the trained LSTM network. According to the method of the invention, detection can be carried out without a large number of samples when deception detection is carried out based on a deep learning method, and rapid detection can be realized.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a black flight unmanned aerial vehicle identification method, which comprises the following steps: a sight distance range detection step: detecting an unmanned aerial vehicle in a sight distance range through a visual sensor, positioning the position of the unmanned aerial vehicle based on a preset target detection algorithm, and establishing an unmanned aerial vehicle position list; a black flight unmanned aerial vehicle identification step: continuously receiving an ADS-B signal and a Remote ID signal through a pre-installed signal receiving device, and based on the position of the unmanned aerial vehicle and the ADS-B signal and the Remote ID signal, sequentially verifying whether the corresponding unmanned aerial vehicle sends the ADS-B signal and the Remote ID signal, whether the signal is a registration signal and whether the registration signal is a real signal in a sight distance range; and when any verification result is no, determining that the unmanned aerial vehicle is a black flight unmanned aerial vehicle. According to the invention, the position and the signal of the unmanned aerial vehicle are comprehensively analyzed and judged, and the reliability and the robustness of counterfeit signal identification are improved.