253 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 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 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 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 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 automobile power system testing, in particular to a dynamometer and HIL integrated finished automobile multi-working-condition automatic testing and calibrating system, which combines a dynamometer rack with a hardware-in-the-loop simulation technology, develops a dynamics scene model covering conventional and limiting working conditions, and improves the testing accuracy of the dynamometer rack and the hardware-in-the-loop simulation technology. The conversion and interaction of virtual and real signals are realized through road load simulation and energy consumption simulation; the dynamometer and HIL integrated whole vehicle multi-working-condition automatic test calibration system is composed of IO, DOE, automatic calibration, objective evaluation and other functional modules, can automatically execute test cases, complete parameter calibration and optimization analysis, and perform objective evaluation on test results, and can realize multi-working-condition test, reduce the dependence on real vehicle road test, improve the test efficiency and comprehensiveness, and reduce the test cost. The controller development period is shortened, and the cost is reduced.

The invention relates to the technical field of electroencephalogram signals, in particular to a method, a device and equipment for removing motion artifacts of electroencephalogram signals, and the method can obtain to-be-processed electroencephalogram signals, decompose the electroencephalogram signals into a plurality of mutually orthogonal intrinsic mode components by utilizing multivariable empirical mode decomposition, and obtain motion artifacts of the electroencephalogram signals. The real electroencephalogram signal and the artifact signal are decomposed into different components; standard mutual information between each intrinsic mode component and a preset class label is calculated; multiplying the standardized mutual information by a corresponding intrinsic mode component to obtain an optimized signal component, so that a component related to a real signal is enhanced, and an artifact signal is weakened; all the optimized signal components are accumulated, and the electroencephalogram signals with the motion artifacts removed are obtained. It can be understood that according to the technical scheme shown by the invention, through multi-level and multi-dimensional signal decomposition and artifact removal processing, the motion artifacts can be accurately recognized and removed, and the quality of the electroencephalogram signals is greatly improved.

The invention discloses a medical signal generation method and system based on a time-frequency double-domain constraint generative adversarial network. According to the method, a multi-category electrocardiosignal generation model is designed on the basis of a conditional generative adversarial network, global attention interaction is carried out by utilizing a learnable potential feature prototype and position-coded signal features through an adaptive neural representation module, and long-distance time sequence dependence of electrocardiosignals is modeled; through the adaptive spectrum sensing module and the dynamic mask mechanism of energy sensing, the discriminator intensifies the discrimination of the consistency of signal frequency domain information while paying attention to the time domain form, thereby effectively inhibiting the non-natural high-frequency artifacts in the generation process, and ensuring the high fitting of the generated signal and the real signal. Experiments show that the fidelity and diversity of generation of various types of electrocardiosignals can be remarkably improved.

The application discloses a kind of under the condition of aviation engine inter-shaft bearing fault diagnosis method of class imbalance based on Cycle-GAN, and its specific include: data acquisition and pre-processing, improvement and establish Cycle-GAN model, train improved Cycle-GAN model and complete signal conversion, complete the fault diagnosis of actual vibration signal by Cycle-GAN model.The application proposes a new fault diagnosis transfer learning method based on Cycle-GAN, by improved Cycle-GAN model, signal sample under known condition is converted into new signal sample under unknown condition, provide the conversion signal of data distribution closer to real signal, while also ensure that conversion signal retains the fault category information in original signal, solve the problem of fault data scarcity in actual scene, and use conversion signal to train classifier, so that the classifier can distinguish fault data under unknown condition.In practical engineering application, especially in the problem of aviation engine inter-shaft bearing fault diagnosis under the condition of class imbalance, the application has wide application prospect.

The application relates to a medical imaging system, a medical imaging method and a computer readable storage medium. The method comprises the following steps: obtaining first projection data by detecting radioactive rays passing through a first scanning field of view, and simultaneously detecting a part of radioactive rays in radioactive rays not passing through the first scanning field of view but passing through a second scanning field of view to obtain second projection data, wherein the second scanning field of view is larger than the first scanning field of view, and the first scanning field of view is located in the second scanning field of view; obtaining third projection data corresponding to the second scanning field of view based on the first projection data and the second projection data; and reconstructing a tomographic image in the second scanning field of view based on the third projection data. Through the application, the problem that the signal in the extended scanning field of view obtained by speculation is greatly different from the real signal in the related art is solved, and the reconstruction quality of the computer tomographic image is improved.

The application provides a signal predistortion processing method and device, electronic equipment and storage medium, and relates to the technical field of communication. The method comprises the following steps: firstly, calculating predistortion complex array by a memory polynomial model based on historical baseband data and historical baseband feedback data; then, applying the predistortion complex array to a baseband signal to generate an in-phase component signal and a quadrature component signal containing amplitude and phase predistortion information; then, respectively performing 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; then, calculating a predistortion real signal based on the in-phase branch signal and the quadrature branch signal; and finally, performing carrier modulation on the predistortion real signal to obtain an up-converted signal. The technical scheme provided by the application solves the technical problem that phase predistortion information cannot be preserved in the signal predistortion process.

The invention discloses an insect flapping frequency inversion method based on initial phase calculation, and relates to the technical field of radar measurement. The method comprises the following steps: establishing an insect flapping motion radar echo model containing an amplitude initial phase and a phase initial phase, and obtaining a micro-motion signal from collected insect echoes based on the insect flapping motion radar echo model to generate a micro-Doppler spectrogram; based on the micro-Doppler spectrogram, calculating an initial phase value of the phase of the insect flapping wing, and based on the initial phase value, constructing a phase compensation factor to obtain a flapping wing parameter plane; and detecting an energy peak value in the flapping parameter plane based on the phase compensation factor, and determining the flapping frequency of the insect based on the position of the energy peak value. According to the method, the phase initial phase and the amplitude initial phase are introduced, the double physical processes of wing movement and body micro movement during insect flapping are restored, a radar echo model is highly matched with a real signal generation mechanism, errors are reduced, and the anti-interference capability is improved.

Disclosed is a method for determining area (202) in which attenuation object (204) is located in space (206), wherein space is divided to finite elements (210, 210a-d). The method comprises providing first transmitter (208, 302) to transmit radio signal with first strength value; moving measurement device (212, 304) along first path (214) and a plurality of real signal strength values at interest-locations (216a-b) along the first path; calculating theoretical signal strength value, for each of the interest-locations of the first path, using radio signal propagation model assuming a free propagation between the first location and the interest-locations; setting attenuation value for each of the finite elements, along lines (218a-b) connecting the first location and the interest-locations; and using locations of the finite elements with the non-free space attenuation value to determine the area in which the attenuation object is located.