101 results about "Energy (signal processing)" patented technology

The invention provides an anti-interference sound signal data extraction method based on energy data perception, and belongs to the technical field of underwater sound signal processing, and the method comprises the steps: carrying out the A / D sampling and digital orthogonal demodulation of a received sound signal, and obtaining a baseband signal; performing low-pass filtering and downsampling on the baseband signal to obtain a preprocessed signal; performing matched filtering on the pre-processed signal by using a reference signal; the key improvement lies in that a local energy estimation function of a preprocessed signal is calculated, and normalization processing is carried out on a matched filtering result and the energy estimation function to obtain a normalized detection signal. The method effectively suppresses the shielding effect of a strong interference signal on a weak target signal under a non-orthogonal reference signal and a strong near-end and weak far-end scene, remarkably reduces the misrecognition probability, and improves the accuracy and reliability of the extraction of a long-distance and low-signal-to-noise-ratio target beacon signal in a complex underwater acoustic environment. The method is suitable for underwater navigation, positioning and communication systems.

The invention discloses a human body posture estimation method based on radar point cloud imaging and multi-dimensional feature fusion, and relates to the cross technical field of computer vision and radar signal processing, and the method comprises the following three key technical links: firstly, improving the target resolution through spatial energy distribution estimation; reconstructing target three-dimensional space distribution by using the positive correlation between radar signal energy and a target reflection area and adopting a least square estimation algorithm; secondly, constructing a structured multi-dimensional point cloud matrix, and converting sparse radar point cloud into high-information-density imaging representation through a distance-speed hierarchical sorting strategy; and finally, designing a multi-dimensional feature fusion attitude estimation network, integrating three-dimensional convolution, a multi-head attention mechanism and a gating circulation unit, and realizing collaborative extraction of spatio-temporal features. According to the method, the problems of sparse target features, noise sensitivity and poor universality in traditional millimeter wave radar attitude estimation are solved.

The invention belongs to the technical field of power equipment signal processing, and particularly relates to a multi-channel signal reverberation suppression method based on phase weighted cross-correlation optimization, which comprises the following steps: S1, acquiring an original multi-channel time domain signal matrix; s2, estimating propagation time delay of each channel relative to the reference channel; s3, enabling all channels to realize time domain alignment in a direct sound main energy section; s4, processing to obtain a multi-channel time-frequency domain signal; s5, performing reverberation suppression by adopting a WPE algorithm to obtain a multi-channel time-frequency domain signal after reverberation removal; before the WPE algorithm is executed, through a Bayesian optimization method based on a Gaussian process, a prediction order and prediction delay parameter combination enabling the SI-SNR of the multi-channel dereverberation signal to be maximum is searched, and the prediction order and prediction delay parameter combination is applied to the WPE algorithm; s6, reconstructing the multi-channel time-frequency domain signal after reverberation removal into a time domain signal; and S7, calculating a normalization coefficient, and performing amplitude scaling. According to the method, the dereverberation ultrasonic signal with high fidelity, high consistency and high robustness can be obtained in a complex reverberation environment.

The invention relates to the technical field of signal processing, in particular to a radar antenna signal processing method and system, and the method comprises the following steps: collecting an antenna time domain echo extraction channel energy curve, calculating the initial phase attenuation curvature of a period length energy peak value, and building an interference characteristic parameter set; matching real-time echoes, judging homology, eliminating interference, extracting main echoes, extracting main peak position spacing through frequency spectrum transformation, calculating a judgment threshold, comparing, and judging and executing decoupling operation to obtain target frequency spectrum data. According to the method, by extracting Doppler channel coherent energy distribution and establishing a periodic interference characteristic parameter set, the intrinsic characteristics of periodic interference can be accurately described, rapid judgment and effective stripping of interference source homology are achieved, and by analyzing the Doppler center frequency change trend and generating a pulse delay compensation quantized value, the interference source homology can be quickly judged and effectively stripped. The stable target identification and frequency decoupling operation under the interference background is realized, and the target identification and frequency control level in the variable environment is obviously enhanced.

The invention provides a data-driven saline screw compressor unit dynamic modeling method, and belongs to the technical field of industrial intelligence and predictive maintenance. According to the method, adaptive deep denoising of original data is realized through a composite signal processing flow of fusing variational mode decomposition, permutation entropy criterion and wavelet packet transformation optimal threshold denoising; meanwhile, a sectional sampling strategy is introduced to enhance the diversity of training data. Then, a wavelet multi-scale energy entropy extraction layer is used for constructing a high-information-density feature vector; furthermore, a prediction model formed by multiple layers of stacked long and short-term memory network units is adopted, and the complex time sequence dependency relationship of the system is deeply captured. According to the method, pure and stable system dynamic representation can be extracted from high-noise industrial data, the dynamic characteristics of the system in the full working condition range are accurately described, and it is ensured that the prediction result is self-consistent physically and reliable in engineering, so that the prediction precision and generalization performance of the model are remarkably improved.

The invention discloses a traction stretching motion state monitoring method based on signal processing, and relates to the technical field of signal processing, and the method comprises the following steps: collecting traction force, electrocardio, displacement and conductivity signals, carrying out normalization and filtering processing, extracting phase and amplitude components, and constructing a signal spectrum containing track change and time sequence characteristics; establishing a structure response mapping relation to generate a self-evolution feature map, and aligning the self-evolution feature map with the amplitude component to form a channel-level feature matrix and a multi-channel feature representation map; structural path perception convolution mapping is adopted, a path dissipation factor is introduced to extract deep features, direction enhancement is achieved in combination with an energy constraint factor and a scheduling matrix, and an energy adjustment feature map is obtained; finally, the features are spliced according to time steps to form a high-resolution sequence, signal optimization is achieved through iterative training, and the state recognition accuracy and stability are improved.

The invention provides a micro-seismic P-wave first arrival pickup method based on deep learning and an adaptive time window, and the method comprises the steps: (1) collecting and preprocessing a micro-seismic signal, making a data set, and dividing the data set into a training set and a test set; (2) building an MSC-NET deep learning neural network, taking a data set as input for training, and adjusting model parameters to achieve an optimal model effect; (3) performing a micro-seismic signal classification task by applying the trained deep learning model, and dividing a large number of micro-seismic signals into an effective available class and an ineffective unavailable class; and (4) finally, carrying out P-wave first arrival time pickup on the effective micro-seismic signals based on an energy ratio algorithm of an adaptive time window to obtain a relatively reliable pickup result. According to the method, the deep learning technology and the improved traditional signal processing technology are combined, the reliability and accuracy of the automatic pickup result of the micro-seismic signal P wave first arrival time are greatly improved, and meanwhile the method has the advantages of being high in robustness and applicability and the like.

The invention discloses a time-frequency analysis method based on local entropy optimization synchronous demodulation reassignment transformation, relates to the technical field of signal processing, and solves the problem that a traditional time-frequency analysis method is not easy to carry out when complex non-stable vibration signals generated by rotating machinery and the like are processed. The problems of difficulty in balancing time and frequency resolution, easiness in energy diffusion, difficulty in multi-component signal separation and the like exist; according to the method, zero padding is carried out on the two ends of a target signal, a discrete Gaussian window is constructed to generate a sliding windowing signal matrix, a projection angle sequence is generated in an angle space, a time axis and a frequency axis are discretized, a demodulation function containing undetermined parameters is constructed to generate a complex number time-frequency representation matrix, the Renyi entropy value of time-frequency sub-blocks is calculated to determine an optimal demodulation parameter, and the optimal demodulation parameter is obtained. And setting a frequency difference threshold to construct a frequency redistribution operator to generate an optimized time-frequency matrix. The method is mainly used for time-frequency analysis of complex non-stationary vibration signals in the fields of rotating machinery and the like, and high-precision frequency modulation feature extraction and enhancement processing are achieved.

The invention discloses a very high frequency broadband radiation source positioning method based on energy weighted subspace fitting, and relates to the technical field of signal processing and radiation source imaging, and the method comprises the steps: carrying out the DFT processing of a lightning very high frequency broadband signal collected by an array, and obtaining any narrowband component; then characteristic decomposition is carried out on covariance matrixes of X-axis and Y-axis sub-arrays according to array receiving signals, main characteristic vectors are taken as signal sub-spaces, in order to emphasize the signal sub-spaces with higher energy, characteristic values are used for weighting, weighted signal sub-spaces are constructed, then the signal sub-spaces are fitted by using a least square method, and the signal sub-spaces with higher energy are obtained; the method comprises the following steps: further constructing a sub-band energy weighted spatial spectrum adapted to a very high frequency broadband signal, then carrying out spectrum peak search on the weighted spatial spectrum to obtain a spatial angle of a spectrum peak, and finally calculating the direction of arrival of a radiation source according to a spatial angle relation to obtain an azimuth angle and an elevation angle. According to the invention, aiming at a lightning very-high-frequency weak radiation source positioning problem, a lightning channel can be inverted more clearly.

The application discloses a high-efficiency target distance migration compensation method based on Chirp-Z transformation and belongs to the field of radar target detection signal processing.The application realizes the method as follows: FFT is conducted on a reference signal and an echo signal along a fast time dimension, the fast time time domain is transformed into a frequency domain, and the echo signal is pulse compressed by conjugate multiplication, so that target echo energy is more concentrated in the fast time dimension; a Doppler ambiguity compensation function is transformed from an integer form into a fractional form, so that a compensation Doppler frequency interval is changed from a fixed value into a value that can be changed as required; a CZT formula with the fractional Doppler ambiguity number compensation function is used to transform the pulse-compressed signal, target distance migration is compensated, and the efficiency of target distance migration compensation is improved; IFFT is conducted on the fast time dimension of the signal, so that the fast time frequency domain is changed back into a time domain; and two-dimensional data containing target distance information and Doppler information are detected, so that distance and speed information of the target is obtained.

The invention discloses a memory alloy part durability experiment data analysis method and system, relates to the technical field of data processing, and aims to solve the problem of poor alloy performance detection effect caused by environmental noise interference and signal scattering of existing memory alloy durability experiment data. A differential signal processing strategy is adopted to carry out noise reduction optimization on multi-dimensional performance response data, characteristic factors representing deformation recovery, residual deformation and energy dissipation are extracted and corrected, and combined logic of the characteristic factors is dynamically judged by combining a recognition rule set based on an application scene so as to determine a specific performance degradation mode. Corresponding residual use cycle prediction models are constructed according to different degradation modes, the credibility range is calculated, finally, a performance optimization strategy is generated through matching, and full-process closed-loop analysis from data preprocessing and feature extraction to degradation mode recognition, service life prediction and strategy generation is achieved. And the performance prediction precision and the reliability evaluation efficiency of the memory alloy part under the complex working condition are remarkably improved.

The invention provides a runner chamber wall thickness and void synchronous detection method, and relates to the technical field of hydro-power engineering nondestructive testing, S1, modular calibration: preparing a combined simulation block of a metal standard plate and an organic material plate, establishing a concrete water content inversion model and a void depth model segmented according to the thickness of a steel lining, and outputting calibration parameters; s2, field data acquisition: arranging measuring lines along a vertical welding seam and a longitudinal rib plate of the runner chamber, acquiring ultrasonic echo time and a thermal neutron counting rate, correcting the wall thickness in combination with the temperature of a steel lining, performing secondary verification on an abnormal measuring point, and outputting an original data set S = {}; and S3, machine learning signal processing: performing wavelet packet decomposition to extract energy ratio characteristics, and calculating thermal neutron counting rate fluctuation characteristics. Precise collaborative detection of the wall thickness and the void is realized through a link-by-link process of modular calibration, field data acquisition, machine learning signal processing, segmented collaborative modeling and Internet of Things data management.

The application belongs to the technical field of radar signal processing, and particularly relates to a multi-target DDMA demodulation method based on sub-band unit accumulation. Firstly, initial parameters of a radar system are initialized, then a DDMA-MIMO signal model is constructed according to the system parameters, and then 2D FFT processing of distance dimension and Doppler dimension is performed on the received signals. Then, the multi-target DDMA demodulation method based on sub-band unit accumulation is used for demodulation, and the characteristics are that under each angle domain, the whole RV-MAP is copied, the energy of each unit cell of all sub-bands is recorded, and energy accumulation of cyclic shift with a length of the number of transmitting antennas is performed to obtain the sub-band group energy of each unit. Then, the maximum accumulated energy of each unit sub-band group is found, threshold judgment is performed according to the energy to obtain the correct sub-band where the target is located. Finally, the real distance and speed of the target are calculated according to the distance formula and the speed formula. The method provided by the application can well process the case that the traditional method cannot be applied to the multi-target scene.

This invention discloses an improved method for generating driver fatigue EEG data using a latent diffusion model, belonging to the field of EEG signal processing. The method includes: processing the original multi-channel driver fatigue EEG signal through multi-scale wavelet denoising, adaptive thresholding, and an improved soft thresholding function; then performing inter-channel covariance alignment and whitening to eliminate redundancy to obtain a preprocessed signal; performing short-time Fourier transform and logarithmic energy normalization to obtain normalized time-frequency features; inputting the time-frequency features into an encoder to obtain the mean and variance of latent variables, and obtaining latent variables through reparameterized sampling; reconstructing the time-frequency features using a decoder combined with fatigue state labels; training a conditional variational autoencoder by minimizing reconstruction loss and KL divergence loss; adding noise through forward diffusion in the latent space; training a denoising network to remove noise based on fatigue state labels during reverse denoising; inputting the denoised latent variables into the decoder, combining them with a specified fatigue state to generate new time-frequency features, and reconstructing them into the target EEG signal. This invention can enhance the training dataset and improve the accuracy of driver fatigue monitoring.

The application provides a method for trajectory midcourse target recognition based on a low-data-rate HRRP sequence, and relates to the technical field of radar signal processing.The method obtains target signals through steps one and two, and inputs the HRRP sequence obtained by observing the target through a radar sensor as sample data; the method extracts features of the target through steps three and four: the method establishes a precession model by taking a cone-cylinder target as an example, uses the periodicity of scattering center points in the HRRP sequence to perform time-frequency analysis, and realizes one-dimensional feature distribution of energy strength by using a projection method, so as to extract a precession frequency; and the method extracts target structure features, entropy value features and period features; the method performs signal feature fusion processing through step five, classifies the features by performing feature importance analysis and using a Random Forest algorithm, obtains the category of a target to be recognized, and outputs a final fusion result.

The invention provides a roadbed compaction quality real-time evaluation method and system for multi-domain signal processing, and relates to the technical field of roadbed compaction detection, and the method comprises the steps: obtaining a vibration wheel acceleration signal, a roadbed vibration signal and a noise signal; obtaining a vibration wheel excitation signal according to the vibration wheel acceleration signal and the noise signal; according to the vibration wheel excitation signal and the roadbed vibration signal after noise reduction, obtaining a wave velocity dispersion characteristic and an attenuation coefficient; obtaining an energy-compactness quantitative relation according to the wave velocity dispersion characteristic and the attenuation coefficient; generating a depth feature vector according to a wave velocity dispersion characteristic and an energy-compactness quantitative relation; and outputting a real-time compaction degree evaluation result according to the depth feature vector. According to the invention, three-dimensional space global continuous sensing of a vibration wave field is realized through the hexagonal geophone array; a time-frequency-space three-domain cascade noise reduction framework is combined, so that the purity of a vibration signal is improved; based on an online incremental learning mechanism, adaptive modeling during working condition change is realized.

The invention relates to the technical field of ship communication signal processing, and discloses a ship communication signal quality detection method for automatic detection. The method comprises the following steps: hierarchically decomposing a captured original communication signal to obtain a discrete signal data unit group; constructing a spatial distribution map of the signals through topological mapping of the vector features, and identifying a main signal core area according to a density gradient and a vector field streamline in the map; tracking a signal energy diffusion path along a vector field streamline, positioning discontinuous nodes on the path, and aggregating to form a suspected interference signal cluster according to the discontinuous nodes; performing morphological consistency verification on the signal clusters, and separating interference components and effective signal components from the atlas; the effective signal components are recombined to reconstruct a communication waveform, and quality measurement and analysis are performed based on the waveform to generate a quantized quality report. According to the invention, accurate positioning and separation of interference components can be realized, and objectivity and accuracy of ship communication signal quality evaluation are improved.

The application provides an electromagnetic signal recognition method and system based on energy entropy template matching, relates to the technical field of electromagnetic signal processing, and solves the technical problems that the existing technology has high calculation complexity of electromagnetic signal feature extraction, low template matching efficiency and difficulty in realizing accurate positioning of long-time signals. The method specifically comprises the following steps: acquiring time domain data of an effective duration stage of a known category electromagnetic signal; generating a time domain energy entropy sequence of the known category electromagnetic signal based on the time domain data, as a signal template; storing the signal template and constructing a signal template library; acquiring a global time domain energy entropy sequence of a to-be-detected electromagnetic signal; determining a target signal template from the signal template library; and determining a recognition result of the to-be-detected electromagnetic signal by sliding window correlation matching based on the global time domain energy entropy sequence and the target signal template. The application is used for recognizing electromagnetic signals.

This invention belongs to the field of radar signal processing and target detection technology, specifically relating to a statistical scattering method for clutter particles in radar detection. The method includes: acquiring single scattering parameters and simulation control parameters of the clutter particles; dynamically determining the simulation space length based on the single extinction coefficient and asymmetry factor; initializing a quasi-Monte Carlo low-discrepancy sequence; after initializing each photon packet, randomly generating a propagation step size and updating the cumulative propagation step size; when the photon packet does not exceed the boundary, determining whether scattering has occurred based on the single extinction coefficient and cumulative propagation step size; if scattering has occurred, updating the scattering direction based on the Henyey-Greenstein phase function and attenuating the energy weight; employing an adaptive dynamic threshold roulette wheel mechanism to dynamically adjust the survival probability based on scattering efficiency, photon position, and propagation direction, and maintaining statistical unbiasedness through energy weight compensation; after traversing all photon packets, calculating the total transmitted energy and total reflected energy, and calculating the equivalent extinction coefficient and radar cross section. This invention significantly improves simulation efficiency while ensuring statistical accuracy through physically guided importance sampling and a quasi-Monte Carlo low-discrepancy sequence, providing reliable support for radar anti-environmental clutter interference design.

In an analysis system (100), a detector (50) is configured to detect the count of electrons with respect to each of a plurality of channels respectively corresponding to a plurality of energy bands. With respect to the count of each of the plurality of channels, a processing device (60) calculates a correction value by performing a first correction using first dead time corresponding to all of the plurality of channels and a second correction using second dead time corresponding to each of the plurality of channels, and generates an energy spectrum on the basis of the correction value of each of the plurality of channels.

The invention relates to the technical field of state monitoring and fault diagnosis of ship power devices, and provides a noise data processing method based on cavitation flow characteristics of a propeller. According to the method, observable dynamic frequencies such as leaf frequency and cavitation bubble falling main frequency and statistical non-stationary quantities such as a frequency spectrum broadening factor, self-correlation time, broadband energy and kurtosis are unified in the same analysis framework, a mapping model of a cavitation state and signal processing parameters is constructed, analysis parameters such as FFT, STFT and CWT are adaptively configured, and the analysis parameters of the cavitation state and the signal processing parameters are analyzed. And multi-scale feature extraction of non-stationary pressure pulsation signals under different cavitation types and cavitation intensities is realized. According to the method, the cavitation state and type are identified and evaluated on the basis of the extracted multi-domain features, the threshold and mapping parameters are reversely updated according to the identification result, a closed loop of state judgment, parameter mapping, multi-domain analysis, feature output, cavitation identification and parameter updating is formed, and the precision and engineering applicability of propeller cavitation monitoring and diagnosis are improved.

The present application relates to a kind of screening machine vibration signal extraction methods based on multi-algorithm fusion, belong to the signal processing technical field of mining screening equipment, solve the problem of modal aliasing, distortion of signal decomposition in prior art. Including: collecting vibration signal;Through the SVMD algorithm after the AE-CMA-ES algorithm optimization parameter, vibration signal is decomposed into IMF modal component;The energy distribution of each IMF modal component is obtained by HHT transformation, and then the high-frequency energy proportion and instantaneous acceleration peak value are obtained. Precise decomposition of vibration signal is realized.

The application belongs to the technical field of radar signal processing, and specifically discloses a method for detecting airspeed targets based on long-time accumulation, which comprises the following steps: firstly, segmenting the echo signals after pulse compression according to the number of pulses, and compensating the distance migration and Doppler spread of the envelope of the echo signals in each segment; secondly, performing in-segment coherent accumulation on the compensated results in each segment; and finally, performing envelope migration and non-coherent accumulation between segments on all the echo signals, so that the energy of the echo signals can be effectively accumulated. The long-time accumulation method of time segmentation, in-segment coherent accumulation and inter-segment non-coherent accumulation can solve the problems of long-time coherent accumulation calculation and echo signal coherent deterioration, improve the detection performance, and is easy to implement in engineering.

The application discloses an NPU native Bartlett beam forming method for MIMO radar and belongs to the technical field of radar signal processing.The baseband echo data of the MIMO radar is constructed as a four-dimensional input real number tensor, and a Bartlett steering vector is constructed as a six-dimensional weight real number tensor, which are both stored in an interlaced format of real and imaginary parts; based on an NPU parallel multiply-accumulate array and a vector processing unit, equivalent complex matrix multiplication operation and element-by-element square and scaling operation are performed in a real number domain to obtain a four-dimensional output real number tensor with real and imaginary parts in the same sequence; after inverse transformation to restore the array structure, coherent accumulation is performed along a preset beam pointing dimension to output a power spectrum vector of K beam channels, and target azimuth estimation is realized by searching for a power spectrum peak value.The application deeply couples the NPU architecture characteristics and the essence of the Bartlett algorithm, reduces the processing delay by one order of magnitude, significantly improves the real-time performance and energy efficiency ratio while retaining the complete angle resolution and anti-interference performance, and is suitable for edge-side deployment in a resource-limited environment.

The application provides an anti-interference acoustic signal data extraction method based on energy data sensing, and belongs to the technical field of underwater acoustic signal processing. The method comprises the following steps: A / D sampling and digital quadrature demodulation are performed on a received acoustic signal to obtain a baseband signal; low-pass filtering and down-sampling are performed on the baseband signal to obtain a pretreated signal; the pretreated signal is matched filtered by using a reference signal; a key improvement lies in that a local energy estimation function of the pretreated signal is calculated, and the matched filtering result is normalized with the energy estimation function to obtain a normalized detection signal. Through energy sensing normalization, the application effectively suppresses the shielding effect of strong interference signals on weak target signals due to non-quadrature reference signals and the "near-end strong and far-end weak" scene, significantly reduces the false recognition probability, and improves the accuracy and reliability of extracting a long-distance and low signal-to-noise ratio target beacon signal in a complex underwater acoustic environment. The application is suitable for underwater navigation, positioning and communication systems.

The application discloses a y rock small fault feature extraction method based on wavelet transform and principal component analysis, and belongs to the technical field of seismic signal processing. The method comprises the following steps: pre-processing obtained seismic data, performing multi-scale decomposition on the seismic signal by using discrete wavelet transform; calculating the energy proportion of each decomposition level, selecting the level with the highest energy proportion and the approximate component to perform signal reconstruction, so as to enhance the effective signal; extracting various seismic attributes of the reconstructed seismic data and constructing an attribute set; and performing dimension reduction and fusion on the attribute set by using principal component analysis, so as to obtain a principal component feature vector. The application can effectively improve the signal-to-noise ratio of the seismic signal, eliminate attribute redundancy, significantly enhance the small fault response feature and improve the recognition accuracy.

The application belongs to the technical field of millimeter wave radar target detection and signal processing, and specifically discloses a millimeter wave radar multipath target suppression method in a tunnel environment. The method establishes a geometric position correlation relationship between a real target and each order of multipath targets in a range-Doppler diagram by using millimeter wave radar echo signals in the tunnel. On this basis, a candidate signal point set meeting a geometric constraint condition is subjected to coherence analysis and energy consistency verification in sequence, so that the real target echo and the multipath interference signal are effectively distinguished, and the discrimination accuracy of the multipath signal is significantly improved. At the same time, after distinguishing the real target and the multipath signal, the spatial position and amplitude information of the identified multipath signal point are used to construct a multipath signal space distribution estimation diagram. According to the diagram and in accordance with a reduction ratio, the multipath signal component is removed from the original range-Doppler diagram, so that the multipath interference is directly suppressed in the signal processing stage.

The invention discloses a motion compensation imaging method based on Radon transformation and an SWO algorithm, and belongs to the technical field of laser radar signal processing. The method comprises the following steps: firstly, carrying out dechirp receiving and range pulse compression on an ISAL echo signal; detecting an energy peak value by using Radon transformation, and roughly estimating a target radial speed; constructing a local constraint search space based on the rough estimation speed; in the constraint space, the SWO algorithm is utilized, the image contrast is used as a fitness function, and speed and acceleration parameters are finely searched; and finally, constructing a full-aperture phase compensation function by using the optimal parameters for imaging. The problems that a traditional cross-correlation method is low in precision, and a direct optimization method is slow in iteration and prone to falling into local extremum are solved, and the imaging quality and the algorithm efficiency are remarkably improved.

The invention relates to the field of radar signal processing, in particular to a detection integrated waveform design method based on information receiving and transmitting interaction, which comprises the following steps of: acquiring target characteristics, and estimating or predicting the target characteristics at the current moment; the method comprises the following steps: pre-modulating an original transmitting signal according to target characteristics, and designing a pre-coded transmitting signal; transmitting and receiving the pre-coded transmitting signal; and detecting an original emission signal by adopting a matched filter. According to the method, the target scattering characteristics are acquired in advance, the signals are actively subjected to pre-compensation at the transmitting end, and the transmitted signals are subjected to pre-coding customization design, so that adverse effects caused by target multi-scattering-point interference can be actively counteracted, and the problems of echo energy attenuation and dispersion caused by target multi-scattering-point interference in the prior art are solved.

A speed pulse recognition method based on zero-crossing closing interval and decoupling criterion belongs to the field of earthquake engineering and seismic signal processing, and comprises the following steps: step 1, speed time history acquisition and pretreatment; step 2, candidate interval of closing pulse is constructed; step 3, energy calculation and candidate screening; step 4, global energy concentration window construction and screening: cumulative energy curve is constructed, and consistent constraint screening is carried out based on the global energy concentration window; step 5, adjacent interval continuity discrimination and boundary marking: the continuity of adjacent intervals is discriminated, and the decoupling and to-be-merged boundary are marked; step 6, merging and reorganization iteration and pulse group generation; step 7, energy concentration review and determination output: the concentration is reviewed, and the pulse type and key parameters are output. And a speed pulse recognition device based on zero-crossing closing interval and decoupling criterion is provided. The present application improves the reliability and engineering applicability of the pulse recognition result under complex seismic motion conditions.