1505 results about "Center frequency" patented technology

The invention provides a multi-band radio frequency power amplifier dynamic range adaptive test method and a system thereof. The method comprises the following steps: acquiring an output signal frequency spectrum of a tested radio frequency power amplifier, extracting characteristic frequency points including a center frequency and a bandwidth, matching the characteristic frequency points with a preset frequency band database, and automatically loading a test scheme of a corresponding frequency band. According to the method, the frequency bands are automatically matched through spectrum sensing, the test scheme is loaded, the multi-frequency-band characteristics can be rapidly adapted without manual parameter configuration, index missing test is avoided, and the switching efficiency is improved; dynamically optimizing test parameters by using a genetic algorithm, and accurately capturing a nonlinear distortion critical point while balancing coverage and efficiency; by combining multi-dimensional signal analysis and a deep learning model, the problem of missing detection of traditional single-domain analysis is solved, and the abnormal signal recognition capability is remarkably enhanced; through linear model correction, impedance dynamic matching and index threshold iteration, full automation and high precision of the test are realized, the environmental interference influence is effectively reduced, and a self-adaptive test system is constructed.

The present invention relates to the technical field of power source power allocation. Disclosed are a direct current power source power allocation method and system for a generator status monitoring apparatus. The method comprises the following steps: by means of system data, constructing a variational problem model; solving the constructed variational problem model, and using a particle swarm optimization algorithm to optimize computing parameters of the variational problem model; and, on the basis of a computing result of the variational problem model, allocating the total output power of a hybrid energy storage system to an energy-type storage device and a power-type storage device according to a ratio. By means of using the convergence-guaranteed particle swarm optimization algorithm, the present invention not only excels in the solving speed but also shows significant advantages in computational accuracy; furthermore, by means of solving the variational problem model, more accurate allocation ratios are obtained; variational mode decomposition can achieve adaptive matching of the optimal center frequency and bandwidth for each mode, thus effectively separating intrinsic mode components and achieving frequency domain partitioning of signals.

The invention relates to the field of electric power material warehouse management and control, in particular to an electric power material digital intelligent professional warehouse management and control method, which comprises the following steps: acquiring electromagnetic spectrum data, identifying a central frequency point energy distribution diagram and an electromagnetic interference intensity level, constructing a three-dimensional electromagnetic field intensity distribution model of a professional warehouse, and dynamically adjusting the working mode of an RFID reader-writer; the method comprises the following steps: deploying a multi-modal check node in a storage area of a professional warehouse, and generating a composite check result through an infrared contour matching method and a UWB coordinate verification method when the confidence coefficient of electric power material data read by an RFID reader-writer is lower than a preset confidence coefficient threshold value; updating an electric power material inventory database according to a composite verification result, and performing electric power material inventory state analysis in real time through an edge computing node; the problems that inventory data distortion and material positioning deviation are caused by reduction of the accuracy rate of RFID in a strong electromagnetic environment, and inventory accuracy management of an electric power material warehouse is seriously restricted are solved.

The invention belongs to the technical field of radar signal processing and target identification, and particularly relates to a maritime hybrid target identification method and system based on modal decomposition and reconstruction, and the method comprises the steps: carrying out the variational modal decomposition of a radar echo signal of a maritime hybrid target, and decomposing an original signal into a plurality of intrinsic modal signals; estimating a background noise energy reference and removing noise modals based on the decomposed modal signals, clustering the remaining modals according to the center frequency, and combining the modals with the frequency within the center frequency range into an independent single-target signal which is of the same target and is reconstructed into an independent single-target signal; respectively carrying out time-frequency analysis on each reconstructed single target signal to obtain a corresponding time-frequency diagram, and extracting time-frequency domain features from the time-frequency diagram; and inputting the extracted features into a trained support vector machine classifier to realize automatic identification of the ship target and the floating target. A ship target and a floating target can be distinguished more accurately in a mixture scene, and the stability of tracking identification is improved.

The present invention discloses a neural network accelerated simulation method for the radar scattering field of a wide-band target group, belonging to the technical field of electromagnetic simulation. The method of the present invention constructs a KAN neural network model at the central frequency of the wide band, and constructs KAN neural network models at other target frequencies through transfer learning or direct training; uses the trained KAN neural network model to generate a multi-frequency point numerical Green's function data set, and then constructs a wide-band training sample to train the multi-task learning model to realize the global prediction of the numerical Green's function of the entire target wide band; uses the numerical Green's function output by the multi-task learning model to realize the fast simulation of the scattering field of the wide-band target group. The method of the present invention can realize the fast and accurate simulation of the radar scattering field of the wide-band target group.

The invention provides a bridge dynamic displacement measurement method based on an unmanned aerial vehicle, and relates to the field of bridge health monitoring, and the method comprises the steps: obtaining a bridge image sequence through the unmanned aerial vehicle; obtaining transformation matrixes according to the relation between the other frames of images and the fixed measurement points of the reference frame, correcting the coordinates of the motion measurement points of the corresponding frames of images according to the transformation matrixes, obtaining a corrected bridge image sequence, and further obtaining an initial displacement signal; variational mode decomposition is carried out on the initial displacement signal, a constraint variational optimization problem is constructed, the optimization problem is solved through iteration to obtain each mode component of variational mode decomposition and a corresponding center frequency, and a low-frequency mode component is removed to obtain a displacement signal after secondary correction of the influence of the unmanned aerial vehicle; and obtaining the real displacement according to the scale factor between the image obtained by the unmanned aerial vehicle and the real world. The influence caused by the movement of the unmanned aerial vehicle is corrected, and the accuracy, reliability and stability of a measurement result are effectively improved.

Disclosed are techniques for wireless communication. In an aspect, a network node receives, from a network entity, a configuration for reporting carrier phase measurements of one or more reference signal resources spanning a plurality of subcarriers, wherein the configuration indicates one or more center frequencies to use for reporting the carrier phase measurements, obtains one or more carrier phase measurements of the one or more reference signal resources based on the one or more center frequencies, and reports, to the network entity, the one or more carrier phase measurements.

The invention discloses a multipath interference suppression method and device during in-pipe detection and a storage medium, and belongs to the technical field of radar signal processing. According to the method, a CFAR detection and clustering algorithm combined method is adopted, and the target number and the center frequency are automatically extracted from a radar amplitude-frequency signal; an original noisy signal is decomposed into a target number of modal components by using an improved variational modal model, and an improved power reference analysis method is proposed to divide the modal components into effective modals and ineffective modals; sVD noise reduction is carried out on the effective mode; and performing signal reconstruction on the plurality of modal components after SVD noise reduction to obtain a signal after multipath interference suppression. According to the method, the problem that the decomposition number and the center frequency of the variational mode decomposition algorithm are difficult to determine is effectively solved, the influence of inaccurate parameters is reduced, multipath interference suppression during complex environment detection is facilitated, and the effectiveness of the algorithm is verified through actual measurement.

The invention discloses a LoRa-based parallel backscatter communication method and system. The method comprises the following steps: transmitting a single-tone radio frequency source signal as a signal excitation source; designing a backscattering label, and performing backscattering modulation processing on the environmental perception data in combination with the signal excitation source to obtain a modulated standard LoRa data packet; and the receiver presets the center frequency of a communication link, and performs frequency band distinguishing and demodulation processing on the modulated standard LoRa data packet to complete parallel backscatter communication of the LoRa data. According to the invention, while a parallel backscattering LoRa communication system compatible with commercial LoRa equipment is realized, the throughput of the system is improved, and frequency division multiple access parallel transmission is realized. The LoRa-based parallel backscatter communication method and system can be widely applied to the technical field of parallel backscatter communication.

The invention discloses a signal acquisition and processing method and system based on a multifunctional radar, and relates to the technical field of signal acquisition and processing, and the method comprises the steps: employing a quantum genetic algorithm to optimize radar transmission waveform parameters, including center frequency, bandwidth and frequency modulation slope, and generating a nonlinear frequency modulation waveform through FPGA hardware; a non-uniform sparse array is adopted to receive a target echo signal, time domain random interval sampling and frequency domain pseudo-random frequency point selection are synchronously implemented, and time-space-frequency three-dimensional compressed sensing observation data are formed; performing trilinear tensor joint sparse reconstruction on time-space-frequency three-dimensional compressed sensing observation data, decomposing a polarization scattering matrix eigenvalue from a reconstructed signal, and calculating a coupling characteristic quantity of eigenvalue entropy and micro-Doppler frequency; inputting the coupling characteristic quantity into a deep reinforcement learning model, and dynamically outputting a constant false alarm detection threshold, a moving target display filter order and a resource allocation weight; and detecting a threshold based on a constant false alarm rate.

The invention discloses a composite fault signal separation method based on adaptive spectral kurtosis deconvolution. The method comprises the following steps: S1, carrying out noise reduction processing on an original composite fault signal; s2, separating the composite fault signal after noise reduction through Hilbert envelope demodulation; s3, optimizing a low-pass filter and a high-pass filter through a Bayesian objective function, and constructing a band-pass filter and a band elimination filter; and S4, carrying out iterative filtering on the composite fault signal after noise reduction through a band-pass filter and a band elimination filter, constructing a frequency-bandwidth fast spectral kurtosis graph according to a fast spectral kurtosis deconvolution method, obtaining a center frequency and bandwidth range corresponding to the maximum spectral kurtosis, and separating out the weak fault signal in the step S2. According to the method, a spectral kurtosis graph is constructed by combining a low-pass filter and a high-pass filter through a fast spectral kurtosis deconvolution method to determine the center frequency and the bandwidth range, targeted separation is carried out on composite fault signals, an initial filtering parameter range is provided for Bayesian optimization, and the fault separation efficiency and accuracy are improved.

The invention discloses a hydroelectric generating set state online monitoring system and method based on data analysis, and relates to the technical field of data analysis. The method comprises the following steps: setting a training period, extracting a time domain feature vector and a frequency domain feature vector of each data packet, and training to generate a gated attention mechanism model; training and generating a mixed density network model according to the data packet containing the fault state label; according to the real-time fault risk probability, the latent variable representation vector and the real-time center frequency, whether abnormal features are analyzed or not is judged, if the abnormal features are analyzed, the contribution degree of each input feature to the real-time fault risk probability is calculated through a gradient approximation method, and an influence degree score is calculated; and taking the corresponding input features exceeding the influence degree score threshold as abnormal features, and outputting prompt information for operation and maintenance personnel to check, thereby improving dynamic environment response, identification precision degree and reliability of a monitoring system.

The invention belongs to the technical field of metal crack detection, and discloses a metal microcrack ultrasonic detection method, which comprises the following modules: an ultrahigh frequency composite array probe with a center frequency dynamic adjustable range of 5-50 MHz; the dual-mode excitation and receiving module is used for independently generating a pulse reflection mode excitation signal and a diffraction mode chirp frequency modulation signal; the phase-controlled synthetic aperture dynamic focusing module is used for realizing + / -60-degree deflection and sub-wavelength level focusing of acoustic beams; the nonlinear harmonic enhancement module is used for exciting a crack nonlinear effect by superposing 1-2MHz fundamental waves; and a time-space-frequency three-domain data fusion module. According to the method, signal-to-noise ratio collapse caused by the diffraction effect in a traditional method is overcome, more importantly, the system has low-frequency penetrability and high-frequency resolution at the same time due to 80%-120% of broadband coverage of chirp frequency modulation signals, the diffraction signal gain is increased by 20 dB or above, a near-surface blind area is compressed to be within 0.2 mm, and the system can be applied to the field of radar imaging. And the core pain point of missing detection of the near-surface microcracks of the thin-wall component is solved.

The invention discloses a high-precision spectral interference signal demodulation method for micro distance measurement. The method comprises the steps of S1, interference spectrum shaping; measuring an interference spectrum and a reference spectrum; selecting a six-item combined cosine window as a primary function of an interference signal to obtain a shaped interference spectrum; s2, performing non-uniform Fourier transform to obtain a frequency spectrum of the target signal; s3, on the basis of a multi-spectral-line interpolation algorithm, interpolation is carried out on the window function to solve a fitting function, and a center frequency k is obtained; and S4, outputting a result. And solving and outputting an optical path difference d. According to the method, a six-item cosine combination window is selected as a primary function of an interference signal, and the problem that the accuracy of signal extraction is influenced by mutual superposition of side lobes and even main lobes of a direct current item and a solved frequency item is solved; an interference spectrum is processed by using a fast non-uniform Fourier transform algorithm, accuracy can still be kept under the conditions that the distance to be measured is increased and sampling is gradually sparse, and the problem of resampling deviation caused by a traditional interpolation method can be solved.

In a wireless local area network (WLAN) system, a transmission STA can transmit a PPDU via a 320 MHz channel, and a Medium Access Control (MAC) signal may be generated for the PPDU. The MAC signal may include puncturing pattern information and channel center frequency segment (CCFS) information for a 320 MHz band. The CCFS information may include a first CCFS field related to channel center frequency (CCF) information of a primary 160 MHz channel, and a second CCFS field related to CCF information of a 320 MHz channel.

The invention discloses a multi-mode communication and perception channel twinborn AI data set generation method. The method comprises the following steps: establishing a high-precision three-dimensional indoor scene model and an outdoor scene model; configuring parameters of a signal receiving and transmitting end and the RIS, determining dielectric constants and magnetic conductivities of different object surfaces in a scene according to a signal center frequency, and determining reflection, refraction, scattering and diffraction effects of electromagnetic waves in different media based on a geometric optical theory and a geometric diffraction theory; calculating channel parameters of each ray, generating a channel matrix and a channel intensity thermodynamic diagram based on the channel parameters, and generating a complete data set in combination with scene information; and aligning the coordinates of the scene and the ray, loading and rendering ray data and a channel intensity thermodynamic diagram based on a moving path selected by a user, and obtaining a visual file of the multi-modal channel twin AI data set. According to the invention, refined materials and electromagnetic properties are given to objects in a scene, and efficient simulation and second speed visualization of electromagnetic wave propagation are realized.

The invention relates to the technical field of radar waveform optimization, in particular to a synthetic broadband waveform optimization method based on reinforcement learning. According to the method, a deep reinforcement learning framework is adopted, and end-to-end optimization is carried out on the center frequency and amplitude parameters of multiple sub-pulses. The method comprises the following steps: constructing a synthetic broadband waveform signal model, and parameterizing sub-waveforms to determine a search space of an optimization problem; converting a waveform optimization problem into a reinforcement learning form, and defining a state space, an action space, a reward function and a state transition mechanism; based on a deterministic soft action value iteration strategy algorithm, point spread function features are extracted in combination with a Transform encoder, and sub-pulse parameters are dynamically adjusted. Through the method, the main lobe resolution can be effectively suppressed and improved, the target detection performance of a radar system is improved, and the method has good global optimization capability and environmental adaptability and is suitable for cognitive radar and adaptive waveform design scenes.

The invention belongs to the field of filter optimization of coupling matrixes and neural networks, and discloses a filter optimization design method of a neural network based on guidance of a coupling matrix, and physical elements and mechanisms are introduced into the neural network. The method comprises the following steps: firstly, determining filter optimization indexes, randomly generating sample data in a range near the size of the optimization indexes, and screening data samples by adopting a sample screening method to obtain high-quality samples; then, extracting a coupling matrix from the obtained S parameter according to a vector fitting algorithm, and forming new coupling information M'by combining the center frequency f0 and the bandwidth BW; a full-connection neural network is used as a medium of filter device size parameters and output frequency response, the size of a to-be-optimized device of the filter is input, and coupling information M'representing S parameters is output. And the forward simulator is connected with a genetic optimization algorithm GA to realize optimization design of a specific index parameter filter. According to the method, the optimization design time is effectively saved, the optimization efficiency is improved, and the optimization dimension is reduced.

The invention discloses a medium cavity filtering coupler with an adjustable center frequency, particularly relates to the technical field of wireless communication, and solves the problems that in the prior art, the fusion design between a coupler and a filter of the same type is mainly adopted, most designs are low-power planar circuits, and the cost is low. The technical problem of lack of fusion design of an adjustable cavity filter and a coupler is solved. According to the technical scheme, a metal stepped shaft assembly is inserted into a non-metallized through hole of a medium cavity in a segmented mode, disturbance of different magnitudes is generated on a TE110 mode electric field in the medium cavity, and then the resonant frequency of the medium cavity is shifted; the effect of flexibly regulating and controlling the center frequency of the filtering coupler can be achieved.

The invention discloses a method for adaptively identifying the thickness of a black soil layer based on a variational mode decomposition method and a ground penetrating radar technology, and the method can directly measure the thickness of the black soil layer of a large-area black land through the ground penetrating radar technology, automatically filters ground penetrating radar signal clutters, and corrects the ground position. According to the technical scheme, on the basis of a center frequency method and a residual error method, variational mode decomposition parameters suitable for black land ground penetrating radar signals are automatically obtained, then modal components of variational mode decomposition are subjected to counting analysis, a spectrogram of original ground penetrating radar signals and the actual black soil layer thickness are compared, an empirical model based on the peak area proportion is established, and the calculation accuracy is improved. The automatic screening can be used for identifying the post-screening center frequency and frequency sweep width of the thickness of the black soil layer, and the position of the black soil layer is adaptively identified after frequency filtering, so that the thickness of the black soil layer can be quickly obtained, and the identification accuracy and efficiency are improved.

The invention discloses a device and a method for accurately acquiring working face abnormal body information by a multi-frequency ground penetrating radar. The device comprises a multi-frequency data acquisition module, a data alignment module, a data fusion module, a data management module, a model training optimization module and an abnormal body information identification module. The method comprises the following steps: acquiring ground penetrating radar data of different center frequencies of a target by using a multi-frequency ground penetrating radar; preprocessing the original multi-frequency ground penetrating radar data; performing spatial alignment on the preprocessed ground penetrating radar data; fusing the ground penetrating radar data through improved non-subsampled contourlet transform (NSCT) to obtain a multi-frequency ground penetrating radar fusion profile; fused images are collected to make a ground penetrating radar image data set; and importing an improved YOLOv11 model to perform underground anomalous body identification and marking on the ground penetrating radar image data set. Through multi-frequency data complementation and deep learning optimization, anomalous body detection precision and efficiency are significantly improved, and the method is especially suitable for geological exploration of complex underground environments such as a coal mine.

The invention belongs to the field of hydroelectric generating set monitoring, and discloses a set vibration signal noise reduction and feature extraction method based on IAVMD and WOA, and the method comprises the steps: constructing a comprehensive evaluation index comprising an energy difference, an average energy entropy and a center frequency variation coefficient; combining an adaptive variational mode decomposition method and a whale optimization algorithm to obtain an optimal decomposition layer number K and a penalty factor; calculating an energy entropy and a Pearson correlation coefficient of each intrinsic mode component; screening the K intrinsic mode components through a double-threshold mechanism; and calculating to obtain a denoised unit vibration signal time sequence and fault features according to the screened intrinsic mode components. According to the method, the problems of large noise interference, fuzzy characteristics, parameter dependence on experience and the like in a traditional method are solved, the precision and efficiency of unit vibration signal processing are remarkably improved, nonlinear dynamic characteristics in unit vibration signals are captured through multi-scale analysis, and the sensitivity and diagnosis accuracy of early weak faults can be improved.

The invention provides a multi-frequency ground penetrating radar data fusion method based on deep learning. The multi-frequency ground penetrating radar data fusion method comprises the following steps: acquiring multi-frequency ground penetrating radar data by using different center frequency ground penetrating radar devices under the same survey line; performing data preprocessing operation on the acquired multi-frequency ground penetrating radar data; constructing a deep learning model for fusing the multi-frequency ground penetrating radar data, and inputting the multi-frequency data fusion into the fusion model to realize multi-frequency radar data fusion; designing an unsupervised loss function, and training a deep learning model based on the data; and integrating the trained deep learning fusion model into a ground penetrating radar data acquisition system to realize real-time data fusion and processing. Compared with the prior art, the method can integrate the complementary characteristics of the original signals of the multi-frequency radar, and overcomes the limitation and difference of single-frequency data.

The invention discloses a wide-dynamic-range diamond NV color center magnetic sensing method and system, and the method comprises the steps: (1) calibrating the initial resonant frequency and magnetic sensing coefficient of a diamond NV color center in an initial magnetic field environment; (2) in a to-be-measured magnetic field environment, a microwave generator applies frequency-modulated microwaves to the diamond NV color center with the current resonant frequency as the center frequency; demodulating the fluorescence signal in real time by using a phase-locked detection technology to obtain an error signal; calculating the detuning amount of the center frequency in real time according to the error signal and the magnetic sensing coefficient, and further calculating the variable quantity of the magnetic field intensity; meanwhile, the detuning amount is fed back to a microwave generator to track the resonance frequency of the diamond NV color center; and (3) continuously and repeatedly executing the step (2) to realize wide-dynamic-range magnetic field measurement. The method has the advantages of wide dynamic range, high detection capability on strong and weak field mixed signals in a complex application scene and the like while giving consideration to the magnetic sensing sensitivity.

A radar sensor device. The radar sensor device includes at least one radar sensor and a controller which is connected to the at least one radar sensor and is designed to control the process of generating a transmission signal and the process of sampling a received signal of the radar sensor. The transmission signal is a periodically repeated and linearly frequency-modulated signal. The center frequency and / or ramp gradient and / or pulse repetition rate of the transmission signal and / or the number of frequency ramps per measurement cycle and / or pauses between measurement cycles can be adjusted. The controller is designed to detect pulse interference occurring in the sampled received signal and to calculate the frequencies of the transmission causing the interference.

The invention discloses a GIS ultrahigh frequency partial discharge signal feature extraction method and device, and relates to the field of electrical data processing, and the method comprises the steps: employing a center frequency method to optimize the number K of modal components of a VMD algorithm, and obtaining an optimal K value; decomposing the ultrahigh-frequency partial discharge signal by using a VMD algorithm of which the number of mode components is the optimal K value to obtain K intrinsic mode function components; performing different types of entropy calculation on each intrinsic mode function component to obtain values of different types of characteristic variables in each intrinsic mode function component, and constructing a characteristic matrix; and inputting the feature matrix into a dynamic network marker model based on a sample covariance matrix to extract dominant feature variables and key network markers corresponding to the dominant feature variables, calculating correlation quantitative indexes among the dominant feature variables, and constructing the extracted key features based on the key network markers and the correlation quantitative indexes. The problem that the robustness of feature expression is insufficient is solved.

Methods, apparatuses, and computer readable media for basic service set (BSS) channel operation are disclosed. Apparatuses of a station (STA) are disclosed, where the apparatuses comprise processing circuitry configured to decode a first frame, the first frame comprising an extremely-high throughput (EHT) operation information field, the EHT operation information field comprising a channel width subfield and a channel center frequency segment, the channel width subfield indicating a basic service set (BSS) contiguous channel width, and the channel center frequency segment indicating a channel center frequency for the BSS channel width on which the BSS operates in 6 GHz. The processing circuitry further configured to encode for transmission a second frame for transmission on a primary channel within the BSS channel width.

The invention discloses a broadband signal parameter identification method based on wavelet noise reduction and an improved VMD-Prony algorithm, and the method comprises the following steps: carrying out the noise reduction preprocessing of a broadband signal based on a soft threshold wavelet noise reduction method, so as to reduce the interference of the noise to a measurement process, and building an evaluation index according to a Pearson's correlation coefficient and a signal-to-noise ratio for evaluating the noise reduction effect; parameters of a mutual information entropy and an energy entropy optimization VMD algorithm are introduced, the noise-reduced signal is decomposed by using the parameter optimization VMD algorithm, a plurality of modes with center frequencies are decomposed, and the complexity of broadband signal processing is reduced; performing parameter estimation on each mode by using a Prony algorithm, selecting a proper model order p according to the characteristics of the modes, performing numerical calculation by constructing a plurality of functions, obtaining frequency, amplitude and phase information of each mode, and realizing parameter identification of the broadband signal; according to the invention, the problems of noise interference and insufficient identification precision of the broadband oscillation signal in the parameter identification process are solved.

The invention discloses a broadband oscillation suppression method for offshore wind power transmitted through flexible direct current. The broadband oscillation suppression method comprises the steps that a topological structure of an offshore wind power end-to-end flexible direct current system and an impedance model of a flexible direct current transmitting end modular multi-level converter are established; a self-adaptive active damping suppression model comprising a power outer ring low-pass filtering link, a feed-forward cascade band-stop filtering link and a feed-forward additional phase compensation link is constructed, the power outer ring low-pass filtering link and the feed-forward cascade band-stop filtering link continuously operate, and the feed-forward additional phase compensation link is controlled to be input through a switch; monitoring common coupling point voltage harmonic waves in real time, and when it is detected that a risk frequency dominant frequency signal amplitude exceeds a threshold value, closing the switch and dynamically adjusting a center frequency and a damping coefficient of the feedforward additional phase compensation link; and the negative damping characteristic near the resonance point is suppressed through the adjusted feedforward additional phase compensation link.

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.