16 results about "Polarization scattering matrix" patented technology

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 relates to a near space high-speed target identification method and device. The method comprises the following steps: acquiring complete polarization high-resolution range profile data; non-linear feature coding is carried out through a sparse denoising variational auto-encoder model, and depth sparse features are obtained; depth sparse features automatically mined by a sparse denoising variational auto-encoder model in fully-polarized high-resolution range profile data are utilized, and a new thought is provided for solving the near space target recognition problem. According to the full-polarization high-resolution range profile data, determining a polarization scattering matrix of each range unit, performing continuous polarization basis rotation transformation to obtain a plurality of polarization related directional diagrams, and determining polarization rotation domain features; the polarization rotation domain feature is used for target attitude identification, and has good robustness for the attitude change of the target; and the depth sparse feature and the polarization rotation domain feature are subjected to weighted fusion to obtain a fusion feature, and the fusion feature is identified to obtain a target category identification result, so that the robustness and interpretability of the model are improved.

This application relates to a method, apparatus, device, and medium for full polarization compensation of antennas and waveforms. First, the cross-polarization isolation and orthogonal waveform isolation of a simultaneous full polarization measurement radar system are measured and calculated. Then, the radar system is used to detect a target, and an estimate of the target's polarization scattering matrix is ​​obtained based on the echo results. Finally, a coefficient matrix is ​​constructed based on the isolation index, and this coefficient matrix is ​​used to compensate for the estimated polarization scattering matrix to obtain the true value of the target's polarization scattering matrix. By modeling and compensating for the radar system's polarization error, the accuracy of target PSM measurement is improved.

The invention discloses a rotating ellipsoid particle parameter estimation method and system based on dual-station polarization, electronic equipment and a computer storage medium. The method comprises the steps that after single-time dual-station polarization measurement is carried out on rotating ellipsoid particles, the single-time dual-station polarization measurement is carried out on the rotating ellipsoid particles; obtaining a scattering matrix of the rotating ellipsoid particles under the global coordinate system, and material electromagnetic parameters, a first azimuth angle, an incident angle, a second azimuth angle and a scattering angle of the rotating ellipsoid particles; calculating a compensation polarization orientation angle according to the first azimuth angle, the incident angle, the second azimuth angle and the scattering angle; performing polarization rotation on the scattering matrix to determine a polarization scattering matrix; carrying out singular point condition judgment on the polarization scattering matrix; if the singular point condition is met, calculating to obtain the normal direction of the vertical rotating ellipsoid particles; and if the singular point condition is not met, calculating to obtain the size parameter and the three-dimensional orientation parameter of the rotating ellipsoid particle. According to the method, the three-dimensional orientation of the rotating ellipsoid target can be accurately obtained through single measurement of the double-station complete polarization radar.

The present invention discloses a passive polarization calibration body for calibrating a full-polarization measurement radar system. When rotated in azimuth, its polarization scattering matrix at different azimuth angles includes non-depolarized scattering components similar to those of metal spheres, cylinders, and the like, and depolarized scattering components similar to those of right-angle dihedral reflectors. By using a single passive polarization calibration body and measuring its full-polarization scattering data at multiple azimuth rotation angles using a turntable imaging measurement radar, multiple polarization scattering matrix measurement values ​​required for full-polarization calibration of a general non-reciprocal radar system can be obtained, thereby achieving polarization calibration of the measurement radar system. Furthermore, since only a single calibration body needs to be measured using a turntable, measurement uncertainty caused by calibration body replacement during the measurement process is reduced. Furthermore, since no additional mechanical rotating devices such as stepping motors are required, the difficulty of angle alignment and the influence of interfering scattering components are reduced, thereby improving the efficiency and accuracy of polarization calibration measurements.

The application discloses a signal collection processing method and system based on a multifunctional radar, relates to the technical field of signal collection processing, and comprises the following steps: optimizing radar emission waveform parameters by adopting a quantum genetic algorithm, wherein the parameters include a center frequency, a bandwidth and a frequency modulation slope, and a nonlinear frequency modulation waveform is generated by FPGA hardware; receiving target echo signals by adopting a non-uniform sparse array, synchronously implementing time domain random interval sampling and frequency domain pseudo-random frequency point selection, and forming time-space-frequency three-dimensional compressed sensing observation data; performing three-linear tensor joint sparse reconstruction on the time-space-frequency three-dimensional compressed sensing observation data, decomposing polarization scattering matrix eigenvalues from the reconstructed signals, calculating eigenvalue entropy and micro-Doppler frequency coupling characteristic quantities; inputting the coupling characteristic quantities into a deep reinforcement learning model, and dynamically outputting a constant false alarm detection threshold, a moving target display filter order and resource allocation weights; and performing resource allocation based on the constant false alarm detection threshold.

The invention discloses a long sphere target three-dimensional orientation method and system based on dual-station polarization, electronic equipment and a computer storage medium. The method comprises the steps that after single-time double-station polarization measurement is carried out on a long sphere target, a first polarization orientation angle of a transmitting antenna and a second polarization orientation angle of a receiving antenna are obtained through calculation according to an obtained double-station complete polarization scattering matrix and a preset polarization angle extraction algorithm; carrying out singular point condition judgment on the first polarization orientation angle and the second polarization orientation angle; if the singular point condition is met, the normal direction of the vertical long target sphere is calculated according to the transmitting antenna direction and the receiving antenna direction; and if the singular point condition is not met, calculating the three-dimensional orientation of the long sphere target according to the first polarization orientation angle and the second polarization orientation angle. According to the method, the three-dimensional orientation of the long sphere can be quickly and accurately obtained through single-time measurement of the double-station full-polarization radar.

The present application belongs to the technical field of insect radar, and particularly relates to a two-dimensional head orientation inversion method based on MLP-Attention, which comprises the following steps: step one, constructing a data set based on the polarization echo data obtained by a multi-frequency full polarization coherent scattering radar measurement system under the microwave darkroom scene of experimental migratory insects and the polarization echo data collected by a high-resolution multi-frequency full polarization radar under the outside field flying scene; step two, designing an MLP-Attention architecture model constructed by taking a multilayer perceptron MLP as a base, adding a residual link and an attention module Attention, wherein the MLP-Attention architecture model realizes the mapping between the polarization scattering matrix of insects and the head orientation thereof, and completes the high-precision inversion of the head orientation, and the performance of the MLP-Attention architecture model under a low signal-to-noise ratio is particularly outstanding. The present application significantly improves the inversion performance under different noise environments, and provides strong support for improving the radar insect monitoring accuracy and optimizing the decision-making of agricultural pest control.

The invention belongs to the technical field of forest remote sensing, and discloses a P-band SAR polarization characteristic-based forest type identification method, which comprises the following steps of: solving a coherence matrix and a covariance matrix on the basis of a pre-processed polarization scattering matrix, and carrying out wavelength dependence correction on basic radar statistical characteristics for a forest three-layer vertical superposition scattering structure formed by a P band; extracting feature subsets of a scattering mechanism related to a forest vertical structure, integrating the corrected basic features and adding polarization phase difference and scattering mechanism combination coefficient feature dimensions to form a complete polarization feature set covering multiple types of features, and extracting various terrain parameters of a target forest region based on DEM data to obtain a complete polarization feature set covering multiple types of features; performing terrain sensitivity analysis on the preliminarily screened feature subset and dividing sensitivity grades, constructing an adaptive terrain correction model in combination with P-band under-forest surface scattering proportion characteristics, and introducing a radar wave polarization state correction term to complete feature radiation normalization correction.

The invention discloses a multi-modal fusion-based fan blade corrosion evaluation and self-repairing method, and belongs to the technical field of fan blade state monitoring, and the method comprises the following steps: a microwave radar collects a polarization scattering matrix and meteorological data of a blade surface in real time; calculating a real corrosion index through a polarization-meteorological adaptive compensation algorithm; extracting residual error characteristics of the SCADA power curve; fusing the corrosion index and the residual feature by using a gated attention network; and generating a repair decision based on a deep reinforcement learning DRL model. According to the fan blade corrosion evaluation and self-repairing method based on multi-modal fusion, the influence of rain and fog weather on radar detection is overcome through the polarization scattering compensation model, and the misjudgment rate is reduced; fusing multi-source data to quantify the corrosion depth of the blade, and controlling a detection error; meanwhile, the repair opportunity is intelligently decided based on parameters such as dynamic electricity price and wind speed prediction, excessive maintenance is reduced, and the repair economy is optimized.

The embodiment of the invention discloses a method for monitoring migratory flying pests across a scattering region, and relates to the technical field of migratory flying pest monitoring, and the method comprises the steps: obtaining complete polarization scattering matrix data of migratory flying pest targets collected by a complete polarization radar, constructing a fusion feature set based on the complete polarization scattering matrix data, and obtaining a fusion feature set; at least two specific polarization directional diagram feature parameters are screened in the fusion feature set, and clustering features are determined; determining class group affiliation information of migratory fly pest targets through the clustering features so as to determine a corresponding first insect body type parameter prediction model and / or an insect body type parameter prediction model group in a preset insect body type parameter prediction model set, the class group affiliation information comprises a first membership degree belonging to a resonance region class group and a second membership degree belonging to a Rayleigh region class group; and determining a corresponding body shape parameter estimation value through the first insect body shape parameter prediction model and / or the insect body shape parameter prediction model group, and monitoring the migratory fly pest target according to the body shape parameter estimation value.

The invention provides a design method and device for a double-satellite-following low-ambiguity mixed polarized SAR system, and belongs to the technical field of radars, and the method comprises the steps: transmitting orthogonal circularly polarized waves through two SAR satellites, carrying out the collaborative observation of the same scene, obtaining the polarization information of a static target, and forming a double-satellite-following distributed mixed polarized SAR system; carrying out registration and phase synchronization on a double-star image in the distributed hybrid polarimetric SAR system followed by double satellites to obtain hybrid polarimetric SAR data; and reconstructing a low-ambiguity complete polarization scattering matrix based on the mixed polarization SAR data. According to the invention, the application potential of the polarimetric SAR system is improved, and the defects of low observation breadth, low resolution and low observation range of a complete polarization mode are overcome.

The application provides a full polarization scattering feature extraction method and device and computing equipment, the method comprising: acquiring an ISAR image of a target to be identified under a preset azimuth central angle; wherein the ISAR image comprises sub-images corresponding to four polarization states; for each group of ISAR images, performing: scattering center extraction is performed on the sub-images in the ISAR image, and at least two amplitude values of scattering centers are obtained; according to the amplitude values extracted from each group of ISAR images, an amplitude matrix corresponding to each scattering center is determined; wherein the amplitude matrix comprises the amplitude value of the scattering center under the preset azimuth central angle; and according to the amplitude matrix, a polarization ratio distribution of the scattering center is obtained. The full polarization scattering feature extraction method provided in the scheme reduces the sensitivity of the polarization scattering matrix to the radar line-of-sight angle and the dependence on the scene, and improves the accuracy of target identification.

The embodiment of the specification discloses a kind of methods for monitoring migratory pests across scattering area, it is related to the technical field of monitoring migratory pests, method includes: obtaining the full polarization scattering matrix data of migratory pest target collected by full polarization radar, based on full polarization scattering matrix data, construct fusion feature set, at least two specific polarized direction map characteristic parameters are filtered in fusion feature set, determine clustering feature;Through the cluster feature, determine the class group attribution information of migratory pest target, to determine the corresponding first insect body size parameter prediction model and / or insect body size parameter prediction model group in the preset insect body size parameter prediction model set, class group attribution information includes the first membership of belonging to resonance area class group and the second membership of belonging to Rayleigh area class group;Through first insect body size parameter prediction model and / or insect body size parameter prediction model group, determine the corresponding body size parameter estimate, to monitor migratory pest target with body size parameter estimate.

The application discloses a full polarization accurate jamming waveform generation method based on a constant modulus constraint, and the method comprises the following steps: multiplying a receiving beam weight with a receiving signal to recover a radar angle domain signal, and obtaining two polarization signals; according to a full polarization radar array response matrix, a full polarization jammer transmitting signal and the two polarization signals, a polarization scattering matrix is used to polarize the recovered radar angle domain signal, and a transmitting signal design model of the jammer is determined; and the transmitting signal design model of the jammer is iteratively solved to generate a full polarization jamming signal for the full polarization radar. According to the method provided in the application, the polarization scattering matrix is used to polarize the jamming transmitting signal, and the constructed transmitting signal design model of the jammer is solved, so that the generation of the full polarization jamming waveform can be realized, and thus the problem that the single polarization jamming signal is easily identified can be avoided, and the jamming capability on the full polarization radar is improved.

The invention relates to an SAR sea ice classification method based on polarization decomposition and deep learning, and the method comprises the following steps: S1, obtaining GF-3QSPI mode single-view complex product SLC data, and carrying out the preprocessing, and obtaining a filtered polarization coherence matrix form expression; for the preprocessed GF-3 data, obtaining a complex scattering coefficient in a preprocessed polarization scattering matrix S through backstepping the polarization coherence matrix of each pixel, and synthesizing an RGB pseudo-color image; then, the RGB pseudo-color SAR image is marked; performing polarization decomposition feature extraction on the preprocessed GF-3 data by using MRSD polarization decomposition to obtain MRSD polarization decomposition features of a plurality of channels; making image data containing a backscattering coefficient and image data containing MRSD polarization decomposition characteristics; making image slices by using the two kinds of image data and the corresponding labels according to the same segmentation mode; and constructing and training a DU-Net model.