21 results about "Scatterometer" patented technology

The invention provides a satellite scatterometer Ku wave band observation value rainfall influence correction method, and relates to the field of sea surface wind inversion methods, and the method comprises the steps: obtaining Ku wave band observation data, C wave band data and rainfall rate data of a satellite scatterometer, carrying out wind field matching and wind speed correction under a rain-free condition, so as to obtain an NRCS reference value corresponding to a sea surface real wind speed, and carrying out the calculation of the NRCS reference value; constructing to obtain a scatterometer rainfall matching data set; according to the scatterometer rainfall matching data set, the influence of rainfall on the scatterometer observation value is analyzed, and a Ku waveband rainfall influence conceptualization model is constructed; and calculating rainfall influence conceptualization model parameters and generating a deviation lookup table. According to the invention, on the basis of a classic scatterometer wind field inversion process, the problem of large inversion wind field deviation caused by the influence of rainfall on the observation value of the Ku wave band scatterometer in a medium-low rainfall rate range is solved.

The invention discloses an NRCS determination method for a Ku wave band scatterometer under a rainfall condition, and relates to the field of satellite ocean remote sensing and meteorological detection.The method comprises the steps that the action mechanism of rainfall on radar signals in the atmosphere is considered, and a sea surface radar back scattering theoretical model is constructed based on a wind-generated sea surface wave spectrum and a dual-scale Bragg scattering theory; obtaining a rainfall attenuation coefficient, a normalized radar backscattering cross section contributed by raindrop scattering and dual-polarization normalized radar backscattering cross sections of a C wave band and a Ku wave band based on a sea surface radar backscattering theoretical model; according to the rainfall attenuation coefficient, the normalized radar backscattering cross section contributed by raindrop scattering and the dual-polarization normalized radar backscattering cross sections of the C wave band and the Ku wave band, the normalized radar backscattering cross section of the Ku wave band scatterometer under the rainfall condition is obtained; according to the method, the accurate normalized radar back scattering cross section of the Ku wave band scatterometer is obtained under the rainfall condition.

This invention discloses a joint inversion method for sea surface wind speed, comprising: jointly inverting observation data from a microwave scatterometer and a spaceborne GNSS-R over an overlapping observation area to obtain a first wind speed inversion result for that area; independently inverting the observation data from the microwave scatterometer to obtain a second wind speed inversion result; expanding the first wind speed inversion result to the second wind speed inversion result to obtain a third wind speed inversion result for the complete observation area of ​​the microwave scatterometer; independently inverting the observation data from a synthetic aperture radar to obtain a fourth wind speed inversion result; and expanding the fourth wind speed inversion result to the third wind speed inversion result to obtain a final joint inversion result. The technical solution provided by this invention performs joint inversion based on observation data from a microwave scatterometer, a spaceborne GNSS-R, and a synthetic aperture radar, thereby obtaining a final joint inversion result with a large observation area, high accuracy, and high resolution.

The present application belongs to the technical field of marine remote sensing, and particularly relates to a kind of full wind speed inversion methods based on spaceborne microwave active and passive combination physical statistics network, comprising the following steps: obtaining the brightness temperature data and backscattering coefficient of microwave radiometer and scatterometer, and performing data quality control;Construct sample data set;Load physical statistics constraint deep learning inversion algorithm model for training;Physical statistics constraint deep learning inversion algorithm model includes: input layer, network layer, physical layer, output layer;Network layer is composed of residual neural network, and physical layer is composed of Log formula of microwave radiometer and Log formula of microwave scatterometer to form microwave statistical matrix P matrix, which is a nonlinear matrix, and provides physical statistics constraint for the whole model;Output the wind speed of current full sea area.The present application effectively solves the problem of lack of physical support in the process of deep learning in marine parameter inversion, and provides high-quality and reliable wind speed products suitable for full wind speed range.

This invention discloses a space-ground coordinated radiometric characteristic calibration method for spaceborne active microwave sensors in the field of radar Earth observation technology. It constructs a space-ground coordinated calibration system consisting of a radiation reference source satellite and a ground calibration processing station. The method includes: determining the orbital parameters of the radiation reference source satellite; the satellite transmitting signals to the ground station in Earth observation mode, using a known RCS target on the ground to calibrate its own antenna gain and link gain; receiving signals from the satellite to be calibrated in Earth observation mode, accurately calculating and transmitting echo signals based on the calibrated parameters; and the satellite to be calibrated calibrating its own transmit / receive link gain based on the received echo signals. This invention can be widely applied to the on-orbit radiometric calibration of active microwave sensors such as synthetic aperture radar, altimeters, and microwave scatterometers, significantly improving calibration frequency and accuracy, supporting multi-band, high-stability quantitative remote sensing tasks, and providing reliable technical support for precise observation in fields such as ocean monitoring, weather forecasting, and topographic mapping.

The invention discloses a high-resolution and high-flux small-angle scatterometer which comprises a focus lens which is an ellipsoid multilayer film focus lens, the optical surface shape of the focus lens is determined by an ellipsoid, and a light source and a detector are arranged at the positions of a first focus and a second focus of the ellipsoid respectively. The focusing lens focuses the X-ray from the light source to the light sensing plane of the detector, and the focus of the straight-through light falls on the plane of the detector. According to the invention, the ellipsoid multilayer film focus lens is introduced to form a confocal light path, and the light source and the detector are respectively located at two focuses of the ellipsoid. Photons cut off by a traditional slit are actively collected, focused and utilized, and the luminous flux is greatly improved while the high resolution is kept; the light spot size d is matched with the pixel size of the detector, so that the light spot size d is controlled, and the light spot size d cooperates with the distance L to determine the angular resolution. In this way, the resolution performance of the device can be precisely customized, and the angular resolution and the incident luminous flux can be improved at the same time.

The invention discloses an overlay error measurement system and method based on an angle resolution scatterometer, and the system comprises a computer control and processing system, a light source and a to-be-measured sample, and the axis of the light source is perpendicular to the axis of the to-be-measured sample. A collimating lens, a polarizing film, a first diaphragm, a first relay lens group and a first beam splitter prism are sequentially arranged along the irradiation direction of the light source, and the first diaphragm is placed at the conjugate plane position of the rear focal plane of the microscope objective; the alignment error measuring system based on the angle resolution scatterometer has the beneficial effects that the alignment error measuring system based on the angle resolution scatterometer is designed by utilizing the advantages of low cost, no damage, high efficiency and the like of an angle resolution scatterometer measuring technology. And on the basis of the basic principle that the positive and negative primary light intensity difference and the overlay error are approximately in a linear relationship within a certain range, a neural network model is trained for data fitting. And obtaining a predicted overlay error through the measured scattered light intensity information and the trained neural network.

The invention belongs to the technical field of ocean remote sensing, and particularly relates to a satellite-borne microwave active and passive combination-based physical statistical network full wind speed inversion method, which comprises the following steps of: acquiring brightness temperature data and backscattering coefficients of a microwave radiometer and a scatterometer, and performing data quality control; constructing a sample data set; loading a physical statistical constrained deep learning inversion algorithm model for training; the physical statistical constraint deep learning inversion algorithm model comprises an input layer, a network layer, a physical layer and an output layer; the network layer is composed of a residual neural network, and the physical layer is composed of a microwave statistical matrix P matrix which is composed of a Log formula of a microwave radiometer and a Log formula of a microwave scatterometer, is a nonlinear matrix and provides physical statistical constraints for the whole model; outputting the current wind speed of the whole sea area. The problem that deep learning lacks physical support in the ocean parameter inversion process is effectively solved, and a high-quality reliable wind speed product suitable for the whole wind speed range is provided.

The application discloses a kind of satellite-borne scatterometer comprehensive calibration and verification method based on multi-objective.The method is after satellite-borne scatterometer on-orbit operation, high-precision calibration is realized using on-board inner calibration and star-ground integrated outer calibration means, supplemented with ocean calibration, rain forest calibration, cross calibration and other calibration verification means, to ensure on-orbit observation accuracy.In addition, the method connects on-board real-time link inner calibration, star-ground integrated outer calibration, ocean calibration, rain forest calibration, cross calibration and other multi-target calibration and verification technologies from the angle of business use and information feedback, forms a set of practical complete satellite-borne scatterometer calibration and verification method, greatly improves the completeness of whole process of business system.

The invention discloses a double-frequency scatterometer wind field joint inversion method based on deep learning, solves the problems of insufficient precision and poor adaptability of an existing inversion technology, and belongs to the technical field of space remote sensing and marine environment monitoring. The inversion method comprises the following steps: simulating satellite-borne multi-angle measurement data of the dual-band scatterometer to obtain simulation data; constructing physical enhancement features based on a scatterometer sea surface scattering mechanism; establishing a double-branch deep learning wind field inversion model which comprises a full wind speed model and a branch wind speed model, sharing a feature extraction backbone network, and respectively realizing wind speed and wind direction inversion by two output branches; training the full wind speed model and the sub-wind speed model based on the simulation data; and performing wind field inversion based on the trained full wind speed model and sub-wind speed model.

The invention relates to the technical field of ocean exploration, in particular to an active and passive microwave combined sea surface multi-element inversion method and system without a background field. According to the method, based on multi-band and multi-polarization combined observation data of a satellite-borne active and passive microwave imager and a microwave scatterometer, a linear regression model is constructed by using a microwave radiation transmission model and historical atmospheric ocean data, and initial inversion results of sea surface salinity, sea surface temperature, sea surface wind speed, total water vapor amount and total liquid water amount are obtained. On the basis, a least square method is adopted to carry out staged optimization inversion on observation data of different frequency bands, inversion results of all sea surface elements are updated step by step, and fine inversion of sea surface salinity and sea surface wind speed is realized through active and passive joint inversion. According to the method, collaborative inversion of multiple elements of the sea surface is realized under the condition of not depending on external background field data, and the inversion precision and stability are improved.

The invention discloses a scatterometer calibration task planning method and device, equipment, a medium and a product, and relates to the field of micro-nano satellite networking calibration task planning, and the method comprises the steps: constructing a target characteristic model of a backscattering coefficient; independent variables of the target characteristic model of the backscattering coefficient are a preset sampling moment and an incident angle and an azimuth angle measured by the satellite scatterometer at an observation point at the preset sampling moment, and dependent variables are the backscattering coefficient measured by the satellite scatterometer at the observation point at the preset sampling moment; obtaining an initial state of reinforcement learning according to the target characteristic model of the backscattering coefficient; the state comprises a backscattering coefficient; based on the initial state of reinforcement learning, adopting a reinforcement learning method to obtain actions under each iteration number; the action is a scatterometer calibration task planning scheme; and evaluating the action under each iteration number to obtain an optimal scatterometer calibration task planning scheme. According to the invention, the calibration efficiency can be improved, and the adaptability to extreme or unseen observation scenes can be enhanced.

The invention discloses a cloud layer directional scheduling method based on a low earth orbit satellite and a 5G network, and belongs to the technical field of artificial influence weather. According to the method, a cloud electromagnetic fingerprint spectrum is generated through cross-domain fusion data of a low-orbit satellite constellation and a ground scatterometer array; deploying a federated learning framework based on 5G MEC edge nodes, and constructing a cloud layer behavior prediction model with a transfer learning capability; a low earth orbit satellite and 5G base station cooperative beam forming technology is adopted, and quantum encryption is combined to generate a scheduling instruction; constructing a multi-modal intervention effect evaluation matrix by using 5G inductance integration characteristics; constructing a cross-regional computing power pool through an inter-satellite laser link, and dynamically allocating resources based on a particle swarm optimization algorithm; a block chain technology is introduced to form a self-evolution scheduling closed loop. According to the method, accurate perception, prediction, intervention and evaluation of the cloud layer are realized, the precision, efficiency and safety of directional regulation and control of the cloud layer are improved, and technical support is provided for coping with extreme weather.

The application discloses a method for determining the NRCS of a Ku-band scatterometer under rainfall conditions, and relates to the field of satellite ocean remote sensing and meteorological detection, which comprises the following steps: considering the action mechanism of rainfall in the atmosphere on a radar signal, and constructing a sea surface radar backscattering theory model based on a wind-generated sea surface spectrum and a double-scale Bragg scattering theory; obtaining a rainfall attenuation coefficient, a normalized radar backscattering cross section of raindrop body scattering contribution, and dual-polarization normalized radar backscattering cross sections of C-band and Ku-band based on the sea surface radar backscattering theory model; and obtaining the normalized radar backscattering cross section of the Ku-band scatterometer under rainfall conditions according to the rainfall attenuation coefficient, the normalized radar backscattering cross section of raindrop body scattering contribution, and the dual-polarization normalized radar backscattering cross sections of C-band and Ku-band, so that the application can obtain an accurate normalized radar backscattering cross section of the Ku-band scatterometer under rainfall conditions.

This invention discloses a process simulation system designed for water treatment plants, belonging to the field of process simulation technology. It addresses the problem of accurately identifying key control moments in coagulation reactions. The system scans the raw water under test using a scatterometer to acquire scattered light intensity data, calculates the scattering fluctuation coefficient, determines whether metastable phenomena exist in the raw water, and decides whether to activate the metastable analysis mechanism. Under the metastable analysis mechanism, disturbance tests are conducted, effluent turbidity data is collected, and disturbance response coefficients are calculated to pinpoint metastable candidate moments and define metastable failure intervals. Within these intervals, real-time monitoring of the dual conductivity and hydrolysis reaction data in the coagulation reaction zone generates a conductivity differential response. Combined with reaction information, the system analyzes the raw water potential recovery trend, collects raw water particle size distribution data, calculates skewness coefficients to correct the potential recovery trend, and obtains the collision efficiency. After time integration processing, the raw water is subjected to graded control to improve process stability and treatment efficiency.

The present application belongs to the field of active microwave remote sensing and ocean data processing technology, and particularly relates to a center frequency pre-biased local oscillator signal extraction method and extraction device, which comprises: a satellite-borne scatterometer performs fan beam conical scanning, and according to the relative motion between the satellite ground footprint coverage and the earth, a three-dimensional distribution of Doppler frequency offset with latitude, azimuth and elevation angle is obtained; according to the obtained three-dimensional distribution of Doppler frequency offset with latitude, azimuth and elevation angle, a Doppler frequency offset lookup table is generated according to the division interval of the azimuth angle; the center frequency of the local oscillator signal is pre-biased using the Doppler frequency offset lookup table according to the division interval of the azimuth angle, and a pre-biased local oscillator signal is obtained.

The embodiment of the application provides a sea ice extraction method based on a rotating sector scatterometer, comprising: acquiring polar backscatter observation data collected by the rotating sector scatterometer, wherein the polar backscatter observation data comprises a plurality of groups of backscatter observation data obtained by performing multiple observations on a polar region by the rotating sector scatterometer; performing principal component analysis on the plurality of groups of backscatter observation data to obtain principal component rotation feature data of the backscatter observation data; and performing sea ice feature extraction based on the principal component rotation feature data to obtain distribution information of sea ice in the polar region.

This disclosure relates to the field of remote sensing and mapping technology, and provides an adaptive distributed scatterer interferometry method, apparatus, and medium for mountainous areas. The method includes: acquiring radar image data and digital elevation model (DEM) data of the target monitoring mountainous area; calculating the radar vegetation index of each pixel in the dual-polarization synthetic aperture radar (DAP) image sequence and classifying it into multiple vegetation coverage levels to obtain a vegetation coverage grading atlas; identifying statistically homogeneous pixels in each image scene under the spatial constraints of vegetation coverage levels according to the grading atlas to determine a candidate set of distributed scatterers; further, using the grading atlas, performing adaptive coherence threshold filtering on each candidate point to obtain multiple distributed scatterers; and determining the long-term surface deformation results of the mountainous area based on the multiple distributed scatterers, the DAP image sequence, and the DEM data. This embodiment effectively improves the accuracy and reliability of distributed scatterer extraction and is suitable for deformation monitoring in complex mountainous areas.

An apparatus, system, and method of patterning device inspection are described. According to an aspect, a method of inspection is disclosed that includes receiving, at a multi-element detector within an inspection system, radiation scattered at a surface of an object. The method also includes measuring, with processing circuitry, an output of each element of the multi-element detector, the output corresponding to the received scattered radiation. Further, the method includes calibrating, with the processing circuitry, the multi-element detector by identifying a valid pixel region comprising one or more elements of the multi-element detector having a measured output above a predetermined threshold. The method also includes identifying an invalid pixel region comprising remaining elements of the multi-element detector. Additionally, the method includes setting the valid pixel region as a default alignment setting between the multi-element detector and a light source that caused the scattered radiation.

The present application belongs to the field of active microwave remote sensing and microwave scatterometer, and particularly relates to a kind of two-dimensional look-up table acquisition methods of spaceborne microwave scatterometer slice segmentation, the method comprises: obtaining the three-dimensional distribution of Doppler frequency offset with satellite platform subsatellite point latitude, azimuth and pitch;And based on the three-dimensional distribution, obtain Doppler frequency offset;Again, using the relationship between echo frequency and Doppler frequency offset of spaceborne sector-beam conical scanning microwave scatterometer, obtain the three-dimensional distribution of echo frequency of spaceborne sector-beam conical scanning microwave scatterometer with satellite platform subsatellite point latitude, azimuth and pitch;With equidistant ground slice as target, obtain the position of equidistant ground slice on the echo frequency corresponding to pulse sampling point, obtain the three-dimensional look-up table of slice segmentation;Dimension reduction optimization is carried out to three-dimensional segmentation look-up table, and two-dimensional look-up table of spaceborne sector-beam conical scanning microwave scatterometer slice segmentation is obtained.

The application discloses a sea surface wind speed prediction method, comprising the following steps: receiving GNSS-R data of a sea surface, and setting the GNSS-R data as first data; receiving measurement data of the sea surface by a spaceborne microwave scatterometer, and setting the measurement data as second data; inputting the first data and the second data into a sea surface wind speed prediction model to obtain a predicted sea surface wind speed value. The sea surface wind speed prediction model comprises a machine learning (ML) regression model, and the first data and the second data are subjected to inversion calculation and fusion through the ML regression model to obtain the predicted sea surface wind speed value.