23 results about "Cloud radar" patented technology

The invention discloses a visibility inversion method based on millimeter wave cloud radar and laser radar data fusion, and relates to the technical field of meteorology, and the method comprises the steps: guiding a millimeter wave cloud radar and a laser radar to carry out data correction and equivalent relation construction through employing a fog drop particle size distribution function measured by a fog drop spectrometer in a sample scene in advance; and then applying the correction coefficient and the equivalent function relationship to a target scene to perform multi-source data correction and fusion on the millimeter wave cloud radar and the laser radar and invert visibility. The method can fuse complementary advantages of the laser radar and the millimeter wave cloud radar; the introduction of the fogdrop particle size distribution function effectively avoids the distribution hypothesis of fogdrop shapes in the traditional model, can weaken the seasonal drift of the traditional empirical coefficient, significantly improves the accuracy and reliability of visibility inversion, and achieves the high-precision visibility monitoring under various weather conditions from clear sky to dense fog.

For example, a processor may be configured to process point cloud (PC) radar information comprising radar detection information of a plurality of possible detections, wherein radar detection information corresponding to a possible detection of the plurality of possible detections comprises information of a plurality of radar attributes of the possible detection, wherein the processor is configured to determine a plurality of validity scores corresponding to the plurality of possible detections based on the radar detection information of the plurality of possible detections, a validity score corresponding to the possible radar detection to indicate whether it is more probable that the possible detection is a valid detection or a False-Alarm (FA) detection, wherein the processor is to output radar target information based on the plurality of validity scores corresponding to the plurality of possible detections.

The embodiment of the application discloses a kind of multi-modal cloud classification methods based on visual language model, the method comprises: constructing text generation module, generates the cloud description text containing high-level semantic information;Cloud multi-modal feature extraction and fusion module is constructed, cloud image, cloud description text and millimeter wave cloud radar observation data are carried out feature coding and adaptive fusion, obtain multi-modal feature representation;Classifier module is constructed, and the cloud shape classification result of training cloud image is obtained;Total loss function is constructed, and the multi-modal cloud shape classification model consisting of cloud multi-modal feature extraction and fusion module and classifier module is optimized;Test cloud image, and the cloud description text and millimeter wave cloud radar observation data corresponding to test cloud image are input into multi-modal cloud shape classification model and carry out cloud shape classification.The application is realized by introducing high-level semantic cloud description and designing cloud multi-modal adapter, effective fusion of image, radar and text information, improve the accuracy and robustness of cloud shape classification.

The invention discloses a method for inverting warm cloud microphysical parameters based on a ground-based millimeter wave cloud radar and a microwave radiometer, and the method comprises the steps: 1, obtaining the observation data of the millimeter wave cloud radar, and obtaining a whole-layer liquid water path and a temperature gallery line through the microwave radiometer; 2, identifying warm cloud in millimeter wave cloud radar observation by using a temperature gallery line T; 3, constructing an initial liquid water content profile based on the reflectivity factor and the particle concentration in the warm cloud layer, and calculating an initial liquid water path of the warm cloud layer; 4, the whole-layer liquid water path obtained by the microwave radiometer is used as whole-layer constraint, the initial particle concentration and the initial liquid water content at the vertical height in the warm cloud layer are calibrated according to the proportion, the adjusted warm cloud layer liquid water content and the adjusted warm cloud layer inner particle concentration are obtained, and a physical boundary constraint interval is applied to the particle concentration; and 5, calculating the effective radius of the particles in the warm cloud layer according to the adjusted liquid water content of the warm cloud layer and the particle concentration in the warm cloud layer. According to the method, high-precision warm cloud microphysical parameter vertical distribution can be obtained.

The application discloses a millimeter wave cloud radar echo classification method and system based on physical guidance deep learning, comprising: constructing a multi-source fusion dataset, aligning, standardizing and sliding window sampling preprocessing millimeter wave cloud radar base data and co-sited ground observation data; extracting thermodynamic state information from the ground observation data through a physical constraint gating module, generating a clear sky echo probability graph to adaptively weight the radar initial features; based on an encoder-decoder backbone network, the weighted radar features are jointly convolved and extracted, and an ECH regression branch is used to fuse the thermodynamic features and the radar features to predict the effective clutter height; based on the predicted effective clutter height sequence, an adaptive height filtering region is constructed, and morphological opening operation and intersection operation of dynamic height constraint are used to realize fine echo classification post-processing, so that the height balance of meteorological echo retention rate and clear sky echo suppression rate is realized, and the data quality of the millimeter wave cloud radar is effectively improved.

This application provides a millimeter-wave cloud radar observation method and related apparatus based on multi-source data. A fused dataset is formed by real-time acquisition and preprocessing of at least one of the following: weather radar mosaic data, meteorological satellite data, ground visibility observation data, and co-located or adjacent aerosol lidar data. A preset rule set is used to determine that the current weather scene at the cloud radar station includes at least weather echo approaching, weather echo receding, local weather processes, fog, and non-precipitating clouds. Based on the identified weather scene type, the corresponding combined scanning strategy (composed of a basic scanning mode consisting of vertical top-view scanning, distance-height scanning, and three-dimensional volume scanning, and a fine scanning mode consisting of local planar horizontal scanning and layered planar horizontal scanning, combined according to preset logic) is called from a preset strategy mapping table and executed. During execution, the parameters of the fine scanning mode are dynamically adjusted based on the position and intensity information of the real-time radar echo or changes in the detection mode.

The invention relates to a method for realizing cloud shape automatic identification based on a ground-based millimeter wave cloud radar, and the method comprises the following steps: S10, carrying out the quality control of collected cloud radar data, and removing abnormal echo data; s20, performing cloud boundary identification based on the data after quality control to obtain data sets of different cloud blocks; s30, calculating a cloud feature quantity by using the data sets of different cloud blocks; and S40, cloud identification is carried out based on the characteristic quantity. According to the method, the cloud radar is used for detecting data, through observation data analysis, quality control, cloud boundary identification and cloud characteristic quantity calculation, the cloud characteristic quantity is used as a basis, a threshold value method and a K-nearest neighbor machine learning method are used, automatic cloud identification is achieved, automatic cloud observation of a meteorological system is improved, and the method is suitable for automatic cloud observation of the meteorological major.

The application discloses a kind of spaceborne cloud radar beam conical spiral scanning forward-looking and rearview observation fast registration method, belong to meteorological radar signal processing and spaceborne observation registration technical field.The method includes: according to satellite orbit parameter and scanning geometry, the ground intersection of each pulse beam footprint is calculated, and footprint point set is constructed;According to the interval of scanning circle, the potential overlapping observation is traversed, and the candidate point pair is generated in combination with spatial distance gate;Matching cost function is constructed with footprint distance as main item and fusion observation consistency constraint, and effective registration point pair is obtained under one-to-one correspondence constraint;Registration point pair obtained by different scanning circle interval and different distance gate is accumulated and deduplicated, and global registration point set is formed.The present application makes full use of multiple overlapping observations of spiral trajectory, significantly increases the same sample, improves the reliability and precision of registration under the condition of high-speed spaceborne platform, and can provide high-quality input for subsequent three-dimensional observation data fusion, data quality control and data assimilation.

The invention discloses a cloud recognition method based on multiple observation devices, which is characterized in that a weather phenomenon video intelligent observation instrument, an aerosol laser radar, a millimeter wave cloud radar and other observation devices are used for comprehensive judgment to finally obtain a recognition result, and comprises the following steps: acquiring a sky image from the weather phenomenon video intelligent observation instrument, and judging whether cloud exists or not; the cloud base height measured by the laser radar is combined to judge whether the cloud is high cloud or low cloud; non-cloud echoes and noise echoes are filtered through a quality control algorithm, and a reliable cloud radar product is obtained; the boundary of the cloud is identified through the cloud radar product, so that the cloud information such as the cloud bottom height, the cloud top height, the cloud layer thickness and the cloud layer number is obtained. The method is small in calculation amount, high in recognition rate and wide in application range, and overcomes the defects that in the prior art, manual work is relied on, and the processing speed is low.

This application provides a cloud measurement system and method based on a wave glider. By integrating a laser cloud-measuring radar with a marine meteorological observation instrument onto a wave glider platform with ultra-long endurance, autonomous navigation, and virtual mooring capabilities, a sea-based mobile cloud observation system is constructed. This system overcomes the inherent shortcomings of existing satellite remote sensing observations, such as discontinuity and lack of vertical structure information, as well as the high cost, poor maneuverability, and limited coverage of ship and buoy observations. It achieves long-term, continuous, and mobile observation of cloud systems over vast ocean areas and can acquire high-precision cloud base height, vertical structure, and conventional meteorological elements from the sea surface, significantly improving the spatiotemporal continuity and data quality of marine meteorological observation.

This invention discloses a method and system for identifying layered cloud melting layers based on cloud radar, belonging to the field of cloud radar identification technology. The method includes: acquiring airborne cloud radar observation data; resampling the airborne cloud radar observation data to a unified geographic grid; for each sample, extracting continuous time periods with radar bright band characteristics and performing time averaging to obtain a comprehensive average reflectivity factor profile; calculating the vertical gradient of the comprehensive average reflectivity factor profile; and identifying the effective melting layer based on the vertical gradient. This invention automatically identifies the height of the top and bottom of the melting layer by analyzing the vertical gradient changes of reflectivity factor and vertical velocity, exhibiting high accuracy and strong adaptability.

The invention discloses a snowfall inversion method based on a millimeter wave cloud radar and a microwave radiometer, and relates to the technical field of radar snowfall inversion, and the method comprises the following steps: S1, data input: millimeter wave cloud radar data and microwave radiometer data are input; s2, multi-source equipment data space-time dimension matching is carried out; s3, phase state classification of extremely cold particles, and elimination of non-snowfall phase state interference; s4, inverting the snowfall rates of different height layers; s5, the accumulated snowfall of the whole-layer snowfall rate is obtained through time integration; and S6, outputting a result. According to the invention, the complementary advantages of high temporal-spatial resolution of the millimeter wave cloud radar and abundant atmospheric environment parameters of the microwave radiometer are brought into full play, the inherent defect of single equipment inversion is solved, and the precision and reliability of snowfall inversion in the extremely cold environment are greatly improved; and a data support is provided for practical applications such as meteorological monitoring and early warning in extremely cold areas, and prevention and reduction of snowstorm disasters.

The invention discloses a mechanism-driven chaff cloud radar coupling characterization and imaging recognition algorithm, which comprises the following steps: imaging based on a vector radiation transfer-inverse synthetic aperture radar to obtain a chaff cloud imaging picture; chaff cloud coupling element analysis: performing a deconstruction theory on the acquired chaff cloud scattering elements through a vector radiation transfer-moment method; chaff cloud classification: establishing a chaff cloud recognition algorithm based on a gray-level co-occurrence matrix, and classifying chaff clouds after element decomposition; according to the method, the coupling characteristics of chaff filaments in chaff clouds can be well described, and imaging, analysis and classification of chaff clouds with different number densities are realized; moreover, the radar imaging feature difference between the chaff cloud and the conventional target can be represented based on the digital features, and radar recognition is realized.

This invention discloses a method and system for generating 3D terrain registration profiles and interactive situational awareness data using airborne cloud radar, relating to the field of airborne cloud radar application technology. The method acquires real-time aircraft position and attitude, cloud radar per-detection-unit data, 3D terrain data, and external airspace data; maps multi-source data to a unified time reference and establishes a cache queue; constructs a four-level transformation model from the cloud radar coordinate system through the aircraft coordinate system, local navigation coordinate system to the geographic coordinate system or 3D terrain coordinate system; interpolates attitude parameters for each effective detection unit according to the sampling time, completing point-by-point or group attitude compensation and spatial registration; constructs 3D cloud bodies, terrain, aircraft, flight paths, and airspace objects and overlays them to form a 3D main view; generates cutting planes and section views, achieving bidirectional linkage positioning between the main view and section views; supports multi-view switching, target attribute viewing, and highlight display interaction. This invention solves the problems of cloud body drift and spatial interpretation difficulties, improving the efficiency of flight situational awareness.

The present application relates to radar detection technical field, especially to the visibility fusion inversion method based on cloud radar and laser radar, method includes: obtaining cloud radar equivalent reflectivity factor, laser radar backscattering echo power and ground meteorological station's temperature and humidity data, time and space matching is carried out to multi-source data, and the near field common view area boundary is defined, and the laser radar extinction coefficient in the near field common view area is obtained by inversion; based on the laser radar extinction coefficient in the near field common view area and the cloud radar equivalent reflectivity factor in the near field common view area, combining the temperature and humidity data, the double-band atmospheric drop spectrum equivalent constraint factor is solved; based on the double-band atmospheric drop spectrum equivalent constraint factor and the full range cloud radar equivalent reflectivity factor, the full range liquid water content profile is reconstructed; based on the full range liquid water content profile, the full range equivalent extinction coefficient is inversely reconstructed, and the full range visibility profile is obtained by inversion.

This invention provides a method for monitoring aircraft icing risk based on microwave radiometer and cloud radar data, relating to the field of aviation safety monitoring. The method includes: using microwave radiometer data and millimeter-wave cloud radar data from grid points within a target flight area, calculating the probability of multiple refined particle phase types corresponding to each grid point using a fuzzy logic algorithm; then, determining the target cloud phase at each grid point based on the probability from the multiple refined particle phase types; identifying the category of the target cloud phase; calculating the particle radius and cloud water content according to the category; combining the determined piecewise temperature function value, piecewise particle radius function value, piecewise cloud water content function value, and a constructed icing intensity matrix to obtain the icing risk intensity (ICE) value; and outputting the aircraft icing risk monitoring results for each grid point based on the icing intensity indicated by the target cloud phase or the ICE value. This invention can effectively improve the accuracy and resolution of icing monitoring and identification.

The invention relates to a millimeter wave-laser common-caliber composite cloud radar system, belongs to the crossing field of optics and radar detection technologies, and is mainly applied to meteorological detection. The system is mainly composed of a laser primary mirror, a laser secondary mirror, a millimeter wave primary mirror, a millimeter wave secondary mirror, a laser radar transmit-receive system and a millimeter wave radar transmit-receive system. Based on the Cassegrain antenna structure, through unique design, the common-aperture composite detection function of the laser radar and the millimeter-wave radar is realized.

The invention relates to the technical field of radar detection, in particular to a visibility fusion inversion method based on a cloud radar and a laser radar, and the method comprises the steps: obtaining the equivalent reflectivity factor of the cloud radar, the backscattering echo power of the laser radar and the temperature and humidity data of a ground meteorological station, carrying out the space-time matching of multi-source data, defining the boundary of a near-field common-view area, and carrying out the calculation of the near-field common-view area. Carrying out inversion to obtain a laser radar extinction coefficient in the near-field common-view area; based on the laser radar extinction coefficient in the near-field common-view area and the cloud radar equivalent reflectivity factor in the near-field common-view area, combining the temperature and humidity data to solve a dual-band atmospheric droplet spectrum equivalent constraint factor; reconstructing to obtain a full-scale liquid water content profile based on the dual-band atmospheric droplet spectrum equivalent constraint factor and a full-scale cloud radar equivalent reflectivity factor; and based on the full-scale liquid water content profile, reversely reconstructing a full-scale equivalent extinction coefficient, and carrying out inversion to obtain a full-scale visibility profile.

The invention discloses a millimeter wave cloud radar zero-degree layer bright band identification method fused with texture parameters, and relates to the technical field of meteorological radar inversion secondary products, and the method comprises the steps: carrying out the sliding window calculation of each type of single polarization parameters obtained by a millimeter wave cloud radar in a THI scanning mode in a vertical direction through a vertical window, and obtaining the zero-degree layer bright band of the millimeter wave cloud radar; the method comprises the steps of obtaining vertical texture features of a single polarization parameter at different heights, then fusing the vertical texture features of a plurality of single polarization parameters at the same height to obtain multi-parameter fused vertical texture features at different heights, and then identifying a zero-degree layer bright band at the current scanning moment. According to the method, through deep fusion and collaborative analysis of vertical texture features of multiple single-polarization parameters, multi-parameter observation information of the single-polarization millimeter wave cloud radar can be fully mined, and high-precision and automatic identification of the zero-degree layer bright band can be realized on the premise of not depending on dual-polarization parameters and external temperature data.

A method for velocity estimation of a meteorological target by using a terahertz cloud radar, comprising: first performing frequency division polarization echo phase coherence processing, followed by frequency division polarization echo phase extraction and velocity estimation, then performing mean Doppler velocity bias correction based on ground / sea surface echoes, and finally performing joint velocity correction considering radar reflectivity factor partial derivatives and target velocity spatial continuity. The present invention achieves high-precision velocity estimation, eliminates the ghosting problem caused by low polarization isolation, and solves the high-velocity ambiguity problem in a spaceborne terahertz cloud radar.

The utility model discloses a cloud radar water-cooling heat dissipation device in the technical field of cloud radars, which comprises a base with a rotating body structure, a control main body is fixedly connected to the upper end of the base, a radio frequency cover with a hemispherical shell-shaped structure is fixedly connected to the upper end of the control main body, and fogging guide mechanisms are arranged on the side surfaces of the control main body and the radio frequency cover. The fogging guide mechanism comprises a gas conveying pipe, the gas conveying pipe is fixed to the side faces of the control body and the radio frequency cover in a spiral arrangement mode, one end of the gas conveying pipe is fixedly connected with a protection plate, the lower end of the protection plate is connected with a fixing shell, and one end of the fixing shell is provided with a cooling mechanism for cooling water mist in the gas conveying pipe; the rotating rod drives the oscillation wheel to rotate in the fixed shell, the oscillation wheel oscillates with the liquid in the fixed shell so as to generate water mist, the first air pump is started to drive the water mist generated in the fixed shell to move along the air conveying pipe, the water mist absorbs heat through the radio frequency cover and the control main body, and therefore the radio frequency cover and the control main body are cooled.

The utility model relates to cloud radar antenna technical field, and disclose a kind of cloud radar antenna pitch fine adjustment support, including main rod and the holder rotatingly connected in the upper end of main rod by first connecting piece, the outer wall of main rod is slidably connected with sliding sleeve, and the sliding sleeve is penetrated with limiting bolt, and limiting bolt is threadedly connected with sliding sleeve, the outer wall of sliding sleeve is fixedly connected with second connecting piece, the bottom of holder is slidably connected with third connecting piece, second connecting piece is rotatably connected with telescopic link, and the other end of telescopic link is rotatably connected with third connecting piece;This kind of cloud radar antenna pitch fine adjustment support, when cloud radar antenna is used, the coarse adjustment of the pitch angle of cloud radar antenna can be realized by sliding sleeve up and down, the pitch angle of cloud radar antenna can be realized by adjusting the length of telescopic link Fine adjustment, so not only can the adjustment of the pitch angle of cloud radar antenna be more convenient, but also can the accuracy of the pitch angle adjustment of cloud radar antenna be higher.

A radar system comprises a plurality of receive antennas that receive a radar signal. One or more processors are configured to generate an n-dimensional point cloud comprising values for parameters of an object in an environment of the radar system, where the n-dimensional point cloud is generated based upon the radar signal, and where n is greater than 2. The one or more processors are further configured to generate from the n-dimensional point cloud a 2D RGB image representing the values of the parameters in different colors, respectively. The parameters can comprise at least object radar cross-section, height, and velocity, etc. The one or more processers are further configured to provide the 2D RGB image to a convolutional neural network that assigns a classification to the object based on the 2D RGB image.