1501 results about "Radar detection" patented technology

The invention discloses a low-altitude three-dimensional wind field inversion method and system based on a single wind measurement laser radar, relates to the technical field of radar detection, and solves the technical problems that a traditional wind measurement laser radar cannot directly obtain a velocity vector, data missing of a key area is caused by a blocking effect, and detection nodes are not uniform. The method specifically comprises the following steps: S1, executing a multi-elevation-angle body scanning detection mode and a wind profile detection mode by using a wind measurement laser radar, and obtaining radial wind speed data of a target area; s2, quality control is carried out on the radial wind speed, isolated abnormal points are eliminated, and isolated data missing points are filled; s3, carrying out blocking correction on a single scanning surface by using a smooth spline interpolation method; s4, after the radial wind speed is corrected based on the wind profile, an initial background three-dimensional wind field is obtained; and high-precision wind field inversion of a wind field in a three-dimensional space can be realized by using the radial wind speed of multi-layer body scanning of a single wind measurement laser radar.

The invention relates to the technical field of engineering, in particular to a geomembrane defect detection system and method under the earth and stone medium coverage condition, and the geomembrane defect detection system comprises a multi-mode adaptive sensing module, an edge calculation module, a data fusion module, a digital twinning module and a decision and application module; compared with the defects that single resistivity or geological radar detection is adopted in the prior art, interference of medium conductivity and metal impurities is likely to happen, and the deep resolution is insufficient, the scheme adopts multi-mode sensor network cooperative work, and a dynamic density layout and time-phased acquisition strategy is combined; through multi-mode complementation of an electrode array, an ultra-wideband radar, a distributed optical fiber and an MEMS vibration sensor, a dominant sensing mode is automatically switched in a conductive clay area, temperature gradient interference is avoided through night acquisition, the penetration depth is increased to 15 meters through multi-band radar fusion, and metal interference is inhibited; the detection precision and the anti-interference capability in a complex medium environment are remarkably improved, and the problems of high false alarm rate and high omission ratio of a traditional method are effectively solved.

The invention relates to a target person detection method and device based on millimeter wave radar, equipment and a medium. According to the method, radar detection configuration is adjusted, a gridding coordinate system is established in a target treadmill monitoring area, millimeter wave radar is used for collecting feature data related to movement of a target person, and movement track changes of the target person are extracted to generate person position information; acquiring breathing and heartbeat signals of a target person, and performing signal filtering and feature extraction to obtain vital sign information; comprehensively evaluating the existence state of the target person according to a preset threshold value and a discrimination standard in combination with the person position information and the vital sign information; and when the evaluation result shows that the target person leaves, the vital signs are abnormal or a cheating behavior exists, judging that the target person is in an abnormal state, and generating a corresponding early warning instruction. By adopting the method, abnormal conditions can be found in time, early warning can be generated, and the monitoring accuracy and response speed are improved.

The invention discloses an offshore small target radar detection method and system, and relates to the technical field of radio detection. The method comprises the following steps: acquiring radar echo data, a visible light image, an infrared image, an AIS signal and navigation attitude data in real time; performing sea clutter suppression and pre-filtering on the radar echo data, and correcting target plots in combination with a historical radar plot set to obtain a radar single-source target track set; performing target detection on the visible light image and the infrared image to obtain an optical target detection set and an infrared target detection set; the radar single-source target track set, the optical target detection set, the infrared target detection set, the AIS signals and the navigation attitude data are associated and fused, and finally deception feature recognition and consistency verification are performed to obtain an updated fused target track set; according to the method, man-made confrontation and cheating behaviors on the sea can be accurately identified, and reliable support is provided for safety monitoring on the sea, maritime affair supervision and emergency response.

In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three-dimensional (3D) space, in both highway and urban scenarios. RADAR detections may be accumulated, ego-motion-compensated, orthographically projected, and fed into a neural network(s). The neural network(s) may include a common trunk with a feature extractor and several heads that predict different outputs such as a class confidence head that predicts a confidence map and an instance regression head that predicts object instance data for detected objects. The outputs may be decoded, filtered, and / or clustered to form bounding shapes identifying the location, size, and / or orientation of detected object instances. The detected object instances may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

The invention provides a three-dimensional tracking method applied to a single-photon laser radar to detect a far-field dynamic unmanned aerial vehicle point target, and belongs to the technical field of laser radar detection and target tracking. The problems that in the prior art, under the long-distance condition, the target imaging size is smaller than one pixel, and a detection and tracking algorithm based on shape, texture or edge features fails are solved. The method comprises the following steps: constructing a six-dimensional state vector containing a three-dimensional position and a three-dimensional speed of a target; predicting a state vector according to a state transfer function containing air resistance and turning acceleration; and an extended Kalman filtering framework is utilized to update the predicted state and obtain state estimation of the target, and the extended Kalman filtering framework comprises a self-adaptive process noise adjustment mechanism based on innovation feedback and is used for adjusting a process noise covariance matrix on line. The method is mainly used in the field of detection and multi-dimensional tracking of airspace moving targets.

The invention discloses a phased array weather radar calibration method based on a generalized metal ball calibration theory. Comprising the following steps: in a microwave anechoic chamber, collecting two-dimensional antenna pattern data of a phased array weather radar in at least one working mode and pointing to a plurality of different beams; performing numerical integration on the two-dimensional antenna pattern data, calculating a combined beam main lobe solid angle of the transmitting antenna and the receiving antenna, and calculating an equivalent beam width product; calculating a calibration theoretical reflectivity factor of the metal ball based on the product of the theoretical radar scattering sectional area and the equivalent beam width of the metal ball; actual detection echo data of the phased array radar on the metal ball under different working modes and beam pointing is acquired, and an actual reflectivity factor is calculated; and comparing the actual reflectivity factor with the calibration theoretical reflectivity factor to complete calibration of the phased array weather radar. Aiming at the non-Gaussian working mode of the phased array weather radar, the reflectivity factor of the phased array weather radar can be quantitatively evaluated, and the radar detection performance is improved.

The invention relates to the technical field of unmanned aerial vehicle signal processing, and discloses an unmanned aerial vehicle countering multiband radar early warning system, which comprises a multiband radar detection module, an echo data processing module, a target identification and positioning module and an early warning information generation module. The multi-band radar detection module emits radar waves of different frequency bands to obtain echo data, the echo data processing module calculates the difference degree and the comprehensive difference coefficient for target identification, the target identification and positioning module determines the position of the unmanned aerial vehicle, and the early warning information generation module issues early warning. The method has the advantages that accuracy and reliability are improved through multi-frequency-band detection, targets are accurately recognized and positioned, threat levels can be evaluated, interference resistance, clutter suppression and environment self-adaption capacity are achieved, multiple targets can be distinguished and tracked, the defects of an existing unmanned aerial vehicle monitoring technology are effectively overcome, powerful support is provided for unmanned aerial vehicle countering, and the method is worthy of popularization and application. And public safety and key area stability are ensured.

The invention provides a passive perception radar target classification method and system based on electromagnetic detection feature extraction, and the method comprises the steps: carrying out the multi-dimensional signal decomposition of an original scattered signal sequence through obtaining an electromagnetic radiation signal set of a target region; obtaining a time-frequency distribution feature set and a polarization response feature set corresponding to each signal acquisition period, extracting a target electromagnetic feature combination from the time-frequency distribution feature set and the polarization response feature set according to a preset electromagnetic feature association rule, generating a weight distribution parameter, and performing weighted fusion processing on the target electromagnetic feature combination to obtain a target electromagnetic feature combination; and the fusion feature vector set is input into an adaptive classification network, hierarchical classification operation is performed on the fusion feature vector set, the category identifier of the target scatterer and the corresponding classification confidence are output, and a radar detection optimization instruction is generated based on the category identifier and the corresponding classification confidence. According to the method, high-precision distinguishing of multiple types of scatterers is achieved under the passive sensing condition, and meanwhile the environmental adaptability and the calculation efficiency of the classification process are guaranteed.

The invention relates to a solid-state laser radar ranging method and system based on neural network processing, and belongs to the technical field of laser radar ranging, and the method comprises the steps: obtaining a laser echo signal when laser radar detection is carried out on a surrounding target object, and obtaining a photon counting histogram based on the laser echo signal; preprocessing the photon counting histogram, and inputting the preprocessed photon counting histogram into a pre-trained neural network model to obtain a peak confidence map containing an echo peak value and a peak time offset map; on the basis of the peak confidence map and the peak time offset map, obtaining one or more echo peak values of which the confidence exceeds a predetermined threshold and time position information of the one or more echo peak values of which the confidence exceeds the predetermined threshold; and based on the time position information, the distance information with one or more target objects is determined, so that the time position information measurement accuracy of one or more echo peak values is improved, and the laser radar ranging accuracy is improved.

The invention discloses an external damage hidden danger identification method and system based on AI image and radar dual verification, and relates to the technical field of artificial intelligence and multi-source perception fusion, and the method comprises the following steps: extracting the motion trail features of perception data in a target region, solving a coordinate transformation matrix through an iterative nearest point algorithm, carrying out the time sequence alignment, and carrying out the recognition of the motion trail features of the perception data; obtaining the calibrated sensing data; taking the calibrated image data and the calibrated radar data as input, and outputting a radar detection result and a visual identification result; projecting radar coordinates to an image coordinate system according to a coordinate transformation matrix based on a visual identification result and a radar detection result, outputting a space overlapping degree, and generating a matching result set; carrying out confidence fusion on the matching result set through a Bayesian probability model, and judging an external damage hidden danger level in combination with a radar detection result; according to the invention, through the AI vision and radar dual verification fusion technology, the problems of inconsistent multi-source perception and low external damage hidden danger identification precision are solved.

The invention discloses a target trajectory prediction method and system based on radar detection, and a computing device, relates to the technical field of radar detection, and solves the problem that important details in prediction of a running trajectory of an enemy target automobile are excessively lost due to the fact that an application scene of first filtering and then downsampling operation is not considered in the prior art. And the technical problem of prediction deviation is solved. The method comprises the following steps: collecting laser radar data through a laser radar sensor; converting the laser radar data into initial point cloud data; judging whether the real-time running scene of the target automobile is a preset dense scene or not; if yes, carrying out downsampling and then filtering operation on the initial point cloud data; if not, filtering the initial point cloud data and then downsampling the initial point cloud data; taking the preprocessed point cloud data as input of a trajectory prediction model to obtain trajectory parameters corresponding to a plurality of trajectories of the target automobile in a set period; acquiring a target trajectory based on the trajectory parameters; according to the invention, the prediction precision and accuracy are improved.

The invention relates to the technical field of wind power generation, in particular to a wind turbine generator deformation vibration monitoring device based on millimeter wave radar. The core function module comprises a benchmark sensing radar, a blade monitoring radar, a reference beacon, an inertial measurement unit, a data synchronization and acquisition unit and a signal processing unit; a reference beacon fixed on a substrate is detected through a reference sensing radar, an absolute motion vector A of the reference sensing radar is solved, then the absolute motion vector A is converted into an absolute motion vector B of a blade monitoring radar, then data C measured by an IMU (Inertial Measurement Unit) is converted into data D of the blade monitoring radar in a coordinate system of the blade monitoring radar, and the blade monitoring radar can be used for monitoring the blade. The absolute motion vector E of the blade monitoring radar is fused and extracted through an extended Kalman filter, and is finally used for compensating original data of blade measurement by the blade monitoring radar, so that the influence of cabin motion on deformation vibration monitoring is eliminated.

The invention provides an unmanned aerial vehicle countering method, which comprises the following steps of: acquiring a radar signal containing an unmanned aerial vehicle reflection signal in a target airspace through a radar detection module, obtaining an unmanned aerial vehicle motion feature through feature extraction, and inputting the unmanned aerial vehicle motion feature into a threat judgment model to determine a threat level. And if the active interception condition is met, controlling the radio interference module to transmit a directional interference beam and activate the physical interception module, and analyzing an interference efficiency parameter to generate a dynamic interception instruction. And finally, combining the target airspace coordinate, the threat level and the interception instruction into a countering control instruction, and sending the countering control instruction to a physical interception module to complete interception. According to the invention, automatic and accurate countering of the urban low-altitude unmanned aerial vehicle can be realized, and the response speed and interception reliability of a security and protection system are improved.

The invention provides an ultrasonic radar debugging data interaction method and system, and the method comprises the steps: carrying out the analysis of an obtained structured configuration file, and obtaining a parameter configuration set which comprises radar detection parameters and communication protocol parameters; generating an interaction control on the interaction panel based on the parameter configuration set, and establishing a bidirectional data binding channel between the interaction control and the parameter configuration set; in response to a parameter change operation of a user, extracting a change field of the parameter configuration set, and generating a difference configuration instruction based on the change field; driving an ultrasonic radar test based on the operation parameters of the dynamic parameter storage area, and collecting original signal data and processing result data to obtain a test data set; and performing association analysis on the difference configuration instruction and the test data set to generate test result data. By adopting the method, the efficiency and accuracy of functional verification of the radar sensor can be improved.

The invention discloses a robustness tracking filtering method for group targets, and belongs to the field of radar and signal processing. The implementation method comprises the following steps: establishing a kinetic model of a group target and a three-coordinate ground-based radar detection probability and measurement model; bayesian recursion of a multi-target state random finite set is realized by a Poisson random finite set, and an intensity function of the Poisson random finite set is described in a Gaussian mixture form. Estimating the maximum motion distance of the target between two adjacent filtering steps based on the dynamic characteristics of the group target, and achieving the detection of the target; the detection probability is described by using Bernoulli distribution, under the condition that the clutter intensity does not exceed the real target intensity, the prior clutter intensity is taken as a threshold value, the detection probability is adaptively adjusted in the filtering iteration process, the respective weights of the measurement irrelevant part and the measurement relevant part are updated, and the strong-robustness multi-target tracking under the condition of sensor leak detection is realized. The method also has the advantages of strong robustness, high tracking precision and small calculation amount.

The invention discloses a fusion detection and iron tower photoelectric tracking system and a fusion detection and iron tower photoelectric tracking method for a low-speed small target. The system comprises a multi-source detection module, a data fusion module and a photoelectric tracking module, and various heterogeneous data of a target are obtained through a radar detection unit, a frequency spectrum detection unit and a protocol cracking unit; after data preprocessing, performing multi-source data fusion by adopting a dynamic weight fusion algorithm and Kalman filtering to generate comprehensive target information; and based on the equipment space calibration parameters, calculating an optoelectronic equipment pointing instruction through a coordinate conversion model, and driving the optoelectronic equipment to accurately track a target. The system is further provided with a feedback optimization module which dynamically corrects the pointing instruction according to the tracking deviation. According to the invention, deep fusion and intelligent cooperation of multi-source detection data are realized, and the detection precision, tracking stability and system response speed of a low-speed small target are effectively improved.

The invention discloses a boulder dense stratum geological radar detection method based on horizontal directional drilling, which comprises the following steps of: firstly, drilling into a stratum from the earth surface at a smaller incidence angle by adopting the horizontal directional drilling, and drilling along the central axis of a shield tunnel to form a drill hole with the diameter of about 30cm; after drilling is completed, the geological radar is fixed to the rear portion of the drill bit through a rigid connecting device, and the geological radar synchronously advances and rotates along with the drill bit. Four rolling balls are arranged at the bottom of the geological radar to reduce motion resistance and are tightly attached to the inner wall of a drill hole to ensure detection stability. The geological radar is driven by rotation of a drill bit to rotate by 360 degrees for detection, so that stratum information around a drill hole is obtained; then, performing image recognition on drilling slag, and establishing a corresponding relation between a geological radar image and a stratum type so as to optimize interpretation precision of geological radar data; and finally, in combination with geological radar detection data and a drilling deslagging analysis result, generating a boulder distribution profile map, and predicting the boulder distribution condition on the tunneling path of the shield tunneling machine.

The invention belongs to the technical field of driving management and control, and particularly relates to an intelligent driving auxiliary control method and system based on a millimeter wave radar, and the system comprises a millimeter wave radar detection module, a data fusion output module, a driving decision generation module, a driving auxiliary control module, an auxiliary control safety evaluation module and a vehicle-mounted display early warning module. According to the invention, a plurality of vehicle-mounted millimeter wave radar sensors are used for environment detection, radar detection information and monitoring data of a vehicle-mounted camera are subjected to multi-modal data fusion, an environment sensing result is generated, and a deep learning algorithm is used for analysis according to the environment sensing result to formulate a corresponding control strategy. The corresponding operation is executed based on the auxiliary control decision, the driving safety and the vehicle intelligence and automation level are remarkably improved, the auxiliary control safety and the radar cooperation operation reliability can be reasonably judged, a user can conveniently take corresponding processing measures in time, the driving safety is further improved, and the vehicle supervision difficulty is reduced.

The invention discloses a high-efficiency transmission and reflection polarization conversion metasurface, which belongs to the technical field of electromagnetic wave regulation and control, and is characterized in that a plurality of transmission and polarization conversion metal structure layers arranged in an array are arranged between a first dielectric substrate and a second dielectric substrate; the top of the third dielectric substrate is correspondingly provided with a plurality of reflective polarization conversion metal structure layers which are arranged in an array; an air layer is arranged between the second dielectric substrate and the third dielectric substrate; the bottom of the first layer dielectric substrate and the top of the second layer dielectric substrate are respectively and correspondingly provided with metal grids, and a metal grid cavity is formed between every two adjacent metal grids. The metasurface has the functions of transmission polarization conversion and reflection polarization conversion, high-efficiency transmission polarization conversion can guarantee normal communication in a band, high-efficiency reflection polarization conversion can change polarization of radar detection waves, and the metasurface can be used for an embedded antenna cavity antenna housing.

The invention discloses a tracking optimization method for detecting a low-speed small unmanned aerial vehicle target by a single-photon laser radar, belongs to the technical field of optical detection and target tracking, and aims to solve the problem of unstable target imaging and tracking caused by speckle noise interference in the prior art. The invention provides a speckle noise suppression method based on a vibration emission optical fiber and a space-time dynamic kernel density estimation algorithm, and the method is combined with an improved mean shift-Kalman filtering algorithm to achieve the tracking optimization of a low-speed small unmanned aerial vehicle target, and obtains an echo signal through a single-photon laser radar. Constructing a three-dimensional data matrix and reconstructing distance and intensity images through space-time filtering and kernel density estimation; and then, in combination with gray level histogram probability estimation, similarity measurement and Mean Shift iteration, target area tracking is realized, and finally, a target position is predicted and output by using Kalman filtering. The method is suitable for the fields of long-distance unmanned aerial vehicle detection and monitoring, low-altitude security defense early warning, civil airspace management and control, military anti-unmanned aerial vehicle systems and the like.

The invention relates to a radar identification warning method applied to an electric power line protection area, and relates to the technical field of electric power monitoring. According to the method, radar working parameters are configured by using an environment-corrected radar equation and a genetic algorithm considering a radar coverage range, radar detection accuracy and radar energy consumption; the radar detects a target in a power line protection area to obtain radar point cloud data; in the process, multi-frequency fusion ranging is used for measuring the distance of a target, and the Doppler effect is used for resolving the speed of the target; detecting and repairing abnormal values of the radar point cloud data based on an isolated forest algorithm; inputting the preprocessed radar point cloud data into a pre-trained integrated learning classifier for target identification, and obtaining target category information, speed, distance, environment data and facility operation data as risk assessment data; the risk assessment model assesses a target risk according to the risk assessment data to obtain a target risk value and a risk level probability; and performing graded warning according to the target risk value and the risk grade probability.

The invention discloses a vehicle control method and device, electronic equipment, a storage medium and a vehicle, and relates to the technical field of vehicles. The method comprises the following steps: acquiring multi-modal data of a vehicle based on a long-distance radar array, infrared thermal imaging detection and inertia detection modes; the multi-modal data is fused, three-dimensional space information around the vehicle is generated, the three-dimensional space information around the vehicle comprises at least one obstacle existing in a scanning range, and the scanning range is determined based on a long-distance radar array; performing risk level evaluation on at least one obstacle existing in the three-dimensional space information; and adjusting a vehicle control strategy based on the risk level. Compared with a radar detection mode in the prior art, the method has the advantages that the multi-modal data of the vehicle is collected and processed, the three-dimensional space information is generated, the risk level of the surrounding obstacles is judged on the basis of the three-dimensional space information, more accurate information is provided for a driver, and therefore driving safety is improved.

The invention provides a signal processing method and device, a laser radar and a carrier. The signal processing method comprises the steps that a first signal and a second signal in a first detection period are acquired, the first signal is from a first detection area, and the second signal is from a second detection area; a validity of the first signal is determined based on the second signal. The spatial distance between the first detection area and the second detection area is within a first range; a difference value between a first detection result corresponding to the first signal and a second detection result corresponding to the second signal is within a second range. The first detection area corresponds to the first light-emitting area, the first light-emitting area emits laser in a first detection period, the second detection area corresponds to the second light-emitting area, and the second light-emitting area does not emit laser in the first detection period. The signal processing method can improve the reliability of laser radar detection data.

The invention relates to a fixed-wing unmanned aerial vehicle track anomaly detection method and device and a medium, and the method comprises the steps: building a radar detection noise model based on a radar equation and a signal-to-noise ratio formula; on the basis of the motion equation, simulating the motion process in normal and abnormal states to generate a real track, and adding noise to the real track by using a radar detection noise model to obtain noise-containing simulation track data; constructing a dynamic graph based on the simulated track data; inputting the dynamic graph into a space-time diagram neural network model; the time-space diagram neural network model comprises a diagram attention network layer, a long-short term memory network layer and a linear layer which are connected in sequence; and performing feature extraction and classification on the dynamic graph through a time-space diagram neural network model, and outputting a track anomaly detection result. Therefore, precise detection of tiny track abnormalities of the fixed-wing unmanned aerial vehicle, especially gliding faults and the like, is realized.

The invention provides an ice and snow field snow melting early warning and intelligent snow removal scheduling method, which comprises the following steps: acquiring wet snow water content, snow layer density abnormity and field climate difference data through a multi-source sensor; the method comprises the following steps: performing primary processing on original data acquired by radar detection, infrared imaging and a micro weather station to obtain an initial distribution map of wet snow characteristics and local weather conditions; and according to the corrected wet snow characteristic distribution map, in combination with the initial distribution map and temperature influence factors and melting rate fluctuation characteristics obtained by data analysis of the micro meteorological station, aiming at equipment thrust loss and working surface friction loss, correcting acquisition deviation of multi-source sensor data, and obtaining a dynamic update map of the snow accumulation state in the risk area.

The invention relates to the technical field of low-altitude unmanned aerial vehicle detection, in particular to an unmanned aerial vehicle detection method based on radar infrared visible light multi-mode fusion, which comprises the following steps: S1, respectively inputting an obtained unmanned aerial vehicle infrared image and an obtained visible light image into two visual branches of a double-flow feature extraction network, and extracting scale features by adopting a lightweight convolutional backbone network; s2, acquiring three-dimensional coordinate data of the unmanned aerial vehicle after radar detection and clutter removal processing in real time, and projecting the three-dimensional coordinate data into two-dimensional image coordinates through a camera external parameter matrix; s3, generating a two-dimensional Gaussian distribution probability graph based on the real-time two-dimensional image coordinates, and inputting the two-dimensional Gaussian distribution probability graph as a mask into the radar feature extraction network to extract a radar feature graph; and S4, under the same scale, performing dynamic weighted fusion on the scale features of the infrared branches and the visible light branches to generate a visual fusion feature map. The method greatly improves the capability of detecting the low-altitude unmanned aerial vehicle.

The invention discloses a tumble detection method and tumble detection equipment, and the method comprises the steps: determining whether a target object triggers a tumble detection instruction or not based on a radar echo signal of a target detection region; the target object is an object needing tumble detection; under the condition that a target object triggers a tumble detection instruction, fast Fourier transform of a distance dimension and fast Fourier transform of a Doppler dimension are respectively carried out on radar echo signals, and a multi-frame feature matrix is constructed; the feature matrix is used for representing distribution information of a radar detection target in a distance dimension and a speed dimension, and the radar detection target comprises a target object; and based on the multi-frame feature matrix, determining a tumble detection result of the target object through a tumble detection model. The tumble detection result of the target object can be more accurately determined, and the identification accuracy of the tumble behavior is improved.

The invention discloses a cloud phase fine identification and liquid water and ice water quantitative remote sensing device, which comprises a split-phase cloud water quantitative detection laser radar, a novel temperature and humidity remote sensing system and a millimeter wave cloud radar, and is composed of a laser emission module, a telescope receiving module, a microwave radiation module, three laser radar detection modules and a digital signal processing module. The system is composed of a central control processing module and a terminal server. The invention further discloses a remote sensing method of the quantitative remote sensing device. All-day detection and quantitative inversion of liquid water, ice water, water vapor, aerosol and cloud are achieved through the split-phase cloud water quantitative detection laser radar. A novel temperature and humidity remote sensing system is adopted to realize real-time fine detection of a temperature and humidity profile in the cloud. According to the cloud phase state fine identification and liquid water and ice water quantitative remote sensing device and method provided by the invention, the problems that the quantitative detection precision of the liquid water and ice water content in the cloud is insufficient and the mixed phase cloud identification is difficult in the prior art are solved.

The invention relates to the technical field of geological disaster hidden danger recognition, in particular to a geological disaster hidden danger recognition method based on InSAR and semi-supervised learning, which comprises the steps of dividing hydrological sensitivity of a monitoring area according to a regional underground water unit supply schematic diagram, and setting an InSAR data sampling frequency; acquiring earth surface time sequence displacement data, including accumulated displacement, displacement rate and displacement acceleration, by using a set sampling frequency for preliminarily identifying a potential hidden danger area; geological radar detection is carried out in the potential hidden danger area, underground structure features are evaluated through a geological radar profile map, and a multi-source feature vector is constructed; according to the method, the hydrological sensitivity of the monitoring area is divided according to the regional groundwater unit supply schematic diagram, and the InSAR data sampling frequency is set, so that the problem that the high-risk area is not monitored in time due to the fact that the hydrological sensitivity change is not considered because the conventional monitoring frequency mostly adopts a fixed sampling interval is solved.