77 results about "Radar network" patented technology

The invention discloses a multi-mode laser radar networked atmospheric parameter collaborative monitoring system and monitoring method, and relates to the technical field of atmospheric monitoring, and the method comprises the steps: deploying multi-mode laser radar nodes, synchronously collecting multi-source atmospheric parameters for preprocessing, and obtaining a fusion feature matrix; transmitting the fusion feature matrix to an edge computing node through dynamic networking, and aggregating the edge node through federal learning to realize cloud fusion; the method comprises the following steps: constructing a space-time dynamic graph based on laser radar nodes and a space-time dependency relationship between the nodes, decomposing space-time dynamic correlation into a stable item and a non-stable item through space-time decoupling, and updating space-time dynamic graph data by adopting an alternating optimization strategy through online learning; and constructing a prediction model, taking the real-time feature flow obtained by cloud fusion as input, detecting an abnormal event by predicting atmospheric parameters, and performing pollution traceability. According to the invention, high-precision and real-time monitoring of atmospheric parameters is realized through multi-mode cooperative sensing, intelligent fusion analysis and dynamic optimization decision.

The invention provides a large-scale insect and bird migratory flight monitoring system based on a weather radar net, and the system employs a data collection module to obtain S, C and X wave band weather radar monitoring data of the national weather radar net, and a quality control module carries out the quality control of the radar-based data in the monitoring data, and further provides the radar-based data to an echo recognition module to construct recognition features. A classification recognition result is obtained by adopting a recognition model corresponding to the wave band; the inversion quantification module inverts the insect and bird density, the insect and bird speed and the vertical distribution of the movement direction based on the classified and identified insect and bird echoes to obtain an inversion quantification result; and the classification identification result and the inversion quantification result of the single wave band form a local monitoring result. And the visualization module displays a single-band local monitoring result or a multi-band fusion monitoring result. According to the invention, multi-band data can be collected, monitoring results are fused, real-time and efficient monitoring of migration of insects and birds is realized, and the monitoring range is expanded.

The invention discloses a hydrogel phase state identification method and system based on dual-polarization radar networking fusion, and relates to the technical field of hydrogel phase state identification. The method comprises the following specific steps: acquiring dual-polarization radar observation data from a plurality of radar stations; performing data quality evaluation on the dual-polarization radar observation data to obtain qualified data; qualified data are interpolated into a unified grid, networking fusion is performed through an inverse distance weight algorithm, a data set is constructed, and the data set is divided into a training set, a test set and a verification set; constructing a convolutional neural network, training the convolutional neural network by using the training set, and extracting hydrogel particle features in the training set; and after the training set finishes training, verifying the convolutional neural network by using the verification set, carrying out cycle training for multiple times to obtain a phase state identification model, and carrying out hydrogel phase state identification through the phase state identification model. According to the method, a hydrogel classification result with high robustness and accuracy can be obtained, the method does not depend on manually set weight coefficients, and the phase state identification process is objective.

The invention provides a novel spatial micro-motion target identification method based on a radar network system, and the method comprises the following steps: building a target micro-motion model and a signal model, and constructing a data set of an RCS sequence, a time Doppler image and an HRRP sequence for the spatial micro-motion target identification of a networking radar system; constructing an adaptive feature fusion sub-network; xCA and LA-Att modules with linear complexity are introduced, fine-grained features are effectively extracted, self-adaptive weighted fusion of multi-domain features is realized by adopting a CAFM module, and information useful for recognition is effectively enhanced; and designing a time-space modeling sub-network based on BiGRU and double-graph fusion. According to the method, multi-band and multi-view target features can be obtained, and more accurate information is provided for identification of a spatial micro-motion target; according to the method, the feature information of each radar in the heterogeneous radar network is fully utilized, the recognition precision of 96% or above can still be obtained under the condition that the signal-to-noise ratio is 0dB, and the effectiveness and robustness of the method are obvious.

The invention discloses a phased array radar adaptive cooperative scanning method for severe convective weather, and belongs to the technical field of meteorological radar detection. The method comprises a central cooperative control module, a data analysis module and a distributed phased array radar network. Basic scanning is executed through the phased array radars, and the boundary and the core of the severe convective region of interest are identified and drawn in real time through threshold segmentation and a region growing algorithm; the data analysis module extracts characteristic parameters and calculates a dynamic threat index; the central cooperative control module constructs a global state vector and generates a scanning task instruction for each radar by using a multi-agent depth deterministic strategy gradient algorithm; after scanning is executed, the central module fuses observation data to generate a high-temporal-spatial-resolution three-dimensional meteorological data body and calculates a reward value to update algorithm parameters; according to the invention, multi-radar adaptive cooperative scanning is realized, the monitoring precision and efficiency of severe convection weather are improved, and radar resource allocation is optimized.

The application relates to the technical field of digital signal processing, and provides an intelligent data analysis method and system based on a distributed radar network. The intelligent data analysis method based on the distributed radar network comprises the following steps: acquiring working parameters of all radars at present based on a sensor interface; preprocessing the working parameters to generate attribute parameters; determining an utility function representing a radar working effect based on the attribute parameters, determining a dynamic selection strategy according to the utility function, and constructing a radar network topology to collect radar signals; encrypting the radar signals through a blockchain technology to generate encrypted data; and transmitting the encrypted data to a cloud server. Through the construction of the radar network topology, the scheme can adapt to different application scenarios and requirements, improve the flexibility and scalability of the radar network, enhance the data transmission efficiency and security, and realize real-time acquisition, safe encryption and automatic transmission of radar network data.

This invention designs a method for optimizing networked radar station placement based on a genetic algorithm. Under distributed interference conditions, the optimal placement of networked radar stations is crucial. Existing networked radar station placement schemes are generated solely based on constraints and are suboptimal. This invention combines constraints with a genetic algorithm to optimize, calculate fitness expressions, and perform genetic operations to obtain an optimal station population. By using the genetic algorithm to rationally configure the positions of radars within the network, the radar network's detection area can be expanded while effectively monitoring key detection areas, thereby improving the anti-interference capability of the networked radar system.

The application discloses a forward scattering radar echo parameter extraction method based on GNSS, and comprises the following steps: 1, forward scattering signal extraction; 2, using the Rényi entropy of the smooth pseudo-Wigner-Ville distribution to judge whether the forward scattering signal contains a target scattering signal; 3, using a sliding window to analyze the frequency domain characteristics of each signal segment of the target scattering signal, and judging whether a target crossing event occurs; 4, using a smaller sliding window to analyze the signal segment of the crossing event in sections, calculating the time domain statistical characteristic value of the signal segment in the sliding window, and extracting an optimal signal; 5, using the optimal signal to estimate the target crossing Doppler slope; and 6, using the optimal signal to estimate the target crossing time. The application can realize preliminary judgment of the relative position relationship between a target and a baseline, can realize target crossing Doppler slope estimation and target crossing time estimation of a forward scattering radar echo, and can be popularized to a forward scattering radar network of platforms such as the ground, the sea surface and the air.

The application discloses a kind of distributed space-based radar networking tracking methods based on message passing, based on the distributed space-based radar networking of multiple satellite platforms, antenna array, transmitting system, receiving system, radar power etc., the method comprises: the sequence of first target motion state, target visible state, data association and latent variable in joint observation variable are described using factor graph;Based on the connection relationship of variable node and factor node in factor graph, the approximate solution of joint posterior probability density is solved using BP-MF combined approximation method;According to the approximate solution of joint probability density, the target is tracked.The present application is based on the distributed space-based radar networking, combines factor graph and uses BP-MF combined approximation method to convert the solution of high-dimensional posterior probability density fitting into the solution of low-dimensional posterior probability density product, solves the problem of cumulative propagation of estimation error and identification risk in the case of guaranteeing networking performance using distributed networking tracking method, avoids.

The application discloses a heterogeneous networked radar space registration method based on adaptive grid reconstruction, which comprises the following steps: 1, constructing a radar network system model, presetting related parameters of the radar network system, and initializing the related parameters; 2, obtaining a space sampling unit association group of each grid, and merging grids with the same space sampling unit association group into a reconstructed overlapping area; 3, obtaining a space sampling unit association group of each reconstructed overlapping area, and forming a set; according to the set, an average registration rate of a plurality of randomly distributed point targets is obtained, and a local mismatch rate of each radar station and other radar stations is obtained; if the average registration rate is greater than or equal to a preset average registration rate threshold value, and the local mismatch rate is less than or equal to a preset local mismatch rate threshold value, the optimal grid side length is determined; 4, obtaining a calibration point of the reconstructed overlapping area corresponding to the optimal grid side length. The application effectively improves the registration accuracy.

This invention relates to the field of three-dimensional cloud field forecasting technology, and specifically to a spatially scalable forecasting method, system, device, and medium for three-dimensional cloud fields. The method includes: constructing a first basic training set based on preprocessed first historical radar network data; inputting the first basic training set into a model to obtain an initial forecasting model; inputting various specialized historical data into the initial forecasting model to train specialized identification capabilities, obtaining a specialized enhancement model; constructing a second basic training set based on preprocessed second historical radar network data, and processing the second basic training set based on the specialized enhancement model to select and construct multiple specialized training sets; inputting the second basic training set and the multiple specialized training sets into the specialized enhancement model for multi-resolution joint training to obtain a target forecasting model. This method solves the problems of insufficient spatial adaptability and specialized identification capabilities of three-dimensional cloud field forecasting models under complex weather conditions.

The invention relates to the technical field of weather radar networking, in particular to a multiband networking weather radar reflectivity factor puzzle fusion method and system, and the method comprises the following steps: extracting an observation overlapping region of an S-band weather radar network and an X-band weather radar network, and generating mask data containing geographic information; on the basis of the mask data, generating puzzle data on an equal-altitude surface with different S-band weather radar reflectivity factors; based on the mask data, generating jigsaw data on an equal-altitude surface with different X-band weather radar reflectivity factors; and based on a fusion algorithm, fusing the reflectivity factor puzzle data of the S wave band and the X wave band. According to the invention, the networking observation data of the S-band and X-band weather radars are subjected to jigsaw and fusion processing to be processed into a new data product, the collaborative observation result of the S-band and X-band networking weather radars and the low-altitude blind compensation effect can be visually presented, and a data product support is provided for a meteorological department to monitor and early warn weather with strong disastrous conditions.

The application provides a radar network observable behavior online prediction method, device and electronic equipment in a dynamic electronic countermeasure environment. The prediction method comprises: obtaining observation records of a radar network in a dynamic electronic countermeasure process; constructing a discrete observation sequence of each radar node according to the observation records of the radar node in a preset historical observation window, and performing feature embedding on the discrete observation sequence to obtain a continuous feature sequence of each radar node; inputting the continuous feature sequence of each radar node into a trained radar behavior prediction model to obtain a working mode probability distribution and an anti-interference action probability distribution of each radar node at a next moment. The application can accurately predict the observable behavior of the radar network at the next moment without directly obtaining internal decision basis, physical situation or coordination graph of the radar network, and improves the applicability in a non-cooperative observation scene.

The invention discloses a weak cyclostationary signal detection method based on an unmanned aerial vehicle-mounted passive radar network, and the method comprises the steps: firstly building an active radar transmitting signal and unmanned aerial vehicle passive radar receiving signal model, and then building a binary hypothesis model based on cyclostationary generalized likelihood ratio detection through block cyclic matrix approximation; and constructing generalized likelihood ratio test detection statistics and obtaining local detection results of the unmanned aerial vehicles under a colored noise background by using the cyclostationary characteristic of target echoes, finally fusing the local detection results of the unmanned aerial vehicles based on a Bayesian minimum risk criterion at a fusion center, and outputting global detection judgment. And a detection process of cyclostationary generalized likelihood ratio test-Bayesian minimum risk fusion is formed. According to the method, the detection statistics are constructed by using the cyclostationary characteristics of the target echoes, the global detection accuracy and robustness can be improved under the conditions of low signal-to-noise ratio and complex colored noise, and the method is suitable for a multi-unmanned aerial vehicle cooperative passive radar detection scene.

The application provides a weather radar network wind field self-adaptive correction method and system, the method comprises the following steps: according to the radar coverage condition of the multi-radar network in the target area, adopting the local least square method or the three-dimensional variation method based on multi-radar observation to carry out wind field inversion, and obtaining a first wind field; according to the second wind field obtained by the observation equipment in the target area, the first wind field is fused and corrected; based on the digital elevation model data, the terrain forced correction is carried out on the fused and corrected wind field, and the final wind field is obtained. The application can obtain more fine and more physically consistent real-time wind field under the condition of complex terrain and strong convection, and provide more reliable data support for the monitoring, prediction and mechanism research of disastrous weather.

The invention discloses a vision and laser dual-redundancy anti-collision system and an anti-collision method, and relates to the technical field of crane anti-collision, the vision and laser dual-redundancy anti-collision system comprises a data acquisition layer and a data processing layer, the data acquisition layer comprises a plurality of laser radars and industrial cameras, the laser radars acquire three-dimensional point cloud data, and the industrial cameras acquire picture information of multiple visual angles; the data processing layer comprises a camera network, a laser radar network and a 3D detector, the camera network extracts a first feature from the picture information, the first feature comprises a multi-view two-dimensional feature and a multi-view inference-based three-dimensional feature, the laser radar network extracts a second feature from the point cloud data, and the second feature comprises a three-dimensional structure feature; the first feature and the second feature are fused and then input into a 3D detector, and the 3D detector identifies target information and performs system control and / or early warning based on the identified target information. The effects of accurately detecting the moving speed and the moving direction of the moving target and avoiding the obstacle are achieved.

The application discloses an air-ground cooperative detection networking deployment optimization method, which comprises the following steps: firstly, a mathematical model of ground radar detection sensor networking deployment is established; secondly, a detection area is subjected to gridding treatment, and genetic algorithm is used to optimize the deployment of the ground radar detection sensor networking to obtain a Pareto solution; thirdly, the weak index of each grid of the detection area is calculated to obtain a weak matrix; then, the weak matrix is processed by using the breadth-first search thought to extract and sort weak areas; finally, air-based radar deployment tasks are formulated according to the weak areas, and an air-ground cooperative detection networking deployment scheme is finally generated. The application can make up for the poor detection performance of ground radar networking on low-altitude targets such as unmanned aerial vehicles and the insufficient scheduling mobility, improve the air-frequency domain coverage capability of the ground radar networking and the anti-electromagnetic interference and anti-stealth capability, and make the patrol route designation of the air-based radar detection sensor key task more professional and rigorous.

The present application belongs to the technical field of radar signal processing, and proposes a radar networking system power control method and system for multi-target tracking. The method and system are directed to a radar networking system composed of multiple single-base radars performing multi-target tracking tasks, by obtaining the path propagation loss between each radar node and multiple targets, the clutter scattering matrix prior information between each radar node and clutter, and modeling the path propagation loss as a function of the radar transmitting beam and receiving beam weight vector; the signal-to-interference-and-noise ratio of each radar node for the target is calculated; then, a non-cooperative game joint optimization model for antenna power control is established with the minimum maximum antenna power of each radar node as the target and the desired target signal-to-interference-and-noise ratio threshold value as the constraint condition; a distributed iterative algorithm is used to solve the model to obtain the optimal solution, thereby achieving the purpose of improving the radio frequency stealth performance and radio frequency front end stability of the system.

The present application belongs to the technical field of radar detection, and particularly relates to a biological migration monitoring system based on weather radar network IQ data edge computing processing. The system comprises an edge computing platform and a data center processing server, and the edge processing platform is arranged at a weather radar site. The edge computing platform is used for receiving and processing original IQ data of a weather radar at the site, so as to obtain a range Doppler mask that suppresses weather echoes and retains biological echoes. After frequency domain processing of the range Doppler mask, a multi-peak spectrum structured representation method is used to calculate spectrum characteristic parameters, and the calculation results are transmitted to the data center processing server. The data center processing server stores a network classification and identification model, receives the spectrum characteristic parameters to train the network classification and identification model, and the trained network classification and identification model is used to realize biological classification and output.

The application relates to the technical field of radar data processing, and specifically discloses a quantitative rainfall estimation method based on a dual-polarization multi-radar composite technology, which comprises the following steps: obtaining standardized data by preprocessing dual-polarization radar original observation data; calculating and fusing a multi-dimensional quality index: calculating a static environment index and a dynamic physical consistency index, and fusing to obtain a comprehensive quality index; adaptively selecting an optimal rainfall estimation algorithm according to the quality of radar dual-polarization observation data to generate an initial fused rainfall field; dividing multiple quality levels, independently calculating an average field bias correction factor for each quality level, and applying the average field bias correction factor to radar pixels of the corresponding quality level to obtain a final rainfall inversion result. The application realizes source identification and filtering of non-meteorological echoes and abnormal data, avoids the propagation of false information and the amplification of system errors, and improves the reliability and robustness of radar network composition.

The invention discloses a radar rainfall deviation correction algorithm based on combination of probability density matching and a climate state ratio method. The radar rainfall deviation correction algorithm comprises collection and preprocessing of ground station observation data, PDF deviation correction of radar rainfall and climate state deviation correction of radar rainfall. According to the invention, effective utilization of rainfall observation data of a ground meteorological station is realized, deviation correction is carried out on a radar networking estimation rainfall product by using a ground station grid analysis field and a ground-radar climate state proportion analysis field, and the deviation between a radar rainfall estimation value and real rainfall is effectively corrected. According to the method, the discontinuity and systematic deviation of space-time distribution of a fusion product are reduced, the adaptability of the fusion product to complex terrains and rapid evolution precipitation scenes is enhanced, collaborative optimization correction of multi-scale and multi-type deviation is realized, and the reliability and service applicability of a radar precipitation product are remarkably improved.

The application belongs to the technical field of electronic warfare, and particularly relates to a single-station interference resource optimization configuration method. When a single-station system faces multiple radiation sources at the same time, the detection probability of each opponent radar is obtained based on the false alarm probability of each opponent radar and the signal-to-noise ratio of each opponent radar; the detection probability of a single radiation source is calculated based on the relationship among the detection probability of each opponent radar, each false alarm probability and each signal-to-noise ratio, and then the minimum fusion detection probability is found out under the condition that the single-station system allocates different interference resources, the single-station interference resource optimization configuration is performed according to the strategy, and the interference power is reasonably allocated so that the detection performance of the entire radar network reaches the minimum. The single-station interference resource optimization mathematical model is proposed, and the optimization configuration of the single-station interference resource when facing multiple radiation sources at the same time is realized.

The invention relates to the technical field of radar rainfall estimation, and discloses a dual-polarization radar quantitative rainfall estimation method based on deep learning, and the method comprises the steps: collecting dual-polarization radar body scanning basis data, and carrying out the quality control, and obtaining a standardized polarization parameter; constructing and training a hybrid network model based on 3D-CNN + BiLSTM + Attention, and inputting a three-dimensional tensor constructed by a standardized polarization parameter into the trained hybrid network model to obtain an hourly rainfall intensity preliminary estimated value QPE of each radar grid point; positioning target radar grid points, extracting effective data pairs, and calculating a unique correction factor of each radar grid point; and calculating a single scanning height precision estimated value by combining the hourly rainfall intensity initial estimated value QPE of each radar grid point, and performing accumulation according to a preset time length to obtain an accumulated quantitative rainfall estimation product. According to the method, the dual-polarization radar parameters and the deep learning model are fused, correction is carried out in combination with the real-time rain gauge, accurate multi-period regional rainfall products are generated, and reliable support is provided for meteorological monitoring, disaster early warning and the like.

The application discloses a three-dimensional wind field inversion method based on radial velocity two-dimensional filtering and radar network edge wind vector smoothing, and the method comprises the following steps: obtaining radar site geographic information; using a triangulation method to perform radar networking according to the radar site geographic information; performing inversion grid division after the radar networking; obtaining radial velocity data through radar detection and performing interpolation; obtaining radial velocity two-dimensional filtering results; and performing three-dimensional wind field inversion through the inversion grid, the radar site geographic information and the radial velocity two-dimensional filtering results. Compared with the existing three-dimensional wind field inversion algorithm taking the variational method as the core, the application focuses on the optimization of the radial velocity of each radar before the wind field inversion, thereby reducing the calculation burden in the inversion process, optimizing the radar networking method and the interpolation algorithm in the overall three-dimensional wind field inversion, improving the inversion accuracy while reducing the calculation complexity, and being more suitable for the promotion in actual business.

The invention discloses a radar-based wind measurement method for a wind turbine generator, and the method comprises the following steps: S1, deploying and configuring a radar network, and installing airborne laser radars in a plurality of wind turbine generators on a wind power plant; s2, preprocessing local radar data, and converting original observation data of the radar matched with each fan into standardized wind field information which can be used for cooperative control and power prediction of the wind power plant; the problems that in the detection process of an existing radar-based wind measurement method of the wind turbine generator, unification and fusion of multiple radars are inconvenient, wind field sensing precision is easily reduced, blind areas or overlapping area data conflicts exist in some areas, and the detection accuracy is poor are solved. And different radars may generate inconsistent measurement results due to installation positions, angles, calibration differences and the like, and when a unified fusion mechanism is lacked, system deviation is difficult to eliminate, and the overall wind parameter estimation accuracy is affected.

The present invention discloses a networked radar target detection method based on a non-identical signal-to-noise ratio fusion criterion. It mainly solves the problem of poor fusion detection performance of the existing technology in different scenarios of networked radar signal-to-noise ratio distribution. The scheme is: select the probability density function of the expected observed target signal-to-noise ratio distribution and the number of signal-to-noise ratio sampling points; initialize the support signal-to-noise ratio weight vector and the support signal-to-noise ratio matrix; construct a non-identical signal-to-noise ratio fusion criterion; sample the signal-to-noise ratio probability density function to calculate the target detection probability to obtain the objective function for training; maximize the objective function and iteratively update the weight vector and the support signal-to-noise ratio matrix, substitute it into the non-identical signal-to-noise ratio test statistic function to calculate the actual value of the non-identical signal-to-noise ratio test statistic, and use the non-identical signal-to-noise ratio fusion criterion to judge the target based on the actual value. The present invention can achieve a trade-off between the optimal detection performance of each local radar under a variety of signal-to-noise ratio distributions, improve the fusion detection performance of the radar network in different signal-to-noise ratio distribution scenarios, and can be used for radar target detection.

This invention belongs to the field of radar anti-jamming strategy generation, specifically providing an evaluation method for radar anti-jamming decision models based on AHC (Adaptive Harmony Conversion), addressing the problems of static, single-dimensional, and insufficient robustness considerations in existing evaluation systems. This invention constructs a three-level structure—target layer, criterion layer, and indicator layer—covering the three core dimensions of effectiveness, efficiency, and robustness. It adds indicators such as interference type adaptability and training stability, overcoming the single-dimensionality deficiency of existing evaluation systems and achieving a comprehensive evaluation of anti-jamming strategies. Furthermore, by combining the AHP (Adaptive Hierarchy Process) method with a dynamic weight adjustment mechanism, the weights of each criterion layer are adaptively adjusted according to the interference intensity, enabling the evaluation system to adapt to different dynamic interference scenarios and improving the relevance and accuracy of the evaluation results. Based on this, this invention can achieve accurate and comprehensive evaluation of radar network anti-jamming strategies.

The present application relates to radar technical field, especially to a kind of same frequency pulse radar multi-station cooperative tracking method based on phase information interconnection, the method is based on radar network system implementation, the radar network system includes at least two same frequency pulse radars and a control terminal, the method includes the following steps: S10, each pulse radar gathers own phase information, and sends to control terminal;S20, control terminal receives the phase information sent by each pulse radar, and according to phase information, pulse radar quantity, pulse radar's station information and target position information, calculates the phase control deviation value of each pulse radar, and sends each phase control deviation value to corresponding pulse radar;S30, each pulse radar receives the phase control deviation value sent by control terminal, and controls own phase according to the phase control deviation value.By the present application, the same frequency interference problem of pulse radar chain can be fundamentally solved.