112 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 discloses a multi-extended target tracking method based on radar resource allocation, and the method comprises the steps: constructing a radar network non-cooperative game model according to the joint geometric precision attenuation factor of each extended target, carrying out the allocation of the extended targets of radar tracking, and obtaining an optimal radar tracking target allocation result; a radar network non-cooperative game model is constructed, a distribution problem is modeled as a multi-radar competition and cooperation relationship, and contour characteristics of an extended target are optimized and tracked, so that quicker response is realized, the resource utilization rate is optimized, and the accuracy and real-time performance of target tracking are improved; and after the distributed extended targets are measured by using the radars, constructing a joint association vector according to measurement results of the extended targets, obtaining joint states obtained by tracking the different extended targets by the radars, and performing fusion to obtain fusion results of the different extended targets so as to complete tracking of the multiple extended targets.

The invention discloses a hail early warning method and system based on lightning jump and a support vector machine, and the method comprises the steps: obtaining a combined reflectivity factor, a vertical integral liquid water content and echo top height data through radar networking data, recognizing a severe convective echo region, and calculating a lightning time change rate and a standard deviation thereof; a potential hail event is screened in combination with a lightning jump increase condition, a vertical integral liquid water content maximum value VILmax, a jump increase GVIL and a density VILD are further extracted as feature parameters, and the feature parameters are input into a support vector machine classification model based on historical sample training for secondary judgment, so that the false alarm rate of a traditional 2 sigma algorithm is effectively reduced, and the hail early warning accuracy is improved.

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 radar networking anti-stealth detection performance obtaining method, device and equipment and a medium, and relates to the technical field of radar detection, the method and device are applied to a preset radar networking, the preset radar networking comprises a plurality of basic radar detection units, and the method comprises the following steps: receiving echo signals generated by the plurality of basic radar detection units, obtaining an available observation information group by using the echo signal; performing coordinate estimation of a stealth target on the available observation information group to obtain a target position of the stealth target; obtaining the positioning precision of the target position by using the target position and a preset positioning precision formula; and obtaining the detection power of the preset radar network based on the detection area of each basic radar detection unit. According to the scheme, the anti-stealth detection performance of the radar networking can be determined by acquiring the positioning precision and the detection power.

The invention discloses a multi-radar vital sign monitoring system applied to a fixed crowd place. The multi-radar vital sign monitoring system comprises a horizontal layer 24 GHz radar network, a vertical layer 60 GHz radar array and an auxiliary sensor module. Through three-dimensional collaborative deployment of heterogeneous radars, an improved variational mode decomposition algorithm and a joint probability data association tracking technology are combined to realize separation of breathing, heart rate and behavior characteristics of multiple targets in a dense scene. The system adopts a space-time diagram convolutional network to extract individual gait periodic features, establishes a unique biological feature coding file through dynamic gesture track matching, and completes non-contact identity binding. A multi-band radar chip and a dual-mode communication interface are integrated in hardware design, and a lightweight AI model is deployed in software architecture to realize localized real-time analysis. The embodiment covers nursing home tumble monitoring, hospital postoperative monitoring and kindergarten safety management and control scenes, the radar distance and the beam direction can be dynamically adjusted, and the problems of privacy disclosure and target confusion existing in a traditional monitoring system are solved.

The invention belongs to the technical field of electronic countermeasures, discloses a small approaching type unmanned aerial vehicle flight path deception feasibility evaluation and correction method, and discloses a flight path plan making method for implementing flight path deception interference on a radar by using a small approaching type unmanned aerial vehicle, which comprises the steps of expected flight path feasibility evaluation and expected flight path correction. The advantages of the small approaching type unmanned aerial vehicle are developed to the maximum extent. The method is suitable for flight path deception of a single unmanned aerial vehicle to a single radar and flight path deception of an unmanned aerial vehicle group to a radar net. According to the flight path plan making method for implementing flight path deception on the radar by using the unmanned aerial vehicle, power and speed constraints of the unmanned aerial vehicle are considered, feasibility evaluation is carried out on the designed expected flight path, a set of correction strategies is provided for an infeasible area, it is guaranteed that the corrected flight path is close to the original expected flight path as much as possible, and the flight path deception rate is improved. And the effect of the unmanned aerial vehicle is maximized.

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 invention relates to the technical field of data transmission, and discloses an FPGA-based radar network real-time data transmission system, method, device and medium, the system comprises a network port transmission module, a high-speed interface transmission module and a high-speed optical port transmission module, radar instruction control information of a central machine is transmitted to an FPGA through a network port chip, and the high-speed optical port transmission module transmits the radar instruction control information to the FPGA; the radar state information is transmitted back to the central machine; the high-speed transmission interface is used for transmitting the radar instruction control information in the FPGA to the digital signal processor, the radar instruction control information transmitted to the digital signal processor is subjected to signal processing, radar control parameters are obtained, and the radar control parameters are transmitted to the FPGA through the high-speed transmission interface; the radar control parameters in the FPGA are transmitted to the radio frequency front end through the high-speed optical port, the radio frequency assembly is configured according to the radar control parameters in the radio frequency front end, and echo information of the configured radio frequency assembly is transmitted to the FPGA. According to the invention, the efficiency of radar network data transmission can be improved.

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.

The invention belongs to the technical field of spaceborne distributed radars, and discloses a network optimization method for spaceborne distributed radar multi-node synchronous link construction. The method comprises the steps of establishing a distributed multi-satellite system, performing phase synchronization and improving network stability. Aiming at the problems that the number of distributed radar nodes is large and phase synchronization is needed in cooperative work, in the prior art, on one hand, the more complex the network structure is, the longer the link is, the more synchronization error accumulation is; and on the other hand, when the satellite moves, the positions of the nodes can be changed, or when a certain node is unstable, the whole network can be paralyzed. Compared with the prior art, by dynamically evaluating the signal-to-noise ratio and the mutual shielding condition of multiple satellites, the system can adapt to environmental changes, the connection between radar points is optimized, the radar network is more effectively constructed, and the performance of the network is improved.

A method for operating a cooperative multiple-input multiple-output (MIMO) radar network with a plurality of analog radar sensors and at least one digital radar sensor. The method includes determining the deviation of the transmission time points and transmission frequencies of at least the plurality of analog radar sensors in relation to one another, and adjusting the transmission time points, reception time points, and transmission frequencies of the signals of at least the plurality of analog radar sensors to one another. A method for interference detection and interference avoidance is also described. A cooperative multiple-input multiple-output (MIMO) radar network including a plurality of analog radar sensors and at least one digital radar sensor is also described.

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 invention relates to a multiple-input multiple-output (MIMO) radar network and a method for synchronizing such a multiple-input multiple-output radar network. The method comprises synchronizing at least two radar sensors generated by digital signals having a broadband by transmitting, by one radar sensor, at least two multiplexed frequency modulated signals having a defined frequency offset and receiving, by the other radar sensor, the signals. Through the defined frequency offset, the frequency offset between the radar sensors can be determined or estimated, and the clock generators can be synchronized with each other. The invention also relates to a radar network.

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 invention is suitable for the technical field of underground detection, and provides an unmanned aerial vehicle-mounted full-polarization ground penetrating radar, which comprises an unmanned aerial vehicle system used for determining a ground penetrating radar measuring network and carrying out GPS positioning; the carrying system is used for stabilizing the complete polarization ground penetrating radar system; and the fully-polarized ground penetrating radar system is suspended below the unmanned aerial vehicle system, is used for acquiring fully-polarized data of an underground target body, and comprises a PC port, a network analyzer, a switch controller, a power amplifier, a radio frequency switch and a dual-polarized antenna. According to the invention, complete polarization ground penetrating radar data can be obtained through single measurement, and the data acquisition efficiency is significantly improved; miniaturization and light weight of the unmanned aerial vehicle-mounted full-polarization ground penetrating radar are achieved, the total weight of the system does not exceed 5 kg, rapid large-range underground scanning is facilitated, and the requirement for efficient and accurate detection in complex terrains can be met; remote operation of the ground penetrating radar is achieved through the PC port, and the problem that the ground penetrating radar is difficult to control in the carrying state of the unmanned aerial vehicle is solved.

Method for operating a Doppler multiplexing method (DDM) in multiple-input-multiple-output (MIMO) radar networks, wherein uniquely coded signals are transmitted by means of Doppler multiplexing (DDM) from at least one first (Rad 1 ) and a second radar (Rad 2 ) from a MIMO radar network are convolved with the respective DDM codes and the signals are determined which have a unique assignment to a radar (Rad 1 , wheel 2 ). The uniquely assigned signals are then determined for their occurrence in the monostatic paths, the bistatic path, and their occurrence in all paths using a search algorithm, enabling robust MIMO angle estimation over an arbitrary aperture.

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 discloses a method and device for identifying radar range-velocity deception jamming, which relates to the field of anti-jamming of radar networks. The method determines that the traces originate from different types of deception jamming by using the mutations of the number of traces in the range dimension and the velocity dimension; establishes a spatial recognition feature set; determines the optimized range deception jamming and the first uncertain jamming based on the spatial recognition feature set and a preset spatial decision threshold; establishes a motion recognition feature set; determines the optimized velocity deception jamming and the second uncertain jamming based on the motion recognition feature set and a preset motion decision threshold; establishes a spatial motion recognition feature set; determines the optimized range-velocity deception jamming and the third uncertain jamming based on the spatial motion recognition feature set and a preset spatial motion decision threshold; and makes a comprehensive decision on the first uncertain jamming, the second uncertain jamming and the third uncertain jamming to determine the target range-velocity deception jamming. The present application improves the recognition rate of range-velocity deception jamming.

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.