39 results about "High resolution radar" patented technology

The invention discloses a high-resolution radar echo extrapolation prediction method fused with satellite data, and the method specifically comprises the following steps: firstly, inputting historical radar echo sequence preprocessing at a previous T moment, including denoising, normalization processing and data set segmentation, and obtaining cleaned data; then, through a deterministic modeling method (SimVP), a fuzzy prediction sequence of a future T duration is obtained, then a variational auto-encoder (VAE) maps an original radar echo image and the fuzzy prediction sequence to a low-dimensional potential space, and two-stage diffusion modeling is carried out on the basis; in the first stage, a space-time converter (ST-Translator) is used to extract space-time evolution characteristics of radar echoes; in the second stage, satellite data at the corresponding time of the previous T moment is input, preprocessing including normalization processing, feature selection and data set segmentation is carried out, cleaned data is obtained, and the influence of the satellite data is dynamically adjusted in the diffusion process by adopting a multi-source fusion denoising network Fsrform so as to make full use of satellite information; and finally, inversely transforming output results of the two stages into a pixel space to obtain a high-resolution radar echo extrapolation prediction result of the future T duration. According to the invention, computing resource consumption can be effectively reduced, and the precision and detail fidelity of short temporary rainfall prediction are improved.

The invention discloses a target detection method based on an image semantic matching algorithm, and the method comprises the steps: carrying out the multi-source data collection through an image sensor, an SAR (Synthetic Aperture Radar) radar and infrared remote sensing equipment which are carried by an unmanned plane, and obtaining a visible light image, a high-resolution radar image and thermal imaging data in a low-light environment; carrying out target identification on different data sources by utilizing an integrated learning method; judging whether the target is a tank or not by adopting a soft voting fusion strategy; after the tank is detected, the crossing unmanned aerial vehicle is dispatched to adjust a flight path, and image data with higher resolution are obtained; a deep learning model is trained based on an image semantic matching technology, semantic analysis is performed on a high-precision image, and weak parts of a target are automatically identified and labeled by matching standard part features. According to the method, the multi-modal source data is fused, the reliability of tank recognition is improved, the weak part of the target is accurately positioned by combining the image semantic matching technology, the method can adapt to a complex battlefield environment, and the intelligent level of battlefield intelligence analysis is improved.

The invention relates to an artificial precipitation enhancement operation area identification method and system based on multi-source data, and relates to the technical field of meteorological intervention operation, and the method comprises the steps: building a multi-resolution dynamic data grid system, and enabling a coarse-grained grid layer to cover the whole operation area, and to be used for bearing the macro data of a satellite cloud picture and a conventional radar; the fineness grid layer is used for receiving and mapping real-time data provided by the high-resolution radar; receiving real-time data of all cloud blocks, mapping the real-time data to corresponding grid layers, and projecting the data collected at different time points to a unified time reference frame to obtain a cloud system state view; predictive interpolation is carried out by using the latest data of the conventional radar and the macroscopic trend of the satellite cloud picture in combination with the motion vector and confidence weighting so as to obtain cloud system information; and forming a visual cloud system three-dimensional view, identifying and tracking a target cloud block with an artificial precipitation enhancement potential, and generating an operation instruction. The method has the effect that the target cloud block with the operation value can be identified and tracked to generate the accurate operation instruction.

A radar apparatus at a vehicle may transmit a set of radar energy that is reflected off an object after which the reflected radar energy may be received at the radar apparatus. When relative motion of a radar apparatus and an object include movement in two different directions, power of the reflected radar signals may be spread out in a manner that makes data associated with the reflected radar signals difficult to interpret. This spreading out of the radar signals can make mappings of the received radar data appear to be smeared or distorted. To compensate for this smearing effect, mappings of this smeared radar data may be compared with curves from which compensation factors may be identified. These compensation factors may allow a processor to perform calculations to generate updated mappings of the radar data that may allow the processor to more accurately identify characteristics of the object.

The disclosed embodiments use a diffusion-based generative model for radar point cloud super-resolution. The disclosed embodiments use diffusion modeled arithmetical operations to generate higher resolution radar point cloud data from lower resolution radar point cloud data.

The invention discloses a three-dimensional radar echo reflectivity variational auto-encoder pre-training method, which belongs to the technical field of meteorological radar data analysis, and comprises the following steps: extracting advanced features from three-dimensional high-resolution radar data through a space encoder based on an attention mechanism; mapping the advanced features into determined probability distribution parameters using a probabilistic encoder; sampling according to the distribution parameters by using a re-parameterization technique to obtain continuous potential variables; mapping the potential variables back to a pixel space by using a probability decoder based on an attention mechanism to complete data reconstruction; and finally, constructing a mixed loss function consisting of a mean square error and KL divergence by utilizing a reconstruction result and a distribution parameter to train the model. According to the invention, by optimizing the network architecture and introducing the attention mechanism, high-quality reconstruction of high-resolution three-dimensional radar data is realized while extremely low video memory requirements and low parameter quantity are ensured, and the practical value of data synthesis and enhancement is remarkably improved.

The invention provides a hectometer-level short temporary rainfall forecasting method based on generative adversarial network downscaling and physical constraint, which belongs to the technical field of weather forecast, and is characterized in that S-band radar jigsaw and high-resolution X-band radar observation data are fused through an adaptive rule to generate a fused radar echo combined reflectivity, and the combined reflectivity is used for forecasting the rainfall of the hectometer-level short temporary rainfall. A super-resolution downscaling model is constructed by using a generative adversarial network, a long-sequence high-resolution radar echo training data set is reconstructed, a deep learning forecasting model is constructed, a high-resolution radar echo sequence is used as input, space-time modeling training is performed by using a composite loss function, and a future radar echo forecasting field is output. The Z-R relation is converted into quantitative rainfall forecast, and finally the quantitative rainfall forecast with the spatial resolution reaching the hectometer level is generated. The interpretability of the model is enhanced through physical constraint, the bottleneck of insufficient hectometer-level resolution training data is effectively solved, and the spatial refinement degree of short temporary rainfall forecasting and the forecasting capacity for a severe convection system are remarkably improved.

The invention relates to a radar scanning system (100) for non-invasive analysis of targets (10), comprising a plurality of radar transceivers (111) arranged in an irregular geometric configuration. The system (100) performs sequential acquisitions with a view to generating a high-resolution radar representation (20). A reconstruction processor coupled to an artificial-intelligence module (140) processes the acquired data in order to detect, classify and analyse objects within the target. The system (100) dynamically adapts to the characteristics of the target (10) and is able to compensate for relative movement between the system (100) and target. The invention is applicable to various fields requiring detailed and non-invasive analysis of internal structures.

The application discloses a kind of high-resolution radar echo extrapolation prediction methods of fusion satellite data, specifically as follows, first, input history radar echo sequence pretreatment of previous T time, including denoising, normalization processing, data set segmentation, obtain cleaned data;Then, by deterministic modeling method (SimVP), obtain the fuzzy prediction sequence of future T length, then variational autoencoder (VAE) respectively original radar echo image and fuzzy prediction sequence are mapped to low-dimensional latent space, and two-stage diffusion modeling is carried out on this basis;For the first stage, utilize space-time converter (ST-Translator) to extract the space-time evolution characteristics of radar echo;Second stage first input corresponding time satellite data of previous T time, pretreatment is carried out, including normalization processing, feature selection, data set segmentation, obtain cleaned data, adopt multi-source fusion denoising network Fsrformer, dynamically adjust the influence of satellite data in diffusion process, to make full use of satellite information;Finally, the output result of two stages is inversely transformed to pixel space, and the high-resolution radar echo extrapolation prediction result of future T length is obtained.The application can effectively reduce the consumption of computing resources, improve the precision and detail fidelity of short-term precipitation prediction.

The invention discloses a lightweight multilayer radar echo reconstruction method based on Gaussian splashing, and relates to the technical field of meteorological radar data processing. The method comprises the following steps: preprocessing and standardizing multi-layer radar echo data; dividing the data into space sub-blocks and independently executing Gaussian initialization; constructing a multi-scale lightweight encoder, and fusing spatial context information of adjacent sub-blocks while extracting local features of the sub-blocks; constructing a customized decoder, and reconstructing the implicit vectors into Gaussian ellipsoid parameters with physical constraints according to an attribute dynamic selection path; executing multi-layer micro rendering to generate a simulation image; and calculating composite loss and back-propagating optimization model parameters. According to the method, through sub-block context coding and parameterized Gaussian representation, the problem of discontinuous sub-block boundaries is effectively solved while the high-resolution radar data storage and calculation cost is remarkably reduced, and high-fidelity and physically reasonable radar echo three-dimensional reconstruction is realized.

A method for the model-based, high-resolution separation of radar targets for a radar sensor, in which the radar sensor initially generates radar data by capturing radar targets by sampling a field of view of the radar sensor. Various model orders are calculated for the number of radar targets with the aid of a grid-based method and are interleaved in one another. A highest model order is specified, and only the highest model order is calculated, so that the lower model calculations are implicitly produced from the calculation of the highest model order.

A system includes a transmitter of a radar system to transmit transmitted signals, and a receiver of the radar system to receive received signals based on reflection of one or more of the transmitted signals by one or more objects. The system also includes a processor to train a neural network with reference data obtained by simulating a higher resolution radar system than the radar system to obtain a trained neural network. The trained neural network enhances detection of the one or more objects based on obtaining and processing the received signals in a vehicle. One or more operations of the vehicle are controlled based on the detection of the one or more objects.

The invention discloses an X / Ka dual-band diffraction-free Airy vortex electromagnetic wave antenna, which comprises a feed source and a reflective array surface, the reflective array surface faces an aperture radiating surface of the feed source and is divided into a central disc region and an external annular region; the reflection array plane comprises a plurality of square ring patches which are periodically arranged and a plurality of cross patches which are periodically arranged, and the square ring patch located in a central disc area is used for performing phase compensation on Ka-frequency-band incident electromagnetic waves emitted by the feed source; the cross patches located in the central disc area and the outer annular area are used for conducting phase compensation on X-frequency-band incident electromagnetic waves emitted by the feed source. According to the invention, the phase of the reflected electromagnetic wave is modulated, so that the antenna has vortex characteristics and Airy beam self-healing and long-distance focusing characteristic capabilities on the incident electromagnetic wave, and Airy vortex beams in different modes have equal divergence angles. The method has a potential application prospect in the fields of large-capacity high-speed communication of spacecrafts and high-resolution radar imaging.

The invention belongs to the technical field of radar signal processing, and particularly relates to a radar communication integrated clutter suppression method, a radar communication integrated clutter suppression system, radar communication integrated clutter suppression equipment and a medium. And a radar communication integrated echo signal model containing clutter, target echo and noise is designed based on the model. The radar communication integrated signal waveform with the low sidelobe characteristic is obtained through iterative solution by designing an optimization problem taking an integral sidelobe ratio matched with a filtering result as a cost function. Meanwhile, a two-stage cascade filter is designed to filter echo signals. And finally, performing communication information compensation on the filtered echo signal, and performing target parameter estimation by adopting a two-dimensional FFT algorithm and a subspace algorithm. The method effectively reduces sidelobe interference, improves the precision of target detection, and is suitable for high-resolution radars and high-speed communication systems.

The application relates to a kind of artificial precipitation enhancement operation area identification method and system based on multi-source data, and relates to meteorological intervention operation technical field, comprising the following steps: establishing multi-resolution dynamic data grid system, coarse granularity grid layer covers entire operation area, for carrying satellite cloud atlas and the macro data of conventional radar;Fine grid layer, for receiving and mapping the real-time data provided by high-resolution radar;Receive all cloud block real-time data and map to corresponding grid layer, project the data collected at different time points to unified time reference frame to obtain cloud system state view;Using the latest data of conventional radar and the macro trend of satellite cloud atlas, combined with motion vector and confidence weighted, predictive interpolation is carried out to obtain cloud system information;Form a visual cloud system three-dimensional view, identify and track target cloud block with artificial precipitation enhancement potential and generate operation instruction.The application has the effect that target cloud block with operation value can be identified and tracked to generate accurate operation instruction.

The utility model belongs to the technical field of radars, and particularly relates to a high-resolution radar target recognition device, which comprises a shell, a cylindrical box, a screw rod, an adjusting assembly, a radar signal transmitting unit, a high-resolution signal acquisition unit, a data processing and analyzing unit and a data transmission interface, the radar signal transmitting unit, the high-resolution signal acquisition unit and the data processing and analyzing unit are electrically connected in sequence, and the data transmission interface is connected with the data processing and analyzing unit; the radar signal transmitting unit, the high-resolution signal acquisition unit, the data processing and analyzing unit and the data transmission interface are all arranged in the shell. The device provided by the utility model is reasonable in design, and solves the problems of inconvenient height and inclination angle adjustment and the like caused by signal emission, acquisition, processing, anti-interference and fixed installation modes of an existing device, thereby realizing high-resolution, high-precision and real-time identification of a target and use flexibility.

The invention relates to an adaptive radar sparse imaging method based on a near-field physical model, and the method is constructed at least based on the following scheme: imaging modeling: at least constructing an imaging problem of a specific distance unit into a linear inverse problem; designing a physical decoupling network architecture, and at least designing a learnable induction strategy and an adaptive threshold; offline training: updating learnable parameters at least according to the obtained total loss to complete model training; and performing online reasoning, and outputting a target image at least based on forward propagation. The problems of model mismatch and imaging failure caused by the fact that an existing deep learning model LISTA cannot adapt to the change of a physical observation model along with the distance in near-field or high-resolution radar application are at least solved.

The invention relates to a microwave frequency testing technology, and aims to solve the problems that an existing microwave frequency measuring method based on Brillouin scattering is limited by the Brillouin gain spectrum width, and the frequency measuring resolution is difficult to improve; the invention discloses a high-resolution microwave frequency measurement technology based on a tunable Brillouin fiber laser, and the technology comprises the steps: outputting two narrow-linewidth optical carriers through a laser source, enabling an unknown signal of an upper branch to serve as pump light to excite Brillouin after intensity modulation, enabling a signal outputted by a vector network analyzer of a lower branch to serve as probe light after modulation, and enabling the probe light to serve as the probe light after intensity modulation. The probe light is selectively amplified after interacting with the Brillouin gain, an electric delay line is introduced into the Brillouin resonant cavity, and stepping tuning of the laser frequency is realized by changing the voltage applied to a medium on the electric delay line, so that a to-be-measured frequency signal is measured at high resolution; while the broadband frequency measurement range is maintained, the frequency resolution of 100 kHz is achieved, and the application potential in the fields of high-resolution radar signal detection and the like is shown.

The present invention proposes a W-band rotating circularly polarized magnetoelectric dipole antenna array, which belongs to the field of antenna technology and is used to solve the problems of narrow axial ratio bandwidth and complex structure of antenna arrays in the prior art. The antenna array includes an antenna radiation structure and a feed transmission structure stacked up and down. Four metal patch modules are etched on the antenna radiation structure and are distributed rotationally symmetrically along the central axis, and a vertical metal through-hole is provided below the antenna radiation structure. The feed transmission structure is a metal plate, and four metal waveguides are milled inside the plate and are rotationally fed with an excitation with a phase gradient. A short-circuit path is provided at the upper port, and a coupling gap is opened. The axial ratio bandwidth is effectively expanded by the rotationally symmetrical arrangement of the feed structure and radiation structure. The present invention can be used for millimeter wave antenna communication and high-resolution radar imaging.

Provided are a device and method for radar image super-resolution. The device includes a memory configured to store at least one instruction and a processor configured to execute the at least one instruction stored in the memory. The processor generates a low-resolution radar image of a target, generates a high-resolution radar image of the target, trains a super-resolution model for performing super-resolution on radar images on the basis of the low-resolution radar image and the high-resolution radar image, and performs super-resolution on a target radar image using the trained super-resolution model.

Disclosed embodiments use diffusion-based generative modeling for the super-resolution of radar point clouds. These embodiments utilize the mathematics of diffusion modeling to generate high-resolution radar point cloud data from low-resolution data.

The invention discloses a frequency-space adaptive digital beam forming method of a high-resolution MIMO radar, and the method comprises the steps: extracting a frequency domain-space phase characteristic from an intermediate frequency (IF) signal of the MIMO radar, generating a multi-frequency-component adaptive guide vector, and carrying out the frequency-space adaptive beam forming of a data matrix of the intermediate frequency signal; and performing fast Fourier transform on each angle unit to realize decoupling estimation of the distance and angle of the target, namely accurately obtaining the distance and angle information of the target. According to the method, the guide vector is adaptively designed, and different components of the broadband signal are independently processed, namely frequency-space adaptive beam forming is realized, so that the target signal is accurately focused into a correct angle unit, and the phenomenon that the target signal is distributed in a series of angle units around a correct angle is effectively improved.

This invention discloses a rapid measurement method for sea surface backscattering coefficient based on airborne high-resolution radar, belonging to the field of sea surface backscattering coefficient measurement technology. The method includes the following four steps: S1, determining the rubbing angle interval and the number of measurements, and performing measurements sequentially; S2, compressing the range pulses of the echo signal to obtain a one-dimensional high-resolution range image of the sea surface that varies over time; S3, calculating the local rubbing angle of each pulse in each range unit of the range image, and classifying them according to small step intervals of the rubbing angle; S4, calculating the sea surface backscattering coefficient for each category, obtaining the measurement results at small step intervals of the rubbing angle. This invention employs the above-mentioned rapid measurement method for sea surface backscattering coefficient based on airborne high-resolution radar, requiring only a few measurements with large step intervals of the rubbing angle to obtain the sea surface backscattering coefficient measurement results at small step intervals of the rubbing angle, which helps reduce the cost and time of sea clutter measurement.

The invention provides a wind field inversion method based on an X-band radar and a numerical mode, and the method comprises the following steps: firstly, taking a three-dimensional wind field predicted based on a 3-kilometer numerical mode of a coarse resolution as an initial field of wind field inversion in a wind field inversion process; then, high-resolution S-band and X-band radar observation data are introduced, the radar reflectivity is subjected to quality control, and radial wind is subjected to speed deblurring processing; then combining the coarse resolution mode data and the high resolution radar data to perform data fusion; introducing a conservation equation as a constraint condition and calculating a loss function in a mode of solving the vertical velocity in the high-precision three-dimensional wind field; and finally, iteratively solving the optimal solution of the loss function based on an optimization algorithm to obtain an inversion wind field. According to the technical scheme, the problem of lack of 1-2 kilometer low-altitude wind field monitoring of the S-band radar is solved.

The invention relates to a method for model-based high-resolution separation of radar targets for a radar sensor, in which the radar sensor first generates radar data by detecting radar targets through scanning a field of view of the radar sensor, wherein different model orders for the number of radar targets are calculated using a grid-based method and are nested within each other, a highest model order is determined, wherein only the highest model order is calculated, so that the lower model calculations result implicitly from the calculation of the highest model order.

The invention relates to a radar scanning system (100) for the non-invasive analysis of targets (10), comprising a plurality of transceiver radars (111) arranged in an irregular geometric configuration. The system (100) performs sequential acquisitions to generate a high-resolution radar representation (20). A reconstruction processor, coupled to an artificial intelligence module (140), processes the acquired data to detect, classify, and analyze objects within the target. The system (100) dynamically adapts to the characteristics of the target (10) and can compensate for relative motion between the system (100) and the target. This invention has applications in various fields requiring detailed and non-invasive analysis of internal structures. Figure to be published with the abstract: Figure 1

This invention discloses an inverse synthetic aperture radar (ISAR) imaging method based on multi-scale synchronous compression transform, which is achieved through the following steps: decomposing the target echo signal into low-frequency rigid body components and high-frequency micro-Doppler components; using coarse-scale synchronous compression to extract the low-frequency main structure, and fine-scale synchronous compression to separate high-frequency micro-motion features; using entropy normalization weights to fuse multi-scale time-frequency features to generate a high-resolution fused spectrum; reconstructing the target image based on the fused spectrum, simultaneously suppressing noise and enhancing micro-motion features. This invention, through multi-scale hierarchical processing and entropy weight fusion mechanism, solves the problems of image blurring and micro-Doppler aliasing caused by acceleration motion, significantly improving the imaging clarity and feature recognition capability of high-speed maneuvering targets in complex scenes, and providing a reliable solution for high-resolution radar imaging.

The present application relates to SAR radar signal processing technical field, specifically to a kind of SAR radar system transmitting time delay stable method, comprising: acquisition radar operating frequency, transmitting pulse repetition frequency, system operating temperature and transmitter power amplifier operating voltage and other operating parameters, and system reference time delay value under nominal operating condition is obtained by table lookup method.The real-time compensation time delay value is obtained by using improved adaptive filter algorithm, combining frequency and pulse repetition frequency variation amount for iterative update, and real-time correction to reference time delay.In the SAR imaging range direction pulse compression stage, according to the time delay value, the radar echo sampling time sequence is adjusted, the compensated pulse compression data is generated, and the imaging processing is completed.The method can quickly track the time delay drift caused by operating condition change, accurately eliminate time delay deviation in key imaging link, effectively suppress the image distortion caused by time delay fluctuation, so that the system can output stable high-resolution SAR radar image under different operating environments.

This application discloses a radar echo spatial downscaling method, apparatus, equipment, medium, and program product, relating to the field of meteorological information processing technology. The method includes: acquiring low-resolution radar echo data to be processed; upsampling the low-resolution radar echo data to be processed through interpolation to obtain high-resolution target echo data; extracting features from the high-resolution target echo data to obtain an initial high-frequency information representation map; and inputting the low-resolution radar echo data to be processed and the high-resolution target echo data into a downscaling model to obtain a high-resolution radar echo field corresponding to the low-resolution radar echo data to be processed. This application enhances the application value of radar echo data in refined weather analysis and severe weather early warning.

The invention provides a high-resolution radar signal processing method and system for building deformation monitoring, and the method comprises the steps: setting a radar parameter according to a monitoring demand, and collecting a reflection echo signal of a building surface; the data are acquired by an ADC to obtain staggered I / Q channel data, and the I / Q channel data are separated and reconstructed to obtain a complex signal; processing the complex signal by adopting an improved Chirp-Z transformation algorithm to obtain a complex frequency spectrum; calculating the amplitude of a complex frequency spectrum, extracting a frequency spectrum index corresponding to the strongest reflection point on the surface of the building through peak detection, and converting the frequency spectrum index into a preliminary distance value of a target relative to the radar; calculating an azimuth angle of the target relative to the radar, and obtaining a two-dimensional coordinate of the strongest reflection point in a relative coordinate system of the radar through coordinate transformation in combination with the initial distance value; the method is used for calculating two-dimensional coordinate position changes of monitoring points on the surface of the building, millimeter-level deformation monitoring is achieved, and the method is suitable for a radar monitoring system.