137 results about "Radar data processing" patented technology

The invention relates to the technical field of radar data processing, in particular to a method which is suitable for a multi-external radiation source and multi-station system, carries out single-frame and two-frame multi-target measurement value matching under the condition of no bistatic distance information, has good adaptability to the multi-target positioning problem of external radiation source systems of different systems, and is high in positioning accuracy. The bistatic joint multi-target positioning method based on the external radiation source system is beneficial to the processes of multi-target positioning, real-time tracking, track generation and the like, and comprises the following steps: matching multi-source and multi-station angle measurement values to obtain candidate association, and estimating the intersection point position and the speed state of the angle measurement values; approximately constructing a linear equation of bistatic speed and angle measurement value errors, establishing measurement value error estimation of candidate associations and a real target, and removing wrong candidate associations by using an association hypothesis decision; and establishing a cost function according to the measured value error estimation of the candidate association between the two frames and the state mahalanobis distance, and removing the error candidate association between the two frames by using an association hypothesis decision.

Curvelet-based low level fusion of camera and RADAR sensor information is disclosed and includes processing RADAR data corresponding to a scene to generate a RADAR point cloud and processing camera image data corresponding to the scene using a curvelet transform to identify a target of interest in the scene and generate for the target of interest a target type, (x,y) coordinate values, and a curvelet magnitude per decomposition level. If discrepancies exist between (x,y) coordinate values of the RADAR point cloud and the target type, (x,y) coordinate values, and curvelet magnitude per composition level of the target of interest, a portion of the RADAR data processing is repeated to regenerate the RADAR point cloud; otherwise, the RADAR point cloud to a perception stack of a vehicle.

The invention belongs to the technical field of radar data processing, and particularly relates to a GPGPU-based time-varying target echo simulation method and device. Each thread in each Block in each Grid of the GPGPU is responsible for echo calculation of a target in a channel at each moment, and each echo calculation step comprises the following steps: S1, calculating an inherent frequency domain scattering coefficient of the target according to an incident angle of a radar to the target; s2, obtaining an inherent time domain scattering coefficient of the target through inverse Fourier transform; s3, carrying out amplitude-phase modulation on the inherent time domain scattering coefficient of the target; s4, calculating a target response signal based on the modulated time domain scattering coefficient; and S5, determining a time-varying target echo signal according to the target echo frequency, the time delay, the target Doppler frequency and the calculated target response signal. Important support is provided for testing and verification of a radar detection system, and the simulation effect is closer to a real scene.

Embodiments of this application provides a LiDAR data processing method and apparatus, the method comprises: emitting laser beams multiple times; sampling echoes of a laser beam emitted for one time by at least two receiving blocks to obtain sampling data of the at least two receiving blocks for the laser beam emitted for one time respectively, a first receiving block in the at least two receiving blocks is used to sample a laser beam emitted for an Nth time and a laser beam emitted for an (N+1)th time; obtaining a point cloud data of the laser beam emitted for one time; and generating a frame of point cloud data based on the point cloud data of the laser beam emitted for one time corresponding to at least one of the receiving blocks, which can reduce the cost of LiDAR.

The invention relates to a point cloud denoising method of a photon counting laser radar and an intelligent denoising system, and belongs to the technical field of photon laser radar data processing. The technical problems that the performance of a fixed parameter denoising method is seriously unbalanced under different ground feature and complexity scenes, and high denoising rate and high signal fidelity cannot be considered at the same time are solved. Synchronously acquiring an optical image and an original photon point cloud of the target area; identifying the dominant ground feature type of the laser spot area through real-time semantic segmentation; calculating scene complexity based on the point cloud data and determining the grade of the scene complexity; querying a preset two-dimensional parameter mapping table according to the combination of the surface feature type and the scene complexity level, and dynamically obtaining an optimal denoising parameter set; and finally, the parameter set is utilized to drive adaptive denoising processing, and signal points and noise points are distinguished by calculating the local density and direction consistency of the points and constructing a two-dimensional discrimination space. The method is mainly used for real-time processing platforms such as unmanned aerial vehicles, and the point cloud denoising precision, the signal retention rate and the topographic feature fidelity are remarkably improved.

The invention provides a multilayer laser wind finding radar data processing method and system, and relates to the technical field of radar data processing. The method comprises the following steps: acquiring echo signals of each height layer, synchronously acquiring meteorological parameters of visibility, humidity and rainfall intensity, judging weather types according to meteorological conditions, and setting corresponding SNR reference thresholds; dividing a near-surface layer, a middle layer and a high-altitude layer based on the SNR statistical characteristics of the height layer, and implementing threshold difference adjustment; performing threshold dynamic smoothing according to the meteorological change rate; judging the data validity based on the smoothed threshold sequence, and performing graded repair on discrete missing points, continuous missing segments and large-range missing regions; and finally, calculating the confidence coefficient through the SNR threshold value factor and the repair algorithm factor, and outputting a wind field profile with a reliability identifier. And the data utilization rate, the restoration precision and the result credibility under the complex meteorological condition are remarkably improved.

The invention belongs to the technical field of radar data processing, and relates to a ground target elimination method for two-dimensional phased array low-altitude detection, a medium and electronic equipment. The method comprises the following steps: transmitting a single-frame waveform composed of an airspace detection wave position and a ground sidelobe rejection wave position; selecting the detection point with the maximum power as an effective detection point of the airspace detection wave position, and judging the other detection points as ground side lobe detection points and removing the ground side lobe detection points; and rejecting effective detection points with ground sidelobe rejecting wave positions of which the power is higher than that of the effective detection points from the detection points of the airspace detection wave positions. According to the method, the unmanned aerial vehicle batch starting time is greatly shortened, and unmanned aerial vehicle track batch starting is realized; the contradiction between the detection power and the batch starting time is decoupled, and the ground side lobe target is eliminated; the beam resource utilization rate is improved, and the detection response speed of the radar system to the unmanned aerial vehicle target is improved.

The invention discloses a method and device for supporting multi-radar high-resolution networking and a storage medium, and relates to the technical field of radar data processing. The method comprises the following steps: determining a space range of a networking field; according to the spatial range and the target resolution, defining a grid structure of a networking field data body; allocating computing resources for high performance computing; aiming at each radar, pre-calculating a mapping area of the detection range of the radar in the grid structure; loading the original polar coordinate observation data of each radar into the computing resource in parallel; solving a networking value of each grid point in the grid structure layer by layer based on the mapping region and original polar coordinate observation data, and generating a networking result; and post-processing and storing the networking result, and releasing the computing resources. According to the method, the solution is directly solved from the original polar coordinate observation data based on the mapping region, so that the generation and conversion of massive intermediate data are avoided, and the technical bottleneck of huge memory and video memory consumption is fundamentally solved.

The embodiment of the invention provides a near-field MIMO radar data processing method based on multistage background suppression and target enhancement, and is applied to the technical field of radar signal processing. The method comprises the following steps: acquiring a real-time background template and a real-time image frame of a target scene; calculating to obtain differential image data corresponding to the real-time image frame based on the real-time background template and the real-time image frame; performing Gaussian filtering processing on the difference image data to obtain Gaussian filtering image data; performing morphological processing on the Gaussian filtering image data to obtain morphological image data; performing target enhancement processing on the morphological image data to obtain target enhanced image data corresponding to the real-time image frame; and outputting the target enhanced image data. The technical effect of improving the radar imaging precision is achieved.

The invention provides a radar data real-time dynamic rendering method and system, and relates to the technical field of radar data processing, and the method comprises the steps: carrying out the analysis of radar binary stream data, and obtaining equipment state parameters and echo data; wherein the equipment state parameters comprise radar deployment longitude and latitude, a detection range and a current azimuth angle; if the radar deployment longitude and latitude are the same as the previous radar deployment longitude and latitude and the detection range is the same as the previous detection range, obtaining current frame echo data corresponding to the current azimuth angle from the echo data; wherein the previous radar deployment latitude and longitude and the previous detection range are obtained from previous radar binary stream data corresponding to the radar binary stream data; converting the current frame echo data into current texture data; updating an azimuth angle region corresponding to the current azimuth angle in the current rendering map by using the current texture data to obtain a target rendering map; and outputting the target rendering map. According to the method, the radar data can be accurately rendered on the map.

The application discloses a kind of pulse Doppler radar weather cloud clutter automatic filtering methods, belong to radar data processing technical field.The method utilizes the statistical characteristics of weather cloud clutter on radar echo spectrum diagram, and the spatial position and motion characteristics of radar track in monitoring area, realizes the automatic identification and effective elimination of weather cloud clutter, reduces false track, and improves the detection performance of radar to "low, slow and small" target.

The invention provides a radar data processing method and device, electronic equipment and a vehicle, and the method comprises the steps: comparing the echo time corresponding to an echo signal of a reversing radar with the pre-obtained echo time of a license plate after the vehicle is powered on, and when the echo time corresponding to the echo signal of the reversing radar is smaller than or equal to the echo time of the license plate, starting the reversing radar; and discarding the echo signal of the reversing radar corresponding to the echo time. When the echo time corresponding to the echo signal of the reversing radar of the vehicle is smaller than or equal to the echo time of the license plate, the echo signal of the reversing radar corresponding to the echo time is discarded, so that the echo signal generated by the license plate can be effectively filtered, and the influence of the echo signal generated by the license plate on the detection result of the reversing radar is avoided; in addition, a radar data processing strategy does not need to be changed along with the change of the size of the license plate, and therefore the development cost is prevented from being increased, and a large amount of manpower and material resources are prevented from being consumed.

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.

The invention belongs to the technical field of airborne radar data processing, and particularly relates to a target measurement noise adaptive processing method and device based on historical information. The method comprises the steps that S1, historical parameters of a target are acquired according to target four-dimensional measurement information at multiple time points before the current moment, and the historical parameters comprise three-way positions and radial speeds of the target at the multiple time points; s2, fitting the historical parameters of different dimensions to obtain historical standard parameter curves corresponding to the dimensions; s3, calculating the deviation standard deviation of the historical parameters of different dimensions relative to the historical standard parameter curve of the dimension; s4, performing weight assignment on each dimension parameter of the current frame based on the relationship between each dimension parameter value of the target current frame and the deviation standard deviation of the dimension; and S5, correcting each dimension parameter of the current frame through the weight of each dimension. According to the invention, the stability of target tracking is improved.

The present application provides a radar data processing method, a program product, an electronic device and a storage medium. The method comprises: comparing acquired radar data with a baseline value to obtain a difference value; if the difference value is less than a threshold value, reporting the radar data at a first data reporting frequency; if the difference value is not less than the threshold value, reporting the radar data at a second data reporting frequency; and updating the baseline value on the basis of the baseline value and the threshold value, wherein the second data reporting frequency is higher than the first data reporting frequency. An appropriate reporting frequency is selected on the basis of the comparison result between the difference value and the threshold value, so as to automatically adjust the reporting frequency; for non-abnormal situations, unnecessary data transmissions are reduced, thus saving network bandwidth and storage resources; for abnormal situations, the second data reporting frequency which is a higher frequency is used for data reporting, such that the problem of missing data is ameliorated, thus improving the accuracy of reported data. By means of dynamically adjusting a baseline value, the real state of radar data can be more accurately reflected, reducing false alarms caused by a fixed baseline value.

The present application belongs to the technical field of Mars radar vehicle data processing, and particularly relates to a double-scale data fusion imaging method for Mars radar vehicle data, which fuses RIMFAX and RoPeR double-source data to realize collaborative imaging of shallow precision and deep thickness in a depth range of more than 100 meters. Through grid alignment and Gaussian weight transition, stitching artifacts are eliminated, and the processing efficiency and imaging quality are significantly improved. The present application can overcome the defects in the existing Mars radar data processing, such as the single load being unable to consider the detection depth and resolution, and the double-source heterogeneous data being difficult to realize collaborative imaging, and provides a double-scale data fusion imaging method. Through differential filtering, unified grid registration and depth domain weighted fusion of RIMFAX shallow high-resolution data and RoPeR deep large-penetration data, integrated imaging of Mars shallow structure from shallow fine to deep profile is realized.

The application provides a radar low-altitude target intelligent identification method based on multi-dimensional feature fusion and double-mode decision, and relates to the technical field of radar data processing. The method comprises: processing the radar signal, extracting the radar features and motion features of the target; through an iterative feature collinearity elimination method based on the vector angle, a high-quality and low-redundancy feature set is screened out, on the basis of which, a single-cycle identification model and a continuous-cycle identification model based on a machine learning model are respectively constructed, and the hyperparameters are optimized through Bayesian optimization, the discrimination results of the two models are integrated through a weighted fusion strategy, and the final category determination of small unmanned aerial vehicles, medium-sized unmanned aerial vehicles, birds and air balls is output. The method realizes high-precision and high-efficiency low-altitude target identification, has high trajectory identification accuracy, extremely low resource occupation, and has excellent engineering landing value.

The invention discloses an end-to-end filtering and prediction method based on Conformer-MTP, and belongs to the technical field of radar data processing. According to the method, global and local features of a target track are fused by using a Conformer encoder, and a filtering value of a current period and a predicted value of a next period are synchronously output through an MTP decoder. According to the method, a target motion model does not need to be preset, and the tracking precision and generalization ability of the maneuvering target can be effectively improved.

The invention belongs to the technical field of radar data processing, and particularly relates to a multi-subarray radar anti-interference azimuth super-resolution method and device. The method comprises the following steps: S1, obtaining a sub-array level target echo signal S with interference after radar preprocessing, and filtering interference in echo through an adaptive interference suppression matrix to obtain a filtered echo signal y; s2, uniformly dispersing an airspace steering vector of the echo signal y into L angles in a main lobe range to obtain an observation matrix A formed by a group of base vectors; s3, constructing an underdetermined equation set y = Ax, and solving a vector x by using a complex homotopy algorithm; and S4, determining the orientation of the target according to the relationship between the value of the element in the vector x and the threshold value. According to the invention, the target detection and resolution capability of the multi-subarray active phased array radar in an interference environment is improved.

This application belongs to the field of radar data processing technology, specifically relating to a radar data association and detection capability analysis method. The method includes: classifying and parsing received radar detection result data of a batch to be analyzed according to data source; acquiring the spatiotemporal position data of the platform to be analyzed, the target track data output by the platform's radar detection, and the spatiotemporal position data of the test target; identifying the time period where the above three types of data coexist; using the detection time of the radar detection track as a reference, converting the spatiotemporal position data of the platform to be analyzed and the spatiotemporal position data of the test target to the reference time through interpolation; for the selected platform to be analyzed, associating the track corresponding to the test target from the radar detection track; calculating the error of each radar detection index relative to the given actual value of the test target, and graphically displaying the radar detection value, actual value, and error value of the index. This application significantly improves data processing efficiency and evaluation accuracy.

This invention discloses an X-band radar echo attenuation correction method based on bi-branch feature enhancement, belonging to the field of meteorological radar data processing technology. The method includes: acquiring X-band and S-band radar echo data from a weather radar and preprocessing the raw radar echo data; constructing an attenuation correction model, inputting the preprocessed X-band radar echo data into the attenuation correction model, and simultaneously introducing corresponding S-band radar echo data as supervisory information to train and test the attenuation correction model to obtain attenuated X-band data; and comparing the attenuated X-band data with the preprocessed S-band radar echo data. This invention effectively alleviates the error problem caused by radar signal attenuation by jointly modeling global and local features and utilizing a cross-branch feature interaction mechanism to enhance information fusion capabilities, significantly improving attenuation correction accuracy while maintaining the integrity of precipitation structure.

The invention discloses a spatial-temporal feature fusion interruption track association method based on a bidirectional cross attention mechanism, and relates to the technical field of radar data processing and target tracking, and the method comprises the steps: obtaining new and old track segment data before and after interruption; extracting time features of the track by using a gating circulation unit, and extracting space features of the track by using a time convolution network; performing interactive fusion on the time features and the space features through a bidirectional cross attention mechanism, further introducing time step self-attention, and explicitly modeling the importance of different time steps to obtain fused track feature representation; the Euclidean distance between the new track and the old track is calculated based on the fusion features, and correlation judgment is completed through a comparative learning mechanism. According to the method, the problems that spatial-temporal characteristics are not fully utilized and the association performance is reduced in an information sparse or target maneuvering scene in the existing method are solved, and the accuracy and the robustness of the interrupted track association are remarkably improved.

The application discloses an airborne bathymetric radar point cloud completion method, belongs to the technical field of laser radar data processing, and is used for airborne bathymetric radar point cloud completion and comprises the following steps: acquiring original point cloud data, performing tile division and coordinate conversion, extracting ground points and constructing terrain normalized point clouds; calculating local point clusters according to the neighborhood density of original points, acquiring point cluster spatial position information and attribute information, constructing a proxy point set, constructing an input vector and an initial feature vector based on the proxy point, inputting the proxy point set into a neural network encoder, generating fine completion points and fine completion point confidence, calculating fine completion point calculation fusion weights, restoring original coordinates after removing redundant points, and outputting complete point cloud completion results. Through adaptive voxel clustering, terrain normalization and dynamic completion strategy guided by missing score, the application realizes high robustness completion of complex terrain and uneven density areas in the point cloud, and significantly improves the geometric consistency and detail restoration precision of the completion points.

The invention relates to the field of radar data processing, in particular to a multi-area-array radar track fusion method based on a pre-starting mechanism. Fusion processing is carried out on the tracks of the multiple array planes, each array plane respectively processes respective data, the fusion system analyzes the data of each array plane, the fusion system transmits track information parameters of the tracks which are about to enter the area overlapped with other array planes to the corresponding array planes, and the corresponding array planes process the track information parameters so that the tracks can be batched in time. And carrying out flight path tracking, and then carrying out fusion so as to ensure that the flight path is coherent.

The invention belongs to the technical field of radar data processing, particularly relates to a compact ground wave radar track association method and system, aims to solve the problem of fuzzy association of T / R and T-R dual radars in a multi-target dense scene, and generates time-space aligned short-time track segments by acquiring and preprocessing track data of the two radars; constructing a local topological graph with a main target as a center and a neighborhood target as a node for each fragment, and forming a graph sequence pair to be compared according to a time sequence; the graph sequence pairs are input into a twinning GAT-LSTM network, spatial features are extracted through a graph attention network (GAT), time sequence features are fused through a long short-term memory network (LSTM), double-branch high-dimensional spatial-temporal feature vectors are output, and segment-level association scores are obtained through splicing and similarity estimation; and calculating a consistency index based on the score set of all the fragments in the track overlapping region and judging track association. The accuracy and robustness of track association in a target dense environment are improved by combining time and space features of a modeling target.

The application provides a semi-automatic prediction method for ice thawing and freezing distribution under ice suitable for ice radar data, and belongs to the field of ice radar data processing. The method comprises the following steps: generating a base reflectivity change curve, correcting a thawing and freezing screening threshold, automatically detecting a thawing and freezing transition zone, and predicting the ice thawing and freezing distribution under ice. In view of the error problem of the echo power attenuation term, the ice under water in the region is used as a reference to correct the detection threshold of the thawing and freezing of the region. According to the characteristics of the thawing and freezing transition zone, the corresponding features are defined and extracted, the automatic detection of the thawing and freezing transition zone is realized, and more accurate thawing and freezing distribution in the thawing and freezing transition zone can be obtained. Finally, more accurate ice thawing and freezing distribution under ice can be generated, and the method is suitable for different regions and a large amount of ice radar data.

The application discloses a kind of vehicle-mounted millimeter wave radar error correction methods for complex traffic scene, it is related to radar data processing technical field, and stable weak scattering point set Sca is extracted by candidate scattering point set Csp, and the weak point trace in the sheltered area is not ignored by the aid of boundary corresponding set Bdr, so that track keeps continuity and credibility on time series and spatial coordinates;Combined with the track compensation set Com generated by time synchronization error Ets and calibration residual Ecl and applied to track fitting set Fit, the corrected track set Cor obtained can effectively eliminate the drift and misplacement caused by radar sampling delay and installation angle deviation, to correct track set Cor as output result is used for scene reconstruction and target identification, so that vehicles can still obtain time alignment and spatially accurate error correction final track results in complex traffic environment such as night multi-lane highway, rain and fog weather intersection.

The application relates to the technical field of radar signal processing, in particular to a design method of a software-based radar data processing component based on a VSIPL standard, which comprises the following steps: constructing a layered component model composed of a support library layer, an algorithm logic layer and a memory space management layer; performing algorithm-level granularity division on a radar data processing flow, and decomposing the entire radar data processing flow into multiple algorithm-level modules; designing API interfaces of each function based on the layered component model; using a deep performance optimization strategy based on a VSIPL middleware, and uniformly replacing function functions frequently called in the radar data processing flow into VSIPL standard middleware optimized by a bottom-layer instruction set; and based on the multiple algorithm-level modules and actual reuse requirements, performing secondary aggregation, and encapsulating algorithm-level modules that can be reused into composite components. The method improves the efficiency and real-time performance of radar data processing, enhances the flexibility and expandability of the radar data processing component, and reduces development and maintenance costs.

An automotive controller for controlling a heating, ventilation and air conditioning, HVAC, system of a vehicle, the automotive controller configured to: receive radar data representing an internal cabin of the vehicle; process the radar data to identify an obstructed vent of the HVAC system; and output a control signal to the HVAC system for disabling the obstructed vent.

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.