189 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.

The invention discloses a preprocessing and compression method and system for single-photon laser radar data, and belongs to the technical field of single-photon laser radar data processing. According to the method, firstly, a target distance image is collected by means of a single-photon laser radar imaging system, and original data is provided for subsequent processing; thirdly, constructing a frequency-time histogram for each pixel point of the acquired image so as to visually present the distribution condition of photons in the time dimension; then, Gaussian fitting and peak value screening operation is carried out on the histogram of each pixel point, effective photon signal peak values are accurately identified, false signals caused by noise and interference are eliminated, and the data quality is improved. And finally, inputting the photon data corresponding to the screened peak values into a multi-attention three-dimensional single-photon compression neural network, and realizing high-efficiency compression of the single-photon laser radar data by using the strong feature extraction, high-efficiency compression mechanism and flexible output control capability of the network, thereby providing powerful support for the application of the single-photon laser radar technology in multiple fields.

The invention provides a low-altitude multi-source equipment data base fusion method and system based on deep learning, and belongs to the technical field of low-altitude data processing. A multi-modal dynamic strategy generation engine is adopted to analyze multi-dimensional context information such as equipment modality, space environment and task intention and generate an optimal fusion strategy instruction; according to the present invention, adaptive adjustment fusion is carried out according to the current situation, when the GPS signal is interfered, the dependence on the GPS signal is automatically reduced, the weight of other reliable signal sources is enhanced, and when the optical sensor fails in the heavy fog weather, the data processing assembly line is dynamically reconstructed, the image processing micro service is bypassed, and the radar data is preferentially processed. The dynamic self-adaption and toughness anti-interference capability of the low-altitude multi-source data fusion process is improved, various interferences can be effectively resisted, high-precision fusion output is always kept in a complex and changeable low-altitude environment, data source selection and weight are flexibly adjusted according to a real-time dynamic environment and task requirements, and the real-time dynamic environment and task requirements are met. And the accuracy and the response speed of low-altitude multi-source data fusion are improved.

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 provides a radar sensing road monitoring data processing system and method, and relates to the technical field of radar data processing, and the system comprises the steps that a multi-source data collection module collects first radar data in a road field domain and second radar data of a designated local field domain through a satellite radar and an unmanned plane radar; a rockfall scale identification module performs scale feature identification of rockfall according to the second radar data to obtain rockfall scale features; the confidence coefficient analysis module analyzes the satellite radar confidence coefficient of the first radar data according to the rockfall scale features and local historical radar data of a specified local field domain, and performs rockfall scale feature recognition to obtain the rockfall scale features; and the data processing output module performs analysis according to the rockfall scale features, the rockfall scale features and the field historical radar data to obtain identification confidence, and analyzes and compensates the rockfall disaster rate as a monitoring data processing result. The technical problem that dynamic field domain highway disaster early warning reliability is insufficient in the prior art is solved.

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.

The invention provides a data processing method of a laser radar and a laser detection system. The data processing method comprises the following steps: S11, acquiring a frame of point cloud data of the laser radar; s12, determining a set of ground points in the frame of point cloud data; and S13, determining calibration parameters of the laser radar point cloud according to the set of ground points. In the embodiment of the invention, after the ground point set is extracted by using the GPU, the point cloud ground level is calibrated at a high speed, so that the radar 3D point cloud ground is aligned with the ground level of the physical world. According to the embodiment of the invention, the ground point in the 3D point cloud can be quickly obtained, the ground point is drawn by using the GPU, and the laser radar point cloud calibration parameter is obtained by using the projection of the ground point in the preset plane, so that the point cloud of the laser radar can be calibrated to be close to a world coordinate system and be basically aligned with the horizontal plane under the world coordinate system.

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 discloses a processing method and system for large-data-volume radar data, and relates to the technical field of radar data processing.The method comprises the steps that multiple pieces of real-time data used for a target tracking radar are obtained, a data set is constructed and stored, and then BIT information data is used for cleaning the data stored in the data set; locking a tracking target by using the target feature data of any radar, and synchronously obtaining corresponding first target trace point data; after the tracking target is locked, traversing in the data set of the remaining radars to obtain second target trace point data corresponding to the tracking target; calculating the first trace point data, a first variable quantity of the first trace point data in a preset time and second variable quantities of all the second trace point data; and the first variable quantity and all the second variable quantities are processed to obtain a reference variable quantity, so that the tracking of the tracking target is realized. According to the invention, efficient processing of mass radar real-time data can be realized, and improvement of target tracking precision and stability can be realized.

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 present application relates to the technical field of distributed radar data processing, and discloses an asynchronous robust track association method based on temporal encoding and graph representation. The method relates to an intra-track representation module and an inter-track representation module, wherein the intra-track representation module is used for extracting spatio-temporal features among different plots within a track, and the inter-track representation module is used for extracting, in a track representation space, geometric features between tracks from the same sensor. In the method, a general model of a generalized track association task is established. By means of end-to-end training and learning, multi-source track association and broken track association can be simultaneously solved by using one model, and better generalization performance and association accuracy are achieved under an asynchronous condition and a systematic bias.

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 discloses a multi-rotor unmanned aerial vehicle synthetic aperture radar high-frequency vibration error compensation method, which belongs to the technical field of radar data processing, is used for radar signal error processing, and comprises the following steps: establishing a high-frequency vibration error model and a corresponding echo signal model of a multi-rotor unmanned aerial vehicle frequency modulation continuous wave synthetic aperture radar system; analyzing an influence mechanism of the high-frequency vibration error on the imaging quality; the method comprises the following steps of: accurately extracting vibration components by utilizing improved empirical mode decomposition, then estimating vibration parameters by constructing an optimal solution of a correlation function, and finally designing a compensation function and compensating each vibration component one by one through loop iteration. According to the method, the high-frequency vibration error of the frequency-modulated continuous wave synthetic aperture radar system is modeled and analyzed, and the high-frequency vibration error compensation method based on complete ensemble empirical mode decomposition is provided, so that effective compensation of the multi-component high-frequency vibration error is realized, and the suppression effect of a false target caused by the high-frequency vibration error is improved.

The invention provides a data processing method of a laser radar and a laser detection system. The data processing method comprises the following steps: S101, acquiring a frame of point cloud data of the laser radar; s102, for one data point in the frame of point cloud data, obtaining at least two first adjacent data points; and S103, according to the data point and the at least two first adjacent data points, determining whether the data point is a ground point. The embodiment of the invention provides a technical scheme for identifying the ground points in the point cloud, and the technical scheme can be different from a conventional scheme based on a CPU (Central Processing Unit) to extract the ground points, the GPU can be used for accelerating the extraction operation, and the time consumed by preprocessing is reduced. And by adopting a secondary verification scheme, the robustness of ground point confirmation is improved.

A radar data processing device and method is provided. The method generates a radar image map, predicts a region of interest (ROI) based on the generated radar image map, senses radar data with a radar sensor, identifies the sensed radar data based on steering information, adjusts the steering information based on the predicted ROI, and determines direction-of-arrival (DoA) information corresponding to the sensed radar data based on the adjusted steering information. The radar data processing device may locally adjust at least one of a range resolving power, an angular resolving power, or a Doppler velocity resolving power based on the ROI predicted based on a radar image map, and generate an accurate radar data processing result of a major region.

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 invention relates to the technical field of laser radar data real-time processing, in particular to an airborne laser radar data real-time processing platform based on cloud computing. The platform comprises a point cloud data blocking module, a point cloud data screening module and an end cloud fusion calculation module, wherein the point cloud data blocking module realizes dynamic blocking through flight state driving basic setting and point cloud characteristic driving optimization; the screening module constructs a two-dimensional feature evaluation system, divides the point cloud into a local core feature anchor point set Q1 and a cloud auxiliary modeling redundant point set Q2, and dynamically adapts to a screening ratio; and the end-cloud fusion module corrects the deviation of the cloud preliminary model through Q1, embeds local details, and realizes local dynamic update in combination with incremental data. The platform can solve the problems of time delay, resource adaptation and privacy security of airborne laser radar data processing, and is suitable for emergency monitoring, conventional inspection and other scenes.

The application discloses a full waveform decomposition method with an oscillation signal in an airborne laser radar, belongs to the technical field of laser radar data processing, and is used for waveform decomposition and comprises the following steps: reading and converting original data, performing data blocking, selecting a wavelet threshold for denoising of each block of data, constructing a mixed model, selecting initial parameters of the mixed model, performing particle swarm optimization, obtaining optimized parameters, constructing output data of each block of data, splicing all the output data, and completing full waveform decomposition. The application removes almost all high-frequency noises, has a good retention degree for signals, directly performs full waveform decomposition, fits an oscillation signal, reduces the influence caused by the oscillation signal, and extracts distance data of the airborne laser radar. The multi-point search and randomness of the particle swarm make it more possible to find a global optimal solution, the particle updating is naturally parallelized, is suitable for distributed computing, and the calculation speed is accelerated.

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.

Systems and instruction carrying non-transitory processor-readable mediums are provided to facilitate access of radar data that may be scattered or non-uniformly located within a region of memory for further processing of such radar data. An example system includes counters that increment on different dimensions of the memory region, a lookup table, multipliers, an adder, and a wraparound mechanism to access different sets of non-contiguously stored radar data from a region of memory. The wraparound mechanism performs a wraparound operation when a combined address, generated by the adder based on addresses obtained by the multipliers, is greater than a last valid address in the region. The wraparound operation generates a new combined address that is used to fetch data from the memory. A transform operation is then performed on the fetched data.

The application provides a radar time resource management method and system, and relates to the field of radar data processing.The method comprises the following steps: sending control information before the end of the Mth radar frame, normally returning in the M+1 frame, and not needing to start the task of adaptively adjusting the frame period.If the signal is not returned in the M+2 frame, a Kth short frame is added.If the signal is not returned in the Kth frame, a K+1th short frame is added.If the signal is returned in the K+1th short frame, the normal frame task is executed normally.The application provides a radar time resource management method, dynamically adds a plurality of short frames according to actual needs, realizes accurate compensation of the frame period, can guarantee the return of two frames of data, can effectively improve the utilization rate of the radar time resource, and is easy to implement in engineering.

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.