354 results about "Radar data processing" patented technology

The invention relates to the technical field of intelligent driving, and discloses an intelligent networked automobile multi-modal sensing and decision-making system based on deep learning, and the system comprises a multi-modal data collection module which is used for collecting image data and radar time sequence data around a vehicle through a vehicle-mounted camera and a vehicle-mounted radar sensor, the surrounding environment of the vehicle can be sensed in all directions; the image data processing module is used for processing the image data by using an improved deep learning model and extracting target object feature information in the image; the radar data processing module is used for analyzing the change trend of the radar time sequence data and making an optimal driving decision according to different traffic scenes in combination with the feature information of the target object; and the optimal decision execution module is used for executing an optimal decision action according to the optimal driving decision. According to the invention, high-precision perception and quick response to environment information in a complex traffic scene can be realized, the driving safety is improved, and the traffic efficiency is optimized.

The invention discloses a wind profile radar radial speed quality control and horizontal wind field inversion method. According to the method, through the steps of multi-mode detection splicing, signal-to-noise ratio threshold value quality control, beam consistency inspection, horizontal wind component inversion, space and time continuity inspection and the like, the quality control process of the wind field data is optimized, the precision of the horizontal wind field data is remarkably improved, and particularly, the root-mean-square error and deviation in high-level data are remarkably reduced. The core innovation comprises a mode splicing strategy based on sounding data comparison, dynamic signal-to-noise ratio threshold calculation, threshold setting of beam consistency check and a wind component compensation algorithm when a vertical beam is missing. Through experimental verification, compared with a traditional wind profile radar data processing method, the wind profile radar data processing method has remarkable advantages in data accuracy and stability.

The invention relates to the technical field of radar data processing, and discloses a scene matching guidance system based on a miniature synthetic aperture radar, which comprises a multi-dimensional acquisition module and an intelligent matching module. According to the system, SAR image data of all target areas and SAR image data provided by echo signals in real time are acquired through a multi-dimensional acquisition module and are classified to form a data set, the multi-dimensional acquisition module performs standardization processing and analysis on the data set to generate a digital elevation model, an intelligent matching module analyzes the authenticity of SAR images of each target area, and the authenticity of the SAR images of each target area is analyzed. The method comprises the following steps: generating a corresponding credible index, setting a monitoring period with a fixed time length, analyzing a fluctuation index of real-time signal intensity and a matching index of an SAR image, quantifying data quality and matching potential, dynamically monitoring high image precision, setting a fluctuation threshold value by an intelligent matching module, judging whether the SAR image has a delay problem or not, and judging whether the SAR image has a delay problem or not. And scene matching guidance and track synchronization are carried out according to the credibility index, the matching index and the digital elevation model, and the scene matching guidance is high in applicability.

The invention discloses a lunar radar horizon tracking method based on a dynamic search center, and solves the problems that an existing method is high in dependence on artificial experience and low in processing efficiency, and the false horizon misjudgment rate is high due to lunar soil non-uniformity interference. Comprising the following steps: performing Hilbert transform on a radar signal to extract an envelope; based on historical horizon data, predicting a dynamic search center of a current channel through Gaussian weighted average and determining a search window; screening local maximum value points in the window as candidate points, and calculating signal intensity, smoothness and edge direction scores of the candidate points; selecting an optimal candidate point as a horizon position through multi-feature linear fusion; and according to the lunar soil interface form and the signal-to-noise ratio dynamic adjustment parameters, tracking and outputting a final horizon path one by one. According to the method, historical path constraints and local signal features are fused, the identification precision and robustness of the lunar soil layered interface in the complex electromagnetic environment are remarkably improved, and the method is suitable for radar data processing of lunar exploration tasks such as Chang'e 4 and the like.

The invention discloses a ground penetrating radar small target B-scan identification method and system for underground facilities of a transformer substation, and relates to the field of ground penetrating radar data processing. The problems of false detection and high omission ratio caused by a large number of small target samples in underground facility ground penetrating radar detection in a power grid and the problems that a traditional image processing method depends on manual identification and labeling, and the data processing accuracy and processing speed are difficult to improve are solved. The method comprises the following steps: scanning an underground facility along a motion direction according to a UWB ultra-wideband radar system to obtain a B-scan graph; identifying the B-scan image by adopting a LableImage, and obtaining a characteristic waveform in the B-scan image; a GPR-Bfnet recognition model is constructed, and the GPR-Bfnet recognition model is trained according to the training set; and the trained GPR-Bfnet identification model is used to carry out identification and classification on the characteristic waveform in the B-scan image. The method is applied to the fields of engineering, geological exploration and military.

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.

Described is a way to process radar data for a radar sensor mounted on a vehicle to generate motion spectrum data for estimating ego-motion information of the vehicle. Data samples of each of a plurality of radar return signals received at each antenna element of the radar sensor are generated for each antenna element. Respective Doppler-processed data including a plurality of data values is calculated for each Doppler bin index. In generating a set of motion spectrum data, which comprises a plurality of data elements each calculated for a respective Doppler bin index and a respective spatial bin index, data values of the Doppler-processed data calculated for the Doppler bin index are selected to calculate a covariance matrix. A spectral estimation algorithm, which uses the covariance matrix, can determine a spatial spectrum value for each spatial bin index.

The invention discloses a track initiation method based on an intuitive method and transformer, which belongs to the technical field of radar data processing, and comprises the steps of designing a target motion complex scene, constructing a target motion model, simulating radar detection data information of target motion, and generating measurement data about a target position received by a radar. Calculating speed, acceleration and angle information of a target through measurement data of multiple times, and preliminarily screening possible real track data of the target through a visual method; feature extraction is performed on the preliminarily screened track data, the feature track data is labeled to obtain a track feature data set with a label, and a neural network is trained by using the track feature data set with the label to complete discrimination of true and false target tracks, so that the accuracy of true target track initiation in a complex clutter environment is improved, and the accuracy of target track initiation is improved. And the number of false tracks is greatly reduced.

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 belongs to the technical field of road disease detection, and particularly relates to a visual image and radar image fused road disease detection method, which comprises the following steps: S1, a camera and a laser radar collect road data; s2, preprocessing the collected data of the laser radar; s3, fusing the image information and the radar information; s4, redundant results are removed; and S5, uploading the road disease information. Redundant data in a radar image are effectively reduced by optimizing a laser radar data processing flow, so that the storage pressure of mobile equipment is relieved, and by means of an innovative mode of deep fusion of visual image data and laser radar point cloud data, not only can the road disease type be accurately identified, but also the depth information of the disease can be acquired, and the accuracy of the road disease is improved. And the accuracy and efficiency of road disease detection are greatly improved.

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.

The invention provides a millimeter wave radar waveform data processing method and system, and belongs to the technical field of radar data processing, and the method comprises the steps: carrying out the time-frequency analysis of a plurality of waveform arrays, and obtaining a plurality of image groups and a plurality of micro-Doppler features; the plurality of waveform arrays are obtained by monitoring a target area based on a plurality of millimeter wave radars; generating a target identity label based on the target motion information and the target micro-Doppler feature; the target motion information is obtained by identifying a target person in a target area based on a plurality of image groups, and the target micro-Doppler feature is obtained by calculating based on a plurality of micro-Doppler features; and performing trajectory tracking on the target person based on the target identity identifier. According to the millimeter wave radar waveform data processing method and system provided by the invention, the waveform data of the millimeter wave radar can be analyzed, and the accuracy of personnel trajectory tracking is improved.

The invention relates to the technical field of laser radar data processing, and discloses a laser radar data resolution enhancement method based on an adversarial neural network, which converts low-resolution laser radar data into high-resolution data through adversarial training of a generator and a discriminator, and improves the accuracy of wake flow feature extraction. The generator carries out detail completion and resolution improvement on low-resolution data step by step through a multi-layer convolution module, a deconvolution module and a residual module; the discriminator dynamically optimizes the authenticity of the generated data and ensures that the geometric distribution of the generated high-resolution data is consistent with that of the real data; the generated high-resolution data supports vortex intensity analysis, vortex core position identification and accurate extraction of disturbance area boundaries, provides a three-dimensional reconstruction model of wake flow, and provides reliable support for wake flow interval optimization and flight safety management; according to the method, the accuracy and robustness of wake flow detection are remarkably improved, and a powerful guarantee is provided for flight safety and airspace utilization efficiency.

A single-station single-target flight path data particle filtering method based on a target position relates to the field of radar data processing, and comprises the following steps: sampling a flight path state of a flight target at 0 moment according to prior distribution to obtain a particle set of the flight path state at 0 moment, and predicting the particle set of the flight path state of the flight target at k moment to obtain a particle set of the flight path state of the flight target at k moment; obtaining a predicted value of the observation value of the flight target at the k moment; then tracking and measuring the flight target at the k moment through a radar to obtain an observed value of the flight path state of the flight target at the k moment, updating the weight of the particle set at the k moment, and finally processing flight path data measured by the radar through the steps of resampling, statistical output and iteration. According to the method, the distribution of the particle set can be updated by resampling according to the particle weight, the filtering effect is achieved, the observation error can be obviously reduced, the noise is further reduced, and the measurement tracking effect is improved.

The invention belongs to the field of radar data processing, and particularly relates to a sliding window interval track smoothing method based on interactive multiple models. The method comprises the following steps: calculating a reverse model transition probability; calculating a reverse mixing probability; calculating a state mean value and a covariance of output prediction of a corresponding model; calculating a smooth value of the corresponding model; calculating the mode probability after smoothing; and solving an estimator and a corresponding covariance matrix by using a weighting method, and the like. The method is suitable for improving the flight path tracking precision by using the flight path smoothing technology under the conditions that the radar is long in scanning period, noise exists in measurement, the target motion mode is uncertain and the like, and is suitable for a radar data processing process.

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 discloses a heavy haul railway ballast sinking damage degree judgment method based on a ground penetrating radar, and belongs to the technical field of ground penetrating radar data processing, and the method comprises the following steps: obtaining ground penetrating radar data of a heavy haul railway, and carrying out the preprocessing; performing hierarchical processing on the preprocessed ground penetrating radar data; on the basis of a wave field reverse continuation principle, data matrixes of different layers are introduced to carry out offset and merging processing on the layered ground penetrating radar data, and complete disease offset data are obtained; on the basis of the complete disease offset data, obtaining disease feature data through calculation; and constructing a function relationship for judging the heavy haul railway ballast sinking groove disease degree, and obtaining the heavy haul railway ballast sinking groove disease degree based on the function relationship and the disease feature data. According to the method provided by the invention, the severity of the heavy haul railway ballast sinking groove disease can be more accurately judged.

The invention relates to the technical field of ship navigation, in particular to a multi-mode sensing fusion ship navigation method and system. According to the method, synchronous signal calibration is carried out on multi-path radar equipment, the position and the motion state of a ship are obtained in real time in combination with a quantum positioning system and ship sensor equipment, radar and sonar signals are processed through a radar data processing unit and a sonar detection transducer respectively, and after fusion is carried out through a data fusion processing unit, the ship position and the motion state are obtained. Target data and position information which are updated in real time are generated, gestures and voice commands are recognized through a man-machine interaction system, and finally fused data and routes are displayed on a display module. According to the invention, the precision and reliability of ship navigation are improved, and the robustness of the system and the user experience are enhanced.

The invention relates to the technical field of material pile boundary detection, and particularly discloses an automatic material pile boundary detection system and method for an unattended bucket-wheel stacker-reclaimer. Radar equipment is used for scanning a target material pile to obtain point cloud data of the target material pile, and after coordinate system transformation and filtering processing are carried out on the point cloud data, the target material pile boundary is detected; carrying out local window-based geometric feature extraction on the preprocessed point cloud data by adopting a deep learning algorithm so as to mine local shape information of the target material pile, and carrying out core information-based aggregation analysis on geometric features of the target material pile under each local window; the global shape features and boundary information of the target material pile are captured, and automatic detection of the boundary of the target material pile is achieved through the semantic segmentation technology. By means of the mode, the defects of a traditional radar data processing mode can be overcome, accurate detection of material pile boundary information is achieved, and powerful support is provided for automatic operation of the bucket-wheel stacker reclaimer.

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.

The embodiment of the invention provides a P-band wind profile radar application terminal system which is applied to the technical field of radar data processing. The system is developed based on a Linux system module, the system module comprises a CPU, an RJ45 interface, a reset key, an RTC module, a power switch, a debugging port and a PCIe interface, and the terminal system comprises a data processing and quality control module, a performance monitoring and analysis module, a product processing and display module, a radar control module and a service operation module. Single-time RAD five-beam space consistency check is carried out on acquired RAD data, single-time calculation of wind U, V and W components and multi-time U, V and W time consistency averaging are carried out, and then a quality identifier is determined, so that quality control can be carried out on wind profile radar radial data, and the quality of processed data is ensured.

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.

To provide a workflow for training a radar data processing model with reduced requirements for annotated training data.SOLUTION: A training workflow utilizes an imaging sensor, such as a camera, that is synchronized with a radar sensor to capture a synchronized image and radar point cloud data. The training workflow utilizes, using a pre-trained image model, self-supervised knowledge distillation processing to pre-train a radar data processing model on an unannotated synchronized image and radar point cloud data. The radar data processing model is then fine-tuned using a limited set of annotated radar point cloud data, thereby significantly reducing a burden of human annotation.SELECTED DRAWING: Figure 1

To provide a buried object detection device capable of detecting buried objects more accurately over a wider area, and a buried object detection method.SOLUTION: The buried object detection device includes: a radar data processing unit that converts multiple radar data measured by an underground measuring device into images: a buried object detection unit that detects buried objects from the images of the radar data; an underground feature detection unit that detects a characteristic portion that is different from the buried object in the images of the radar data; an alignment unit that calculates alignment information for multiple radar data alignment based on the detection results from the underground feature detection unit; and a result integration unit that integrates the buried object detection results based on the output result of the buried object detection unit and the output result of the alignment unit.SELECTED DRAWING: Figure 1

The invention provides a point cloud building contour line extraction method based on context geometric global optimization, and relates to the technical field of airborne laser radar data processing, and the method comprises the steps: obtaining airborne laser radar point cloud data; processing the point cloud data by using a progressive triangulation network encryption filtering algorithm, and separating the point cloud data into ground points and non-ground points; obtaining an initial seed point, and extracting a building point; an Alpha Shapes algorithm is adopted to process the building points to obtain building contour points; combining the contour point groups to obtain building contour line segments; constructing an undirected weight map, segmenting the undirected weight map, and distributing a direction angle for each contour line segment; and generating a final building contour line according to the adjusted contour line segment. According to the invention, the collected airborne laser radar point cloud data of the building area is processed in a data driving mode to extract the building contour line, so that subsequent various applications based on the building contour line are facilitated.

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.