17420results about "Wave based measurement systems" patented technology

The invention discloses a radar target echo characteristic dynamic simulation method based on an unmanned aerial vehicle, and the method comprises the steps: obtaining environment parameters, original signals and state parameters of a system, and generating a synchronous data package; progressive decomposition is carried out through the feature tensor, and target characteristic parameters are generated; carrying out dynamic weight fusion on the optimal parameters and the optimal parameters; constructing a hierarchical characteristic matrix, performing tensor reconstruction and coupling strength calculation, constructing a state space, and performing error compensation to obtain a prediction result and a compensation function; performing real-time optimization, and outputting optimized parameters; constructing a simulation state space, and calculating to obtain a simulation signal; constructing a constraint space, and generating a variational optimization equation; calculating a prediction state; optimizing through the sliding time domain, and generating a control instruction; and performing multi-dimensional evaluation on the control instruction and the analog signal, and outputting a comprehensive score and a feedback signal. According to the invention, the target characteristic simulation precision is improved, real-time simulation of real target motion characteristics is realized, and the processing efficiency is ensured at the same time.

The invention relates to the technical field of radar control, in particular to an anti-interference control method applied to a radar, and aims to solve the problem that an existing radar anti-interference technology is insufficient in anti-interference capability in a complex electromagnetic environment. According to the method, multi-dimensional input data of a radar system are synchronously collected, interference feature extraction is performed on multi-dimensional data streams by using a dual-channel deep network, and interference fingerprint vectors are output to a dynamic feature library in real time. And based on the interference feature priority list, performing cooperative processing, including spatial filtering, frequency domain notch, polarization projection and residual extraction and sparse reconstruction of a target signal. And dynamically adjusting the processing parameters according to the real-time evaluation result. And data distribution processing is carried out on the suppressing interference and the deception interference, and final target trajectory information is output through result fusion. Parallel processing is also realized on the FPGA chip, and the processing speed is improved. On the whole, the anti-interference capability, the stability and the real-time performance of the radar system in a complex electromagnetic environment are improved.

The invention discloses a target echo signal simulation method and system with unmanned aerial vehicle rotor frequency spectrum characteristics, and the method comprises the steps: obtaining an original radar echo signal, carrying out the preprocessing and feature enhancement, and obtaining an enhanced rotor feature signal; sequentially performing dynamic component decomposition, nonlinear phase coupling analysis, adaptive parameter estimation and trajectory prediction to generate probability distribution of a feature trajectory; probability feature space mapping and dynamic feature fusion are carried out to obtain a fusion feature vector; constructing a nonlinear dynamic model, and performing adaptive signal synthesis to generate a synthesized signal; performing quality evaluation on the composite signal to generate a quality evaluation report and an optimization parameter set; based on the quality evaluation report and the optimization parameter set, performing multi-target parameter optimization and monitoring system performance to obtain a performance evaluation result; system parameters are updated, stability analysis is carried out, and a system stability report and regulation and control suggestions are generated. According to the invention, the calculation efficiency and the simulation precision are both improved.

The invention provides a wind measurement laser radar optical automatic calibration method and system based on reinforcement learning, and relates to the technical field of calibration, and the method comprises the steps: obtaining the initial state information and environment parameters of a wind measurement laser radar optical system, constructing a three-dimensional simulation model of the wind measurement laser radar optical system, performing a multi-field coupling simulation test on the three-dimensional simulation model to obtain wind measurement data and wind measurement error data under the current environmental condition, and constructing a mapping model of wind measurement errors and environmental parameters and wind measurement laser radar optical system state parameters; and inputting the wind measurement error data and the mapping model into a preset reinforcement learning model to obtain an optimal calibration parameter set in a current state, generating a calibration instruction of the wind measurement laser radar optical system according to the optimal calibration parameter set, and performing aberration correction by combining online wavefront detection and adaptive optical compensation to obtain a calibration result of the wind measurement laser radar optical system. And automatic calibration of the wind measurement laser radar optical system is completed.

A Light Detection and Ranging (LiDAR) system, comprising: a light emitting unit comprising a light source comprising one or more laser emitters for generating one or more laser beams for producing reflected photons off of a target; a scanning unit comprising a Micro-Electro-Mechanical Systems (MEMS) mirror for changing an outgoing direction of the one or more laser beams; a light receiving unit comprising a MEMS mirror array and a plurality of photon detectors, the MEMS mirror array being configured to deflect the reflected photons to the plurality of photon detectors; and a control system for controlling the LiDAR system, the control system being configured to adjust the MEMS mirror and the MEMS mirror array.

The invention discloses an interference suppression method for a random phase modulation electromagnetic metasurface. The interference suppression method comprises the following steps: constructing a mathematical model for a modulated radar echo signal; wherein a random phase modulation electromagnetic metasurface is installed on the surface of a target, a radar transmitting signal enters the random phase modulation electromagnetic metasurface, and after random time domain phase modulation, a radar echo signal is generated; discrete sampling processing is carried out after digital down-conversion and dechirping are carried out on radar echo signals, and then additional phase information is estimated after self-mixing processing is carried out. Phase compensation is carried out on the radar echo signal after discrete sampling processing under the condition that noise is considered, and then time domain differential transformation and discrete Fourier transformation are carried out; estimating a phase mutation moment by using a subspace algorithm, and reconstructing an interference modulation sequence based on an estimation result; and demodulating random phase interference by using the reconstructed interference modulation sequence, and performing matched filtering processing to obtain an output signal.

A sensor as a service ecosystem is disclosed herein. In use, the system includes functionality for receiving first sensor data at a sensor as a service platform, where the first sensor data corresponds to a first capability level of a first sensor. The system also receives a selection of a sensor upgrade for the first sensor, and configures enhanced sensor capabilities for the sensor upgrade based on the selection. Further, the system sends a sensor update with the enhanced sensor capability from the sensor-as-a-service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensor as a service platform, where the second sensor data corresponds to a second capability level of the first sensor.

Provided in embodiments of the present disclosure are a wireless sensing method and apparatus, a communication device, and a storage medium. The method includes: based on a sensing service. sending a sensing service request to a sensing function. The sensing service request is at least used by the sensing function to configure a sensing parameter of the sensing service.

The invention provides a lightweight AI personnel perception method and system based on a millimeter wave radar, and relates to the technical field of personnel perception, and the method comprises the steps: carrying out the phase compensation, amplitude calibration and bidirectional combined filtering processing of a millimeter wave echo signal, and constructing three-dimensional space point cloud data. Then, constructing a spatial distribution matrix based on the point cloud data, calculating the center position of a point cloud cluster, establishing an adaptive sampling radius to extract point cloud features, and calculating a motion vector and attitude parameters; and finally, inputting the motion vector and the attitude parameter into a double threshold judgment module, judging whether a person exists in the target area, generating a person position thermodynamic diagram based on the center position of the point cloud cluster, and outputting a person perception result. According to the method, the calculation complexity is reduced, the real-time performance and accuracy of personnel perception are improved, and the method is suitable for smart home, security monitoring and other scenes.

The invention discloses a radar signal detection method and system based on multi-task learning of a time-frequency domain feature fusion network, and the method comprises the following steps: building a radar signal mathematical model, and obtaining various types of radar signal sequences through the mathematical model; processing the radar signal sequence to obtain a radar signal I sequence and a radar signal Q sequence; marking the radar signal I sequence and the radar signal Q sequence, and constructing a training data set; constructing a time-frequency domain feature fusion network, and training the time-frequency domain feature fusion network by using the training data set to obtain a signal detection and recognition model; and obtaining a to-be-detected radar signal, and completing radar signal detection by using the signal detection identification model. According to the method, combined modeling analysis is carried out on various radar signals, local features and global features of the signals are extracted by adopting a time-frequency domain feature fusion network from the angle of signal I / Q, and the influence of noise on signal identification is reduced by fusing the features of the frequency domain.

The invention relates to the technical field of bridge detection, and discloses a bridge array radar-based signal processing system and method, and the method comprises the steps: collecting a reflected electromagnetic wave signal at regular time through a bridge array radar system, and carrying out the preprocessing of the reflected electromagnetic wave signal, so as to improve the signal quality. A signal is accurately matched to a bridge model by using the spatial resolution capability of three-dimensional modeling and array radar, and an electromagnetic wave reflection image of an internal structure is formed. And according to the electromagnetic characteristics of the bridge material, setting a strength threshold value and a form identification rule to distinguish normal and defect structures, and analyzing the defect development trend. Furthermore, a deep learning algorithm is adopted to carry out feature extraction and classification on the reflection image, defect positions, types and sizes are automatically identified, and a detection report is generated. And finally, displaying defect space distribution in combination with geographic information (GIS), and making a targeted maintenance plan. According to the invention, the detection precision and efficiency of the internal defects of the bridge are improved.

The invention relates to the technical field of data processing, and discloses a virtual volume scanning data generation method and system for a radar data stream. The method comprises the following steps: performing multi-dimensional decomposition and parallel preprocessing on an obtained original radar data stream to obtain a multi-channel preprocessed data stream; carrying out adaptive feature extraction and dynamic clustering on the multi-channel preprocessed data stream to obtain a time-varying feature cluster; constructing a reconfigurable virtual scanning structure based on the time-varying feature cluster to obtain a self-optimization scanning mode; performing multi-level spatial interpolation and phase reconstruction on the time-varying feature cluster through a self-optimization scanning mode to obtain a preliminary virtual volume scanning data field; performing target feature enhancement and multi-source environment information fusion on the preliminary virtual volume scanning data field to obtain high-simulation virtual volume scanning data; and performing multi-dimensional consistency verification and intelligent format adaptation on the high-simulation virtual volume scanning data to obtain target virtual volume scanning data. According to the method and the device, the efficiency and the accuracy of generating the virtual volume scanning data for the radar data stream are improved.

The invention discloses an unmanned vehicle intelligent obstacle avoidance method and system based on a multi-modal sensor, and relates to the field of automatic driving and intelligent traffic. According to the technical key points of the invention, data of a laser radar and a depth camera are aligned through a space-time registration module; the laser radar point cloud is preprocessed, obstacle point cloud is segmented, fusion clustering is carried out in combination with three-dimensional semantic seed points output by a depth camera, and an obstacle cluster with a semantic tag is generated; and calculating a three-dimensional directed bounding box (OBB) of an obstacle, dynamically adjusting a safety distance according to the speed of the vehicle, expanding the OBB of the vehicle, and carrying out collision detection by adopting a separation axis theorem. When a collision risk is detected, the system sequentially triggers a first-level early warning instruction and a second-level emergency braking instruction, and safe and efficient unmanned vehicle dynamic obstacle avoidance is achieved. According to the method, the advantages of multiple sensors are fully utilized, the obstacle detection precision and the response speed are effectively improved, and reliable technical support is provided for application of the unmanned driving technology in a complex road environment.

The invention discloses an electromagnetic interference suppression method based on time-domain periodic phase modulation. The electromagnetic interference suppression method comprises the following steps: constructing a mathematical model of a radar echo signal which is modulated by a phase modulation surface of a target, namely is interfered; constructing a reference signal, and performing dechirp processing on the interfered radar echo signal by using the reference signal; performing frequency and duty ratio estimation on a frequency spectrum of a modulation signal, namely an interference signal, through a signal obtained after dechirping processing, and reconstructing the modulation signal at a receiving end according to an estimation result; carrying out time delay on the reconstructed modulation signal, and multiplying the reconstructed modulation signal with an interfered ground radar echo signal to obtain a demodulated signal; carrying out carrier removal processing on the demodulated signal, and then carrying out pulse pressure processing through a matched filter; and changing the time delay value of the reconstructed modulation signal, namely carrying out sliding window operation on the reconstructed modulation signal in the time domain, and determining the modulation starting time by utilizing the relationship between the maximum value of pulse pressure processing and the time delay, thereby recovering a real radar echo signal.

The invention belongs to the technical field of weather forecast, and discloses an information fusion dual-polarization radar urban heavy rainfall short-term and imminent forecast deep learning method and system. The invention provides a novel urban heavy rainfall short-term and imminent forecasting method based on meteorological radar data through a research thought of'space-time-space-time fusion 'on the basis of a deep learning technology in combination with a physical mechanism of heavy rainfall. According to the method, heavy rainfall is accurately forecasted by analyzing spatial and temporal distribution characteristics of heavy rainfall, extracting high-dimensional spatial characteristics, developing a super-resolution heavy rainfall spatial characteristic extraction model, combining time evolution characteristics of heavy rainfall and adopting an LSTM and spatial and temporal characteristic fusion model, and the accuracy and timeliness of urban heavy rainfall forecasting are improved.

The invention relates to the technical field of radar signal processing and target detection, in particular to a sea-surface target detection method based on time-frequency feature enhancement and false alarm rate control, which comprises the following steps of: performing multi-scale decomposition on a radar echo signal by adopting a fully adaptive noise set empirical mode decomposition algorithm, extracting an intrinsic mode component to realize signal reconstruction, and extracting an intrinsic mode component; target features are highlighted; performing short-time Fourier transform on the reconstructed signal and the original signal subjected to moving average filtering processing, and mapping a time-frequency map into an RGB image so as to enhance the characterization capability of the time-frequency feature; a convolutional neural network with a false alarm rate control mechanism is designed, a fused time-frequency image is used as input, and accurate target detection and classification identification are realized; target signals and sea clutter interference are effectively separated, the identifiability of target features is remarkably improved, and the identification threshold can be dynamically adjusted.

Disclosed is a method for fast automatic calibration of a phased array based on a residual neural network. A phase setting matrix is set and an amplitude and a phase of a array far-field complex signal are measured with a network analyzer to obtain an amplitude and phase vector of the array far-field complex signal. A real part, an imaginary part, and a magnitude of the far-field measured complex signal value are separated and normalized, and mapped to RGB three-channel image data. Datasets are automatically generated according to a preset amplitude-phase error range by a simulation software, the datasets are proportionally divided into a training set and a test set to be input into the residual neural network for training to obtain a calibration model. Measured data is input into the calibration model for automatic estimation of the amplitude-phase error of the phased array.

The invention discloses a radar signal modulation identification method and device for self-supervised contrast mask reconstruction, and belongs to the technical field of radar signal modulation identification, and the method comprises the following steps: obtaining a radar signal, constructing a radar modulation signal data set containing label data and label-free data, and converting the radar modulation signal data set into a time-frequency image; building a self-supervised contrast mask image reconstruction model comprising an online branch and a target branch; the method comprises the following steps: pre-training a self-supervised contrast mask image reconstruction model by using label-free data, performing data enhancement and random mask operation on a time-frequency image to generate double views, respectively inputting an online branch and a target branch, updating model parameters by jointly optimizing reconstruction loss and contrast loss, and obtaining a pre-training weight; and migrating the pre-training weight to a downstream identification network, freezing part of encoder parameters, and performing fine tuning by using a small amount of labeled data to obtain a radar signal modulation identification model. According to the invention, the radar signal modulation identification precision in a complex electromagnetic environment is improved.

The invention discloses a coal yard inventory scheduling system based on real-time data, which comprises a sensing layer, an edge calculation layer, a digital twinborn layer, a decision-making layer and an execution layer, and is characterized in that the sensing layer is used for acquiring basic data; the edge calculation layer can collect and clean data of the sensing layer and can extract feature vectors of environment and equipment states; a three-dimensional density field can be constructed; the digital twinning layer receives data obtained by the edge calculation layer and constructs a three-dimensional dynamic digital twinning engine; after the decision-making layer receives the order information, a control instruction is generated after calculation; and after receiving the instruction issued by the decision-making layer, the execution layer performs operation according to the instruction. According to the invention, through multi-source real-time data perception, edge computing node rapid processing and digital twinborn simulation, accurate dynamic scheduling and management of coal yard inventory are realized; the digital twinborn layer is set for simulation rehearsal and mechanical analysis, the collapse risk is warned in advance, and the accident rate is reduced.

The invention discloses a sonar target recognition system based on a residual network, and the system comprises a sonar preprocessing module which is used for collecting and preprocessing an original sonar echo signal; the frequency spectrum construction module is used for generating a multi-channel frequency spectrum image; the spectrum enhancement module is used for performing spectrum enhancement processing; the residual feature extraction module is used for extracting sonar target feature representation; the label screening module is used for calculating a label credibility score and constructing a label training set; the joint training module is used for combining the label training set and the real labeling samples to form a joint training set and executing iterative optimization of the SonarResBlock model; and the identification output module is used for performing reasoning identification by using the updated model and outputting a category label of the sonar target. According to the method, multi-class spectrum construction and a residual attention mechanism are fused, accurate sonar target recognition is achieved, and the method has the advantages of being comprehensive in modeling, high in robustness and efficient in training.

The invention belongs to the technical field of radar interference identification, and discloses a radar interference identification method based on LSTM and a variational auto-encoder. According to the method, a joint network of the LSTM and the variational auto-encoder is constructed to encode the time sequence signal to obtain a corresponding feature vector, then the feature vectors of the emission signal and the echo signal are spliced to obtain a combined vector capable of representing the interference type, and then classification learning is performed on the combined vector through the convolutional neural network, so that the interference type can be represented. And identification of interference signals is realized. By means of the capability of the LSTM network to solve the long-term dependence problem and the advantages of the variational auto-encoder in dimension reduction and feature extraction, time sequence data can be effectively processed, and complex distribution of data can be learned; and meanwhile, vectors before and after the interference process are spliced to represent a change relation before and after the signal, so that interference pattern identification under any change can be adapted.

A multipath identification method and apparatus, a vehicle, and a medium. The method comprises: preprocessing an echo signal of a millimeter wave radar, so as to obtain static point cloud data and dynamic point cloud data in the echo signal; clustering the dynamic point cloud data by means of improved DBSCAN clustering algorithm, so as to obtain multi-cluster dynamic data; on the basis of an object reflection boundary parameter and cluster center information in the multi-cluster dynamic data, determining at least one matching set, the matching set containing at least two cluster centers, and the object reflection boundary parameter being determined on the basis of the static point cloud data; and for each matching set, performing identification on the cluster centers in the matching set to obtain a target cluster center, and determining the target cluster center and the point cloud data in a cluster where the target cluster center is located as a multipath object in the echo signal. The method can effectively identify a multipath object, thereby improving the accuracy and robustness of vehicle-mounted millimeter wave radar detection.

The invention relates to the technical field of laser calibration, in particular to a laser optical axis calibration device and method, and the device comprises an optical axis offset detection module, a calibration parameter calculation module, a dynamic adjustment implementation module and a calibration effect evaluation module. According to the invention, the response speed and precision are enhanced by monitoring the output of the laser emitter in real time, evaluating the instant deviation of the optical axis and dynamically calibrating the optical axis, the deviation trend of the optical axis is predicted by using a data analysis technology, the future deviation path is effectively controlled, and non-critical calibration is reduced, so that the long-term performance and stability of equipment are improved; dynamic adjustment measures are adopted, the positions of an angle adjuster and an optical element are finely adjusted, so that high calibration precision is ensured, energy loss and equipment abrasion are reduced, calibration effect evaluation of a vibration sensor provides immediate feedback for operation, transparency of the calibration process is improved, an operator can make a more accurate decision based on detailed data, and the operation efficiency is improved. Therefore, the reliability and the operation efficiency of the calibration task are enhanced.

A radar channel calibration method and apparatus, and a storage medium. The method comprises: acquiring an echo signal fed back by a target corner reflector for a radar test signal and extracting a first array steering vector matrix corresponding to the target corner reflector; establishing a signal model having no channel amplitude-phase error and only having a roll error to form a second array steering vector matrix; constructing a multi-variable optimization problem on the basis of the relationship of the first array steering vector matrix, a radar multi-channel amplitude-phase calibration coefficient, and a first-order Taylor expression of the second array steering vector matrix; and using an iteration-based alternating minimization optimization algorithm to solve an original problem.

The invention discloses a multi-person point cloud human body posture reconstruction method and system based on a millimeter wave radar, and the method comprises the steps: firstly generating a three-dimensional point cloud through radar echo preprocessing, and achieving the noise filtering and target separation through the combination of detection region division and a multi-stage DBSCAN clustering algorithm; secondly, a multi-scale feature fusion neural network is constructed, projection plane interference is isolated through grouping convolution, space details are reserved based on a deep high-resolution module, key feature extraction is enhanced through an improved ResNet18 embedded coordinate attention and channel attention mechanism, and end-to-end human body 23 joint three-dimensional coordinate regression is achieved. The system supports synchronous reconstruction of postures of multiple persons through real-time point cloud classification and parallel reasoning, and the frame rate reaches 30-40 FPS in a complex scene. The method gives consideration to privacy protection and high robustness, and can be widely applied to the fields of smart home, smart health care and security monitoring.

The invention discloses a quality detection system and method for a glue injection laser contour, and belongs to the technical field of quality detection, and the method comprises the steps: obtaining a reflection point cloud signal of a glue injection finished product, constructing a point cloud data stream, setting an image reconstruction method, constructing a glue layer three-dimensional model, synchronously generating a pseudo-color grey-scale map, and achieving the visual representation of the surface texture of a glue layer. A three-layer feature extractor is constructed, a quality primary judgment method is set, features of different scales are fused in a cross-layer mode, the problem of missing detection of complex defects in traditional single-feature detection is solved, basic defects are automatically recognized, and a quality primary judgment result is output; and performing defect judgment on the glue-injected finished product which is unqualified in primary judgment, setting a defect fine judgment method, performing voxel-level segmentation on the three-dimensional image, calculating defect characteristics, and judging whether the defects affect the use performance of the product or not so as to generate a fine judgment result, so that accurate positioning of complex defects is realized, and the defect type identification accuracy is improved.

The invention provides a low-resolution scene deception jamming method for three-channel SAR-GMTI, and belongs to the technical field of radar countermeasure and artificial intelligence. The method comprises the following steps: constructing a deception scene template by utilizing an actually measured data imaging result; obtaining a low-resolution deception scene template through low-resolution processing; obtaining an interference amplitude gain parameter through a construction function and an optimization method; and carrying out amplitude modulation, phase modulation and time delay modulation on the intercepted radar signals in combination with the interference parameters, and then forwarding the signals, so as to generate a vivid false moving target detection result for radar detection. The objective of the invention is to generate a vivid false moving target by reducing the number of scattering points contained in a template and keeping a high correlation technique. Compared with an existing deception method, the method has the advantages that the generated signal is matched with the target signal in parameter, the number of scattering points contained in the false motion deception template is reduced, the calculation complexity can be greatly reduced, and a high-fidelity false motion target with an edge contour is generated.

The invention discloses a high-precision flow online monitoring device and method for a siltation section, and belongs to the technical field of hydrological monitoring. The device comprises a sensing terminal, an edge terminal and a mounting mechanism, the sensing terminal is integrated with an upper and lower section layered speed measurement module, an underwater distance measurement module, a first absolute pressure type water level measurement module and an attitude and temperature sensor, and vertical section flow velocity data is obtained by receiving water body reflection signals in a time-sharing manner; and the dynamic deposition section area is corrected in real time in combination with ultrasonic ranging. And the edge terminal compensates water level measurement data by using atmospheric pressure, and calculates the flow through fusion of a telemetering module. The installation mechanism supports fixed or telescopic installation, and the height of the installation mechanism is automatically adjusted according to the distance between a deposition layer and equipment. The method comprises the steps of dynamically correcting the section, calculating the flow through layered integration and compensating errors of multiple sensors, the problems of large area deviation, high operation and maintenance cost and the like caused by sedimentation in a traditional flow measurement technology are solved, and high-precision online monitoring under the high-sand-content complex flow state is achieved.

The invention belongs to the technical field of electromagnetic confrontation and intelligent decision making, and particularly relates to an electromagnetic situation intelligent confrontation decision making method and system based on deep learning. Threat level evaluation and resource adaptation degree analysis are carried out based on a multi-head self-attention mechanism, and a dynamic electromagnetic confrontation strategy knowledge graph is generated; a game confrontation engine driven by deep reinforcement learning is used for deducing an enemy countering path and a situation evolution result under different interference strategies of our party, interference parameters are optimized through a multi-agent collaborative learning model, weight parameters of a strategy network are updated in real time through online confrontation element learning, and historical confrontation efficiency data and current task constraints are combined to obtain a confrontation result. And dynamically generating a target interference decision sequence and an electromagnetic confrontation efficiency prediction report, and synchronously updating the electromagnetic confrontation strategy library. Therefore, the problems of serious disjunction of deduction results, low real-time antagonism and the like in the prior art are solved.

There is disclosed a method of operating a radio node in a wireless communication network, the radio node being adapted for wireless communication, and being adapted for sensing and / or for radar operation. The method includes transmitting sensing signalling of a second type based on transmission and / or reception of sensing signalling of a first type. The disclosure also pertains to related devices and methods.