10702results about "Wave based measurement systems" patented technology

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.

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 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 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 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 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.

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

The invention discloses a multi-mode SAR interference method and device, and belongs to the field of radar electronic countermeasure, and the method comprises the steps: respectively calculating a product matrix of each interference mode in an interference preparation stage; after entering a reconnaissance stage, intercepting a first section of receiving signal, judging whether the first section of receiving signal is an SAR signal or not, estimating parameters of the SAR signal, and extracting one frame of complete SAR pulse signal as a reference signal; intercepting a second section of receiving signal, and judging whether to enter an interference stage or not according to the PRF; after entering the interference stage, receiving a part of SAR pulse signals and recovering the part of SAR pulse signals into complete SAR pulse signals; multiplying the complete SAR pulse signal by the product matrix to obtain a baseband interference signal; and modulating the baseband interference signal into a radio frequency interference signal and transmitting the radio frequency interference signal. According to the invention, multiple modes of interference such as false target implantation, pseudo radio frequency interference and scattered wave interference can be realized, the interference signal modulation complexity is low, and the requirement of the interference device for the isolation of the transceiving antenna is low.

The invention discloses an atmosphere laser radar data analysis method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing atmosphere laser radar echo signal data, and generating a time series data set; s2, constructing a long sequence prediction model based on Transform, and forming a preliminary prediction result sequence; s3, optimizing model structure parameters and training parameters by using a particle filtering algorithm to obtain an optimized model; s4, using the optimized model to predict current and future moment data, and generating a final prediction sequence; s5, performing residual analysis on the final prediction sequence and the real-time observation data, and identifying abnormal points; and S6, sudden disturbance is detected, particle filtering optimization is restarted, and the prediction model is dynamically updated. According to the invention, by fusing the deep learning long sequence prediction model and the particle filter optimization algorithm, high-precision prediction and abnormal dynamic identification of atmospheric laser radar data are realized.

The invention relates to a quick alignment method for optical coupling equipment, which comprises the following steps of: acquiring real-time position information of a laser transmitting end and a laser receiving end, acquiring relative position data by adopting a fiber grating technology, setting a dynamic compensation algorithm according to a light spot offset and a position deviation value, generating an adjustment parameter through the dynamic compensation algorithm, and aligning the adjustment parameter to the optical coupling equipment. According to the method, the structural design of optical coupling equipment is optimized, a high-precision light spot imaging module and a fiber grating positioning module are integrated, and a multi-point data fusion technology and an optical path alignment model in a dynamic environment are utilized, so that the measurement precision is further improved, and the influence of environmental interference is reduced. According to the invention, the problems of real-time performance and accuracy of light path alignment in kilometer-level laser measurement are effectively solved, and a new solution is provided for the development of a long-distance laser measurement technology.

The invention discloses an alignment method for a vehicle-mounted radar and related equipment, and relates to the technical field of navigation calibration, and the method comprises the following steps: obtaining inertial measurement unit data and horizontal tilt angle sensor data of the vehicle-mounted radar; generating an initial attitude matrix based on the inertial measurement unit data; determining a horizontal attitude data difference based on the horizontal tilt angle sensor data and the initial attitude matrix; and obtaining an alignment result of the vehicle-mounted radar based on the horizontal attitude data difference and a Kalman filtering algorithm. The initial attitude matrix is generated by fusing the inertial measurement unit data and the horizontal tilt angle sensor data, fine alignment is performed by combining the horizontal attitude data difference and the Kalman filtering algorithm, the azimuth angle alignment precision of the vehicle-mounted radar in a dynamic environment is improved, dependence on external satellite signals is not needed, and the accuracy of the azimuth angle alignment is improved. High-precision autonomous alignment can be realized under complex working conditions such as shaking and vibration, meanwhile, the hardware cost and the system complexity are reduced, and the method has wide applicability.

The invention provides a convective cloud rain reduction operation analysis method and system based on deep learning, and is used for obtaining a comprehensive convective cloud radar echo extrapolation data set, carrying out the innovative data processing, precisely generating an extrapolation result, formulating a reasonable operation strategy, and improving the working efficiency. The problems of insufficient data utilization, unscientific operation strategy formulation and the like in the flow cloud rain reduction operation in the prior art are effectively solved, and the accuracy and effectiveness of the operation are remarkably improved. The method comprises the following steps: carrying out sparse sampling and space-time alignment processing on a convective cloud radar echo extrapolation data set of a target area to generate a space-time continuous radar echo extrapolation reconstruction data set; calling a space-time sequence prediction model to perform cyclic radar echo extrapolation processing on the radar echo extrapolation reconstruction data set, and generating an extrapolation result within a preset time range; and generating a convective cloud rain reduction operation strategy based on the extrapolation result, and sending the convective cloud rain reduction operation strategy to a rain reduction operation service system to execute operation processing.

The invention belongs to the technical field of radar signal processing and target identification, and particularly relates to a maritime hybrid target identification method and system based on modal decomposition and reconstruction, and the method comprises the steps: carrying out the variational modal decomposition of a radar echo signal of a maritime hybrid target, and decomposing an original signal into a plurality of intrinsic modal signals; estimating a background noise energy reference and removing noise modals based on the decomposed modal signals, clustering the remaining modals according to the center frequency, and combining the modals with the frequency within the center frequency range into an independent single-target signal which is of the same target and is reconstructed into an independent single-target signal; respectively carrying out time-frequency analysis on each reconstructed single target signal to obtain a corresponding time-frequency diagram, and extracting time-frequency domain features from the time-frequency diagram; and inputting the extracted features into a trained support vector machine classifier to realize automatic identification of the ship target and the floating target. A ship target and a floating target can be distinguished more accurately in a mixture scene, and the stability of tracking identification is improved.

The invention discloses a seabed target sonar detection method based on an adversarial network, and the method comprises the steps: S1, collecting deep sea sonar original data, and carrying out the preprocessing of the data, and obtaining a standardized sonar tensor; s2, taking the standardized sonar tensor as training input, and training an improved StyleGAN-3 model in combination with an environmental parameter vector; s3, calling the improved StyleGAN-3 model, and outputting a weak target feature tensor; s4, constructing a sonar detection network; s5, outputting an optimal detection parameter set; and S6, loading the optimal detection parameter set to the sonar detection network, performing target judgment on the weak target feature tensor, and outputting a target confidence value, a three-dimensional space positioning result and a target motion track. Compared with a traditional method, the method has the advantages that in a deep sea scene, the positioning error and the target trajectory loss rate are greatly reduced, the method has excellent online tracking and dynamic situation awareness capabilities, and a solid technical support is provided for ocean monitoring, security and emergency command application.

The invention provides an underwater sonar target detection system and method. The underwater sonar target detection system comprises an optical fiber hydrophone array module which comprises a plurality of optical fiber hydrophone units, each unit comprises an optical fiber Bragg grating sensor and a sound pressure signal demodulation device, and the optical fiber hydrophone array module is used for collecting underwater sound wave signals in real time and converting the underwater sound wave signals into first electric signals; the sonar transmitting-receiving array module comprises a broadband sonar transmitter, a multi-beam receiver and a signal preprocessing circuit, and is used for actively transmitting a frequency modulation continuous wave signal, receiving a target reflection echo and generating a second electric signal; and the multi-mode signal fusion processing module comprises a high-speed data acquisition card, a self-adaptive noise suppression unit and a time-frequency analysis unit. The underwater sonar target detection system and method provided by the invention have the advantages of relatively high reliability, relatively high detection performance, relatively accurate cross-modal signal fusion and capability of performing intelligent target identification and tracking.

The invention discloses a point cloud plane parameter optimization method based on IPOPT and residual combination. The method comprises the following steps: preprocessing point cloud data; obstacle target self-adaptive removal is carried out on the preprocessed point cloud data; a composite objective function is constructed, a maximum error constraint inequality is constructed, and calculation of plane parameters is realized by using an IPOPT optimization algorithm; a loss function containing a hard constraint penalty term is constructed, and it is ensured that the optimization direction conforms to the constraint through dynamic adjustment and parameter rollback; and a dual-path parameter updating strategy of a positive learning rate and a negative learning rate is adopted to generate a corresponding residual item, and ground plane parameter updating is carried out through parameter decoupling and recombination so as to realize further optimization of point cloud plane parameters. According to the method, accurate point cloud plane parameters can be better calculated, and the anti-interference capability and scene adaptability of plane parameter fitting are integrally improved.

The invention discloses a target detection method and system based on a millimeter wave radar. The target detection method and system are used for realizing accurate detection, positioning and dynamic and static recognition of multiple targets under a complex background. According to the method, a distance-Doppler spectrogram is generated through the technical means of sliding window construction, spectral analysis, clutter suppression and the like, and candidate target points are detected by adopting an SO-CFAR algorithm. Then, determining a target position through high-resolution direction estimation and coordinate transformation, performing spatial clustering in combination with a density-based DBSCAN algorithm, and extracting a target geometric center and a bounding box; in the aspect of target tracking, Kalman filtering is used for predicting and updating the position and speed of the target, and a beam forming technology is used for enhancing a target signal, so that the target recognition stability is improved. And finally, the system performs robust dynamic and static state recognition on the target through a dynamic and static judgment module, so that high precision and robustness of the target detection process are ensured. The method can effectively cope with static background interference and dynamic target changes, and is suitable for target detection and tracking in a complex environment.

The invention relates to the technical field of remote sensing data ocean application, and provides an ocean ship association identification method based on AIS and satellite-borne SAR data and electronic equipment, and the method constructs a full-process technical route from data acquisition, preprocessing, ship target detection to trace point association. The method runs through the whole process from SAR data and AIS trajectory preprocessing, intelligent identification of ship targets in SAR images and optimal matching of plots of the SAR data and the AIS trajectories, and a set of uniform, cooperative and efficient technical links is formed. According to the SAR-AIS fusion monitoring method, accurate alignment of AIS data and SAR imaging time is realized by introducing cubic spline interpolation, a ship target in an SAR image is automatically extracted in combination with a deep learning model, finally, global optimal association between trace points is completed by means of a Munkres algorithm, links from data preprocessing to result output are seamlessly connected, and the precision and efficiency of SAR-AIS fusion monitoring are remarkably improved.

The invention provides an OTFS radar communication integrated signal processing method and system based on time-frequency-space domain joint optimization. According to the method, pilot symbol positions and densities are configured in an OTFS time-frequency grid according to channel time-varying characteristics and bandwidths, and a Doppler index range is set to optimize channel information acquisition; deploying a controllable reflecting surface, constructing a phase modulation matrix based on a target position and communication channel parameters, dynamically adjusting a phase gradient direction, and separating energy of a target echo and a communication signal; within a set Doppler index range, calculating frequency offset estimation, correcting a time domain sampling point of a received signal, and generating a phase correction vector; and positioning a spatial aliasing main interference area, and inhibiting symbol interference and spatial aliasing caused by a high-speed target through frequency domain beam forming weighting and phase rotation compensation in combination with a phase correction vector and a sub-Doppler parameter in the area. According to the invention, time-frequency-space domain cooperative efficient processing of OTFS radar communication integrated signals is realized.

The invention discloses a laser radar multi-gas identification method and system based on deep learning, and relates to the technical field of laser radars, and the method comprises the steps: employing a differential absorption laser radar to emit lasers with different wavelengths, receiving echo signals after gas absorption, extracting multi-domain features, and employing multi-target optimization in combination with feature importance evaluation to screen key features; generating a gas portrait of the potential gas according to the key features, and obtaining the type of the interested gas; constructing a graph structure according to the gas portrait and the gas feature similarity, performing representation learning on the graph structure by using a graph convolutional network, enhancing the graph convolutional network by using an interested gas category, constructing a gas category branch, and outputting a gas category; gas concentration branches are built by adopting a multi-layer sensor, final node representation output by the graph convolutional network is used as input, and each gas concentration is output by using linear regression. According to the invention, the distinguishing capability of small difference gases is improved, and high-precision identification and concentration inversion of multi-component gases are realized.

The invention discloses a heterogeneous task migration-oriented multi-modal large model collaborative optimization method, particularly relates to the field of simulated confrontation, and is used for solving the problem that the decision-making efficiency and the task execution reliability of a multi-modal large model in heterogeneous task migration are reduced due to sudden reduction of sensing dimensions. According to the method, a triple high-frequency feedback mechanism of self-consistent information flow, decision flow and reward flow is constructed through full-link series cross-modal completion, mapping correction, hierarchical tuning, gradient weight and rapid adaptive five-loop closed-loop time-dependent driving; after multi-source observation passes through the implicit vector pool, triggering a boundary to be mapped to a target domain in real time, and self-correcting action advantages along with environment evolution; gradient consistency constraint anchors turn-back and dissipation errors in a convergence domain, and a reward signal is subjected to buffer scheduling to catalyze an experience subtask cluster to perform high-optimal activation and maintain activity; the global collaboration enables the initial migration section to generate a smooth path, the roundabout distance and the energy consumption peak value are converged remarkably, and the heterogeneous task migration efficiency, the decision reliability and the long-term robustness are increased synchronously.

The invention relates to the technical field of semiconductor luminescence, and discloses a manufacturing method of a lens module, a mold, the lens module and a sensor device. The manufacturing method comprises the following steps: S1, arranging at least two cavity structures with upper and lower openings in the bearing part; the upper mold and the lower mold are each internally provided with a mold cavity with an opening in one side. And mold cavities of the upper mold and the lower mold extend into the cavity structure from the upper opening end and the lower opening end of the bearing part correspondingly to be aligned and closed. And a gap is reserved between the upper mold and the lower mold in the mold closing process. And S2, a mold cavity for mold closing is filled with the light-transmitting glue, and the redundant light-transmitting glue flows out through gaps in the peripheries of the upper mold and the lower mold and adheres to the inner wall of the bearing part. And S3, after curing, the upper mold and the lower mold are moved away, and a first lens and a second lens are formed in the two cavity structures respectively. The manufacturing method is simple and high in efficiency, and the manufactured sensor device is simple and stable in structure, good in sealing performance, high in precision and easy to optically design.

The invention discloses a dynamic unmanned aerial vehicle cluster electromagnetic characteristic modeling method and system based on a digital array radar, and the method specifically comprises the steps: setting radar system parameters, building a time-varying motion model of an unmanned aerial vehicle cluster, and obtaining the formation pose distribution of the unmanned aerial vehicle cluster under a global coordinate system; component decomposition is carried out on an unmanned aerial vehicle CAD model, shielding analysis is carried out under the radar sight line constraint condition, time-varying scattering center parameter extraction is carried out according to the dynamic electromagnetic effect between targets, scattering center space distribution of an unmanned aerial vehicle cluster is updated in a time sequence recursion mode, and construction of a time-varying scattering center model is completed; a radar system signal model is constructed according to digital array radar DAR parameters, multi-dimensional dynamic feature extraction and parameter mapping are carried out, digital array radar multichannel signal modulation and unmanned aerial vehicle cluster target radar echo construction are completed, and radar scattering electromagnetic characteristic modeling of a dynamic unmanned aerial vehicle cluster is realized. According to the method, the model description accuracy is improved, and the calculation process is more efficient.

The invention belongs to the technical field of ocean surface flow velocity remote sensing measurement, and particularly relates to a Luoga No.2 SAR (Synthetic Aperture Radar) flow velocity correction method based on Bragg orbit velocity, which comprises the following steps of: verifying the physical correctness of an orbit velocity mechanism relative to a phase velocity mechanism; under a DopRIM framework, a physically consistent sea wave error correction model is established; calibrating parameters of the sea wave error correction model; applying the calibrated sea wave error correction model to along-orbit interference SAR observation data of the Luoma No. 2 satellite, and calculating the radial surface flow velocity and sea wave error of each resolution unit; comparing an output result with a matched reference truth value to evaluate the effectiveness of the sea wave error correction model; comparing the sea wave error correction model with a KaDOP model, and respectively counting and analyzing simulated sea wave errors under different methods; the sea wave error simulation result is visually displayed, and the improvement effect is quantitatively evaluated through in-situ observation data and a KaDOP simulation result.

The invention provides a self-adaptive calibration method, device and equipment for airborne radar and a storage medium, and the method comprises the steps: fusing multi-modal monitoring data, such as radio frequency signals, structural vibration and environmental parameters, through introducing a space-time attention mechanism, and generating unified feature representation; further constructing a digital twinborn body combined with Transform and LSTM based on the characteristic representation, realizing dynamic modeling of a nonlinear performance degradation rule of the radar system, and ensuring that a health index sequence accords with an actual degradation characteristic through physical constraint verification; the fault diagnosis report generated by residual analysis and an interpretable AI technology can realize interpretable positioning of abnormal features in a complex environment, and meanwhile, a diagnosis result is converted into a physical constraint cue word to be input into a large language model to obtain an optimization suggestion and feed back and update digital twinborn parameters, so that a closed-loop evolution mechanism is formed, and the diagnosis accuracy is improved. The key problem that the airborne radar is difficult to realize accurate health assessment and adaptive calibration in a complex environment is effectively solved.