46 results about "Source localization" patented technology

The invention discloses a wide-area oscillation source positioning method and system based on physical feature embedding and a graph attention network, and the method comprises the steps: obtaining synchronous phasor data of a power system, carrying out the modal parameter identification of subsynchronous oscillation and / or super-synchronous oscillation based on the synchronous phasor data, constructing a topological graph of the power system, and carrying out the positioning of a wide-area oscillation source. And constructing the oscillation amplitude, the attenuation factor and the phase of each node obtained by identification into a feature vector of each node in a topological graph, inputting the topological graph and the feature vector into a graph attention network, and outputting an oscillation source positioning result, so that high-frequency recording data after an accident does not need to be waited, and the positioning accuracy of the oscillation source is improved only on the basis of synchronous phasor data. The identified modal parameters are used as physical characteristics to be embedded into the graph attention network, and the interpretability of a physical mechanism and the graph structure mining capability of deep learning are fused, so that subsynchronous oscillation and super-synchronous oscillation are accurately positioned.

This invention discloses a GNSS interference source localization method based on ADS-B data. The method involves acquiring ADS-B data, saving data points affected by GNSS interference, and, if no NIC is available, constructing ADS-B data with a NIC. The message data is then sorted by ICAO and timestamp. A diagonal matrix W is calculated based on the ICAO, timestamp, NIC, a table showing the correspondence between NIC and received interference power, and the hyperparameter value τ. A residual vector R is calculated based on the message data and the optimization vector, and the optimized vector is then output. This invention analyzes large-scale aviation ADS-B data, sorts and classifies the original message data to determine whether flights are subject to GNSS interference, and abstracts the interference source localization problem into a least-squares problem. An optimization function is constructed, and an iterative optimization algorithm is used to calculate the possible GNSS interference source locations and transmission power, thus more efficiently locating GNSS interference sources.

This invention discloses a broadband oscillation disturbance source localization method based on compressed sensing and graph convolutional neural networks (GCNs), comprising two stages: offline training and online localization. In the offline training stage, electrical quantities under subsynchronous / supersynchronous broadband oscillation modes are obtained using measurement data from actual systems or simulation examples, constructing a broadband oscillation offline sample library. Compressed sensing is used to encode and compress the electrical quantities in the offline sample library, obtaining a training set and a test set. The constructed GCN localization model is trained using the training set until the test set reaches the target localization accuracy, resulting in a broadband oscillation localization model. In the online localization stage, a substation collects electrical quantity data and compresses and encodes it using compressed sensing technology; the substation data is uploaded to the master station; at the master station, the feature matrix and system adjacency matrix are input into the trained GCN localization model, and the oscillation source location is output. This invention's method is applicable to broadband oscillation disturbance source localization under various operating conditions.

This invention discloses a non-cooperative signal localization method based on radiation source decoupling, belonging to the field of wireless signal processing and spatial positioning. The method includes: transforming multi-source joint localization into multiple independent single-source localization problems by constructing a source-level decoupling theoretical framework. First, the method utilizes multiple receiving nodes to acquire mixed signals; then, it uses a mask-based separation network (MS-Net) to perform blind source separation on the mixed signals, obtaining the decoupled components of each radiation source. This network does not require prior information such as pilot signals; finally, for each decoupled component, a broadband Doppler model considering time-scale effects is used for direct single-source localization. This invention significantly reduces computational complexity, improves model adaptability and localization accuracy in high-speed motion scenarios, and has good engineering application prospects.

The application discloses a multi-modal deep learning source tracing method and system combining magnetoencephalogram and electroencephalogram, relates to the field of artificial intelligence and neuroimaging technology, and inputs real magnetoencephalogram signals and electroencephalogram signals into a source tracing model after preprocessing, predicts the probability of the occurrence of corresponding regional sources, combines an imported source partition distance matrix, and obtains a final source tracing result; the training process of the source tracing model is as follows: a generative adversarial network is constructed to generate a multi-modal neuroelectrophysiological data set; the multi-modal neuroelectrophysiological data set is input into a double-branch structure residual network to perform stage hierarchical extraction and decoupling on the magnetoencephalogram signals and the electroencephalogram signals respectively; a multi-scale convolution module is used to extract features at different stages, the extracted features are input into a classifier after being fused, and a loss function is defined to update trainable parameters of the source tracing model; and the source tracing method improves the accuracy and generalization ability of source positioning.

The application discloses a kind of deep learning brain electromagnetic source positioning method and system based on residual iteration, comprising: S1, current electromagnetic signal is obtained;S2, based on current electromagnetic signal, obtain preliminary source positioning result;S3, based on preliminary source positioning result, obtain sparse source positioning result;S4, based on sparse source positioning result, obtain maximum intensity activation source;S5, update current electromagnetic signal, repeat S2-S5 until reach preset condition, obtain final source positioning result. Carry out preliminary source positioning using sLORETA, provide a more rough brain source positioning result. Then the result of sLORETA is repaired using neural network, to restore the sparse source of accurate position, and as far as possible restore the intensity of activation source. The application comprehensively sLORETA's fast calculation and preliminary positioning ability, and the high-precision repair capability of neural network, so as to realize more accurate, more stable result in source positioning.

The invention provides a multi-detector radioactive source positioning method and system based on a particle filter algorithm, and the method comprises the following steps: defining a to-be-monitored physical space as a search region, and building a radiation measurement model; randomly generating a particle set in the search area, wherein each particle comprises assumed radioactive source position coordinates and estimated activity parameters; a mobile platform carrying a plurality of radiation detectors is controlled to move in the area, the total count value of actual measurement of each detector is obtained, and the weight of each particle in the particle set is calculated by using the radiation measurement model and the actual measurement values; acquiring an environment background count value, calculating a ratio of an actual measurement value to a background value, and only when the ratio exceeds a preset signal-to-noise ratio threshold value, performing resampling on the particle set; judging whether the particle set meets a positioning ending condition or not, and if yes, determining a radioactive source positioning result by using the particle parameter with the highest weight and outputting the radioactive source positioning result; and if not, taking the weight gathering center as the next target position, and controlling the mobile platform to move towards the next target position.

A multi-source localization method and system for quadruped robots based on geometric spatial decoupling and full-dimensional dynamic confidence is disclosed. The method involves: acquiring data and inputting it into a data preprocessing module to obtain distortion-free effective point cloud data; inputting the effective point cloud data into a geometric information matrix construction module to construct a geometric information matrix; based on the geometric information matrix, a full-dimensional spatial degradation decoupling and perception module performs subspace decoupling, algebraic stiffness calculation, and temporal inertial smoothing to form a geometric confidence factor in the [0,1] interval; inputting the geometric confidence factor into a multi-source covariance elastic reconstruction module to dynamically reconstruct the observation noise covariance of the lidar and leg constraints according to the geometric confidence factor, obtaining the reconstructed lidar observation constraints and leg contact observation constraints; inputting the reconstructed observation constraints into an error state Kalman filter to output the high-precision pose of the quadruped robot. This invention enables precise localization of quadruped robots.

This invention discloses an underwater electric field source localization method based on a determinant orthogonal propagation operator and adaptive hybrid-driven differential evolution, belonging to the field of electromagnetic positioning and detection. This invention acquires and receives array signals and calculates the covariance matrix. Based on the propagation operator, it constructs a projection operator matrix orthogonal to the array manifold to replace the noise subspace, thereby constructing a determinant spatial spectrum function and defining a cost function. Then, based on this cost function, it constructs an adaptive hybrid-driven differential evolution framework, fusing the alpha evolution algorithm and an adaptive differential evolution algorithm based on successful historical records to solve for the target position coordinates. This invention eliminates the need for eigenvalue decomposition, reducing computational complexity, and accelerates convergence through an adaptive hybrid-driven search strategy, enabling fast and high-precision electric field source localization in complex underwater environments.

This invention discloses a fine motor imagery decoding system based on PINN and multimodal fusion. The system employs a fine motor imagery decoding method based on PINN and multimodal fusion, and includes a multimodal physiological signal synchronous acquisition module, a data preprocessing and correlation analysis module, a dual-stream decoding network module, a physical manifold constraint layer module, an adaptive gradient balance optimization module, and a classification decision module. By deeply embedding biophysical mechanisms into a deep learning framework, this invention addresses the ill-posedness of the source localization inverse problem, overcomes overfitting in small-sample scenarios, and significantly improves the decoding accuracy, robustness, and physiological interpretability of fine hand movements.

This invention proposes a radio map construction and non-cooperative radiation source localization method based on agent interaction. The method first utilizes a drone swarm to dynamically sample sparse signals, then uses Gaussian process regression to fuse the signal propagation physical priors to generate a spatial attention weight map. These physical priors are injected as biases into a multi-head attention mechanism, enabling multiple agents to form a consistent joint representation of the radiation source target in a unified semantic space. Next, key semantic features are dynamically compressed and selected through task-driven semantic filtering and interaction. Semantic recovery is performed using masked multi-head self-attention and transposed convolutional networks to suppress noise and output preliminary results for both tasks. Finally, an embodied intelligent feedback closed-loop system is constructed. This system can perceive the environment and task status in real time, dynamically adjust the weights and interaction strategies of both tasks, and store the optimal strategy in long-term memory. This invention improves the accuracy of radio map construction, radiation source localization accuracy, and multi-agent collaborative efficiency in sparse sampling, multi-source aliasing, and dynamic complex electromagnetic environments.

A method for acoustic noise source detection. The method may include disposing an acoustic logging tool into a wellbore, taking a first measurement at a first depth with the acoustic logging tool as the acoustic logging tool traverses down the wellbore, taking a second measurement at a second depth with the acoustic logging tool as the acoustic logging tool traverses down the wellbore, and forming a first noise source localization map based at least in part on the first measurement. The method may further include forming a second noise source localization map based at least in part on the second measurement and combining the first noise source localization map and the second noise source localization map to form a final enhanced noise source localization map.

The invention relates to an electroencephalogram traceability model training method and device, equipment and a medium. The method comprises the following steps: constructing a standardized graph structure data set containing multiple individual electroencephalogram signals, a structure connection group and a source activity true value, and training a graph neural network by adopting a meta-learning framework to extract a common rule of a cross-individual brain connection group and a traceability mapping relationship, so as to form a pre-training model with strong generalization ability; for a new individual, only key parameters associated with connection group features in the model are adjusted through a parameter efficient fine tuning technology, and rapid migration of pre-training meta-knowledge to individual specific connection is realized; finally, while individualized traceability precision is kept, computing resources and data volume required for model adaptation are greatly reduced, the problem of traceability deviation caused by individual brain structure difference in a traditional method is effectively solved, and feasibility and efficiency of an electroencephalogram traceability technology in clinical practice are remarkably improved.

This invention relates to the field of UAV swarm cooperative localization technology, specifically to a UAV swarm cooperative localization method for non-cooperative radiation sources. The method includes: constructing a UAV swarm cooperative localization system model; building a semantically driven BDI clustering mechanism based on the output lightweight semantic features and semantic consistency matrix; performing message-passing neural network cooperative inference and parameter updates based on the cooperative topology; iteratively selecting the optimal cooperative node set using a semantic utility-driven long-distance meta-path progressive search strategy; and performing MPNN cooperative inference and model closed-loop updates based on the optimal cooperative node set, ultimately outputting the final position estimate of the non-cooperative radiation source target. This invention solves the problems faced by existing large-scale UAV swarms in non-cooperative radiation source localization, such as difficulties in signal aliasing disambiguation, low cooperative efficiency under non-independent and identically distributed data, and inaccurate node selection strategies.

This invention relates to an early warning system and method for ground pressure disasters in deep metal mining. The system combines high-sensitivity microseismic sensors, distributed acquisition units, signal processing and recognition technology, three-dimensional wave velocity model construction, data visualization, and a multi-parameter fusion early warning algorithm to achieve real-time, accurate monitoring and intelligent early warning of the entire rock mass fracturing process. The system hardware layer includes a 14Hz Geophones sensor network deployed in different deep sections, a netADC / netSP acquisition and processing module, and a UPS power supply system. The software layer includes modules for seismic source localization, feature extraction, deep learning classification, multi-parameter fusion model construction, and dynamic early warning output, supporting multi-platform operation and remote control. By constructing a three-dimensional wave velocity model, identifying microseismic signals in real time, and extracting multiple parameters such as energy, frequency, cumulative apparent volume, and Schmidt number, the system can achieve an early warning accuracy rate of ≥90%, significantly improving the ability to prevent and control ground pressure disasters in mines under complex geological conditions.

This invention provides a distributed radar noncoherent direct localization method, device, terminal, and medium for data anomaly scenarios, relating to the field of radar technology. The method includes: acquiring echo signal data received by each observation array; constructing a signal model characterizing the mapping relationship between the echo signal data and potential source locations, where the common detection area of ​​each observation array is a two-dimensional grid, the grid point coordinates correspond to the potential source locations, the signal model does not contain phase synchronization parameters between observation arrays, and is adapted to the noncoherent characteristics of the signal data; and constructing a system based on the signal model... 2,1 This invention addresses the group sparse optimization problem of norms. A pre-defined optimization algorithm is used to solve the problem, yielding a row sparse matrix. Based on this matrix, the true source localization result is determined, and the column vector elements of the row sparse matrix correspond to the potential source reflection energy estimation results at the grid points. This invention utilizes echo signal data from various observation arrays to achieve primary localization, eliminating secondary localization errors and reducing abnormal noise interference.

The application discloses a multi-sound source positioning and imaging method based on sparse representation and variational Bayesian inference, and steps are as follows: (1) an initial sound intensity matrix is generated under low resolution by using a conventional beamforming algorithm to estimate the sound source position; (2) the signal intensity gradient is calculated, the search position is updated along the gradient direction, and the accurate sound source position and high-resolution sound intensity matrix are obtained; (3) a sparse dictionary learning algorithm is used for sparse coding and dictionary update optimization of the high-resolution sound intensity matrix; (4) a variational Bayesian inference model is constructed based on the sparse coefficient, the lower bound of variation is optimized, and the posterior positioning estimation of the multi-sound source is carried out; and (5) the positioning result is fused with the camera image to obtain the visualized position of the sound source. The method realizes high-precision real-time positioning and imaging in a complex sound field by combining low-resolution positioning, gradient optimization, sparse dictionary learning and Bayesian inference, and has high spatial resolution and strong anti-interference capability.

This invention discloses a satellite navigation multi-interference source localization method and related apparatus, comprising: acquiring received signals from each observation station in the observation system; acquiring the acquisition covariance matrix of each observation station in the observation system; constructing a power spectrum function based on the received signals and acquisition covariance matrix of each observation station; constructing a target cost function based on the power spectrum function; solving the target cost function based on the power spectrum function to obtain a global maximum; drawing a target cost function plane diagram based on the global maximum; and locating multiple interference sources based on the target cost function plane diagram. This method and related apparatus can locate multiple interference sources and have the characteristics of low computational complexity, high positioning accuracy, and high speed.

This satellite device comprises: a radiowave reception unit that receives a radiowave signal from a radiowave transmission source; an acquisition unit that acquires location information indicating the location of a host satellite device when the radiowave signal is received and time information indicating the time of receiving the radiowave signal; a communication unit that communicates with two or more other satellite devices and receives from the other satellite devices that have received the radiowave signal, the pieces of location information and the pieces of time information at the time; an arrival time difference calculation unit that calculates an arrival time difference of the radiowave signals between the host satellite device and each of the other satellite devices on the basis of each time indicated by each of the pieces of time information; a radiowave source localization unit that identifies the location of the radiowave transmission source by using the arrival time difference and each of the pieces of location information; and an output unit that outputs output information including the identified position.

An imaging system includes a detector configured to obtain radiation data from one or more sources and a controller. The controller is configured to define a plurality of buffers based on at least one initial condition. The radiation data includes a plurality of events. The controller is configured to receive individual events of the plurality of events and determine whether the individual events fall within a designated current buffer. Each event of the plurality of events in the current buffer is corrected for pose and aligned in a common two-dimensional space. The plurality of events in the current buffer are reconstructed into a three-dimensional space, the reconstruction being performed once for each of the plurality of buffers. The controller is configured to create a three-dimensional image based in part on the reconstruction in the three-dimensional space.

The invention relates to an aircraft noise source positioning method and device, a medium and application, and the method comprises the steps: S1, grouping M microphone arrays to form m sub-arrays, M and m being integers; s2, aiming at each sub-array, solving sound source space distribution to obtain a sound source positioning result of each sub-array; and S3, multiplying the m sound source localization results by the m-power root of the product to obtain a result as a final sound source localization result. The method provided by the invention can be suitable for more complex and more practical microphone array distribution, and a sound source positioning result with higher precision can be obtained.

This invention discloses a gamma radiation source localization method based on a sparse solution algorithm, comprising: acquiring a series of nuclear radiation measurement data D at different locations within a target area; acquiring a scene model of the target area and voxelizing it to obtain a voxel model, wherein each voxel in the voxel model serves as a gamma source term; constructing a response matrix H of the gamma voxel source term at the location of the nuclear radiation measurement data, wherein the elements in the response matrix H are... h ji , h ji The unit strength of the first i Individual gamma source in the first j The expected nuclear radiation data at each measurement point; construct the system of equations H·S=D for the gamma voxel source term intensity value S, where the source term intensity value, n The number of voxels in the voxel model. s i For the first i The source intensity of voxels; the intensity values ​​of each gamma voxel source term are obtained by solving the equation system using a sparse solution algorithm. s i Based on the intensity values ​​of each gamma voxel source term, the location of the gamma radiation source is visualized in the voxel model.

This disclosure provides embodiments of a method, apparatus, device, and computer-readable medium for noise localization of complex automotive components. One specific implementation of the method includes: acquiring signals generated by various sensors to obtain an initial sensor signal set; performing timing signal preprocessing on the initial sensor signal set to obtain a further sensor signal set; determining fault information of the target complex automotive component to obtain target fault information; extracting Akaike acoustic frequencies from the acoustic signal set to obtain a set of characteristic frequency information for the acoustic array signal; using the characteristic frequency information set for the acoustic array signal to perform noise source localization processing on the target complex automotive component to obtain noise source localization information; and controlling maintenance equipment to perform fault repair on the target complex automotive component based on the noise source localization information and the target fault information. This implementation can improve the accuracy and precision of noise source localization information, improve the performance of the target complex automotive component and the vehicle, and enhance the timeliness and efficiency of maintenance.

This invention discloses a time-difference localization method for frequency-hopping radiation sources based on the maximum discrete spectral value, relating to the field of frequency-hopping radiation source localization technology. The method includes the following steps: constructing a model of the frequency-hopping signal received by the observation station based on the geometric positions of the observation station and the target radiation source; establishing an approximate expression for the frequency-hopping signal based on the discrete spectral characteristics of the signal; generating an objective function related only to the radiation source position using the target radiation source position information contained in the received signal; and achieving direct localization of the frequency-hopping radiation source through an exhaustive search algorithm. This method is advantageous for direct localization, provides accurate localization, and possesses strong versatility and scalability.

This invention relates to the field of radio monitoring and communication technology, specifically to a machine learning-based method for interference feature clustering and rapid interference source localization. The method involves multiple in-use wireless communication terminals performing self-spectrum sensing and measuring received signal strength and field strength to conduct self-interference analysis and determine the presence of interference. When interference is detected, interference feature data is transmitted to the network side in real time. The network side processes the received multi-source data and uses machine learning algorithms for multi-dimensional joint clustering, aggregating fragmented interference alarm information into interference event clusters. For each interference event cluster, the location and field strength information of the measurement points are extracted, the field strength is converted into distance estimation, and localization is calculated. Finally, the location and confidence level of the interference source are output to a visualization interface. This method solves the problems of high construction costs, large coverage blind spots, slow response speed, and limited data dimensions caused by existing technologies that rely on dedicated monitoring facilities.

The invention relates to an underwater acoustic positioning method based on the combination of Bayesian posterior probability and signal cluster arrival time structure information, and the method comprises the steps: firstly determining an error region of the initial positioning of a signal target through the Bayesian maximum posterior probability on the basis of the arrival time delay estimation direction of a horizontal triangular array receiving signal of an IMS underwater acoustic station; then, through deep analysis of intrinsic sound rays of a hydrophone receiving signal, a signal arrival structure is represented in a signal cluster form, and a distance and depth fuzzy plane of a target function is constructed in a preliminarily positioned search area; according to a positioning result of measured data of explosions at different depths beyond thousands of kilometers, a shallow source positioning result with an obvious multipath effect is superior to a deep source positioning result, the deviation between the two positioning results and a real position is only tens of kilometers, and the result deviation is relatively reasonable for positioning of an ultra-long-distance explosive sound source above thousands of kilometers.

The invention discloses a natural action electroencephalogram and myoelectricity recognition method based on frequency band specialization LAURA and a frequency division network. The method comprises the following steps: synchronously collecting multi-channel EEG and sEMG; the EEG and the sEMG are preprocessed, and frequency bands are divided; specifying LAURA source localization by combining the characteristics of each frequency band of EEG, extracting a brain region energy time sequence, and screening out each frequency band activated brain region; constructing a frequency-division multi-layer cortex-muscle network by taking the activated brain region as a cortex node and a muscle group corresponding to a myoelectricity sensor in the sEMG acquisition process as a muscle node; calculating a hyperadjacency matrix representing cortical muscle connection, and inputting the hyperadjacency matrix into a classifier to decode natural actions; cortex-muscle network parameters are calculated, and electroencephalogram and myoelectricity information transmission characteristics are analyzed; and according to the movement process, dynamic evolution of cortical muscle coupling is analyzed. According to the method for optimizing LAURA brain power supply positioning by utilizing the frequency band characteristics, convenience is provided for natural action decoding and frequency band specificity analysis by constructing a multi-layer frequency division network between cerebral cortex and muscle.

The embodiment of the invention discloses a blind sparsity subspace backtracking homogeneous multi-source positioning method for residual divergence decision, which comprises the following steps of: rasterizing a monitored area containing an unknown signal source node and a known sensing node, and acquiring actual received signal strength as an observation vector (also as a residual vector); and a sparse observation model is constructed based on the grid position and the observation vector. And calculating the normalized inner product of the residual vector and each column of the sensing matrix in the model to obtain a correlation numerical value sequence, determining an adaptive threshold value through the absolute deviation of the digits and the mean value, screening candidate elements to form a candidate support set, screening through a received signal strength estimation value to generate an iteration position support set, and updating the residual vector. And after each iteration, comparing theoretical and empirical probability distributions of the residual vector to obtain a difference metric value, stopping iteration when a termination condition is met, and taking the grid position corresponding to the current position support set as a positioning result. The method only depends on the received signal strength, and the deployment cost of sensor hardware is reduced.

This invention discloses a multi-satellite electromagnetic signal source localization method based on overpass time acquisition. It primarily addresses the problems of existing methods failing to achieve on-board positioning under conditions of low link bandwidth and limited payload resources, and also suffers from high algorithm complexity. The implementation scheme is as follows: S satellites are selected, and the frequency of the target signal is continuously recorded starting from the moment the target signal can be detected. The overpass time of each satellite is calculated based on the Doppler frequency change rate of the detected target signal. The region where the target signal source is located is calculated based on the time when the satellite begins to detect the target signal and the time when it stops receiving the target signal. This region is then divided into grids, and the shortest distance time between the grid and the satellite is calculated. Based on the overpass time and the shortest distance time, the average position deviation of each grid is calculated to determine the location of the target signal source. This invention simplifies the complexity of current satellite positioning technology, improves signal positioning accuracy, and can be used for real-time online on-board positioning of electromagnetic signal sources.

The invention relates to an environment service robot-oriented dynamic path planning and pollution source positioning method and system. The method comprises the following steps of S1, acquiring multi-modal data; s2, pollutant concentration filtering and gradient construction; s3, determining the target orientation of the pollution source; s4, modeling a behavior semantic weighted path; s5, dynamic path planning; and S6, executing feedback and path correction. The system comprises a multi-modal data acquisition module, a pollutant concentration filtering and gradient construction module, a pollution source target orientation determination module, a behavior semantic weighted path modeling module, a dynamic path planning module and an execution feedback and path correction module. According to the method, the pollutant concentration data is filtered, the concentration gradient field is constructed, and meanwhile, the user behavior state is mapped into the semantic resistance parameter in the path planning, so that efficient source searching, stable movement and user-friendly service of the robot in a complex indoor environment can be realized.