24 results about "Source localization" patented technology

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.

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.

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 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.

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.

This application discloses a sound source localization model training method, a sound source localization method, and an apparatus, belonging to the field of sound signal processing technology. This application is applied to sound source localization scenarios, such as predicting the spatial position of a loosely connected component, wherein the component is mounted on a mechanical connection structure. This application uses sound signals as the data processing object, trains a sound source localization model based on pre-built training samples, and achieves automatic sound source localization based on the trained model. Since this method requires no manual intervention, compared to manual inspection, it not only saves labor costs and avoids the risks of working at heights, but is also more efficient, enabling real-time monitoring of the fastening status of the connected component.

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 provides a radiation source localization system based on swarm drone collaboration and self-organizing network communication, comprising: a signal frequency acquisition module, which constructs a self-organizing communication network for the target area through a swarm of drones to acquire preset features of the target signal source; a signal edge determination module, which, when any drone detects the target signal features, delineates the outline of the signal coverage area to determine the target area of ​​the target signal source; a target node determination module, which calculates a virtual center point of the target area based on the drone positions, uses the virtual center point as the origin, determines the positioning drones located in three orthogonal directions, and determines the spatial geometric relationship between the positioning drones and the target signal source; and a signal coordinate determination module, which determines the three-dimensional coordinates of the target signal source based on the spatial geometric relationship. This invention improves the localization accuracy of radiation sources, while also increasing swarm search efficiency, reducing system energy consumption, and enhancing anti-interference capabilities in complex environments.

This invention discloses a single-station instantaneous passive localization method and apparatus based on a circular array. The method includes the following steps: S1. Acquiring the same signal emitted by a target radiation source intercepted by the circular array, and estimating the phase difference between the intercepted signals between adjacent antenna elements; S2. Estimating the target coordinates based on the phase difference of the intercepted signals and the interferometer vectors formed by adjacent array elements, obtaining the target coordinate unit vector estimation result; S3. Dividing the possible distribution range of the target into multiple distance intervals, and obtaining multiple sets of interferometer vectors through different combinations of antenna elements, thereby obtaining the intercepted signal phase difference estimation value corresponding to the center value of each distance interval, and obtaining the distance estimation result by comparing the phase difference estimation value with the actual measured value; S4. Obtaining the radiation source localization result based on the target coordinate unit vector estimation result and the distance estimation result. This invention has the advantages of simple structure, high positioning accuracy, high efficiency, and wide application range.

This application provides a method and apparatus for radon source localization and intensity estimation using a multi-detector array. The method includes: acquiring *a* valid reference events detected by the multi-detector array within a preset time period; the multi-detector array includes N detection nodes; performing spatiotemporal clustering on the *a* valid reference events to obtain *b* candidate homologous event clusters; filtering and analyzing the *b* candidate homologous event clusters based on the target space corresponding to the multi-detector array to obtain *c* event spatial coordinates; generating a three-dimensional density map of event point clouds based on the *c* event spatial coordinates; determining the location of the radon release source based on the local maxima of the three-dimensional density map of the event point cloud; and determining the relative release intensity corresponding to the location of the radon release source based on a preset diffusion decay model. By identifying radon sources through the spatial localization and density distribution of decay events, the accuracy of radon release source localization and intensity estimation is effectively improved.

This invention discloses a multi-source localization method for vehicle-mounted microphone arrays based on Doppler effect compensation. First, a mobile received signal model is established to analyze the amplitude-frequency modulation characteristics caused by relative motion, and a compensation algorithm combining nonlinear time mapping and amplitude correction is proposed. Doppler distortion is eliminated through time-domain inverse resampling. Second, addressing the nonlinear coupling problem between compensation and position calculation, a closed-loop localization model based on a "hypothesis-compensation-verification" mechanism is constructed. Furthermore, a ring-topology niche particle swarm optimization algorithm is introduced to achieve multi-peak parallel detection, overcoming the shortcomings of traditional algorithms that easily get trapped in local optima; simultaneously, a density clustering algorithm is combined to remove noise points, achieving automatic extraction of multi-source coordinates. This method effectively overcomes Doppler distortion and multi-source aliasing interference, significantly improving the localization accuracy and robustness in dynamic environments. Real-vehicle experiments show that the dual-source localization error is controlled within 0.45 meters.