82 results about "Spatial spectrum" patented technology

This application discloses a method and system for synchronous testing of metasurface grating waveguide performance, relating to the field of metasurface grating technology. The method includes polarization state modulation and spatial frequency encoding of multi-wavelength incident beams to obtain a modulated incident light field; coupling this modulated incident light field into the metasurface grating waveguide under test, which then undergoes total internal reflection to form the outgoing light field; acquiring a raw dataset of the entire light field, including Stokes polarization images and spatial spectrum images corresponding to each wavelength of the outgoing beam; calculating the total internal reflection hop count based on the Stokes polarization image; calculating the spatial step size offset based on the spatial spectrum image; and establishing a mapping relationship between the image pixel coordinates of the raw dataset and the internal physical regions of the metasurface grating waveguide under test to obtain local diffraction efficiency distribution data. This application solves the problem that traditional outgoing end intensity testing is difficult to trace to local defects inside the waveguide, achieving synchronous, high-precision quantitative characterization of local diffraction efficiency across multiple wavelengths.

This application discloses a method and system for estimating the direction of arrival (DOA) of weak targets based on Riemannian manifold background suppression and adaptive sparse Bayesian learning. The method includes: acquiring time-series signals using a sensor array to construct a series of sample covariance matrices, mapping them to a point sequence on a Hermitian positive definite matrix manifold space; iteratively calculating the background interference covariance matrix using the non-Euclidean geometric properties and logarithmic shielding effect of the Riemannian metric; mapping the background interference covariance matrix back to Euclidean space, adaptively performing background subtraction based on an energy decision mechanism to reconstruct a positive definite covariance matrix to be measured; inputting the covariance matrix to be measured into a sparse Bayesian learning framework, first iteratively recovering the signal power through adaptive mesh refinement sparse Bayesian learning, then performing a closed-loop iteration of subspace noise cleaning while keeping the mesh fixed to recover the sparse spatial spectrum of the target signal; and finally, using local analytical interpolation techniques to eliminate mesh quantization errors and calculate the precise DOA of the target.

The application discloses a kind of wideband signal-oriented distributed movable antenna layout optimization method, comprising: based on swarm intelligence algorithm, antenna layout scheme is iteratively optimized until reaching iteration stop condition, the best fitness of population corresponding antenna layout scheme is regarded as the optimal scheme of antenna layout output;Wherein, the fitness of population is determined according to the wideband cumulative Cramer-Rao bound of its corresponding antenna layout scheme.The present application can directly construct the bottom mapping relationship between continuous position variable and wideband signal direction finding error lower limit by introducing wideband Cramer-Rao bound as fitness function.Because the index itself represents the theoretical minimum direction finding error of antenna layout, it can accurately quantify the spatial spectrum coupling effect of full frequency band;As fitness function, it can make the optimization directly towards the most essential optimization goal, ensure that the output physical array theoretically approximates the wideband direction finding physical limit under this degree of freedom.

This invention discloses a method and system for separating AIS aliasing signals using a four-circular array antenna. The steps are as follows: S100: AIS signals are received through multiple antennas to obtain the spatial baseband signal matrix X, and the phase error Δφ is calculated using interval calculation. k S200: Calculate the phase compensation matrix p; S200: Compensate the baseband signal according to the phase compensation matrix to obtain the baseband signal X. comp S300: For baseband signal X comp By expanding the spatial and temporal dimensions, we obtain the spatiotemporal snapshot matrix X. st (t); S400: The covariance matrix R is obtained by estimating the spatiotemporal snapshot matrix. xx S500: For the covariance matrix R xx Eigenvalue decomposition is performed to obtain the signal subspace and noise subspace; S600: DOA estimation is performed using the MUSIC algorithm to construct the spatial spectrum P(θ); S700: The beam w at the optimal solution is obtained using the space-time constrained minimum variance algorithm; S800: The separated signal is output through the sparse separation method of protocol features. This method can significantly improve the availability of AIS self-organizing network and provide more reliable protection for ship navigation safety.

The application relates to the technical field of spectral imaging, and discloses a multi-snapshot hyperspectral compression imaging system. The multi-snapshot hyperspectral compression imaging system disclosed by the application comprises an optical lens, a pixel filter, a surface array camera and a displacement actuator. The displacement actuator moves the pixel filter according to a preset movement mode so that a detector collects multiple image data with the filter being at different positions and having different filtering effects. An upper computer carries out spatial combination through a spectral domain reconstruction neural network model to reconstruct the multiple image data into spatial-spectral data cube reconstruction data. At this time, image processing is carried out through a set original image acquisition device, so that the spatial-spectral data cube reconstruction data reconstructed through the above scheme has high accuracy, thereby solving the technical problem of low image reconstruction accuracy of the existing hyperspectral compression imaging system.

The application relates to the technical field of power cable insulation state monitoring and fault positioning, and discloses a buried cable partial discharge three-dimensional positioning system and method based on a UHF phased array. The technical scheme of the application receives the ultra-high frequency electromagnetic wave signals radiated by a buried cable partial discharge source through a UHF phased array antenna, carries out synchronous sampling and digital conversion on the signals by using multi-channel high-speed synchronous acquisition, corrects the signal propagation speed in the array manifold matrix in combination with soil dielectric characteristic parameters, solves the initial three-dimensional coordinates according to the time difference of signals in each channel, and carries out spatial spectrum estimation and spectrum peak search in the neighborhood, so that the problems that the existing single-channel UHF detection can only qualitatively judge whether there is partial discharge but cannot accurately position the discharge source position, and that on-site operation and maintenance still needs large-area excavation to find the discharge source, leading to low efficiency and high cost, are solved, the three-dimensional coordinates of the buried cable partial discharge source are quickly and accurately acquired, the fault checking time is shortened, and the operation and maintenance cost is reduced.

The application relates to a MUSIC direction-finding method based on an underwater information system cross array, and belongs to the technical field of underwater acoustic direction finding. In view of the problems of low signal-to-noise ratio and low direction-finding precision of underwater signal direction finding, the application provides a MUSIC direction-finding method based on an underwater information system cross array. The method is based on a MUSIC algorithm, frequency extraction and spatial spectrum accumulation, a cross array is used to perform direction-finding processing on a signal, the relative position relationship between the signal and the cross array is obtained, a suitable linear array in the cross array is selected to perform direction-finding processing on the signal, and the direction finding of an underwater target acoustic signal under low signal-to-noise ratio can be effectively realized.

This invention discloses a non-prior detection method and device for targets obscured by vegetation canopy based on UAV multispectral point clouds, belonging to the field of three-dimensional target detection technology in remote sensing. To address the problem of insufficient target samples due to vegetation canopy obstruction in target detection methods based on multispectral point cloud data, this invention first clusters non-ground points in the spectral point cloud, statistically analyzes the circumradius of each cluster, and extracts multi-scale spatial features of the multispectral point cloud based on the clustering results, constructing a spatial-spectral feature profile. Then, it constructs a radius-adaptive spherical shell model, obtains the local background point set within the spherical shell corresponding to any point, and further analyzes the p... i The spatial-spectral features are jointly expressed, and the residuals between all expressed results and the original features are calculated, thereby realizing the detection of occluded targets.

This invention belongs to the field of acoustic detection technology for partial discharge in power equipment, and relates to an array-type acoustic imaging partial discharge detection method and system. The method includes: establishing the correspondence between channel identifier sequences and array geometric parameters; dividing the array into subgroups according to a fixed subgrid and generating a synchronous sampling time sequence to acquire multi-channel pulse density modulation signals; demodulating and extracting the signals, bandpass filtering and pulse framing; constructing a forward-backward spatial smoothing covariance matrix and using minimum variance distortionless response beamforming to calculate the spatial spectrum to generate an acoustic image; and registering and superimposing the acoustic image with a visible light image to output the positioning result. The technical solution of this application achieves high-contrast imaging and stable positioning output through temporal and geometric consistency constraints, which can improve the consistency of on-site interpretation and meet the requirement of repeatable positioning under weak partial discharge ultrasonic conditions.

The application discloses a textile fabric defect hyperspectral image detection method and system, which comprises the following steps: acquiring a hyperspectral image of a textile fabric and generating a hyperspectral dataset, and pre-processing the hyperspectral image in the dataset; expanding the hyperspectral image based on a mirror filling strategy, and extracting a three-dimensional Patch block set centered on a pixel according to a preset patch size on the expanded image; inputting the Patch block into a multi-branch neural network model, and respectively extracting multi-scale texture features, local spatial-spectral correlation features and spectral deep nonlinear features; performing feature fusion on the outputs of the three branches and defect discrimination, and outputting corresponding defect category probabilities; reconstructing a fabric defect distribution map according to the defect discrimination results of the center pixels of the Patch blocks and performing filtering processing to obtain a final textile fabric defect detection result. The application is aimed at special extraction and fusion of global spatial features, local spatial-spectral collaborative features and pure spectral features, and high-precision and strong-robustness defect detection is realized.

The application discloses a multi-scene robust Bluetooth AOA indoor positioning method, which comprises the following steps: obtaining the IQ value of each antenna array element; solving the phase of each antenna array element; performing eigenvalue decomposition on the covariance of the antenna array phase to obtain a signal space and a noise space; calculating the horizontal angle and the pitch angle that make the spatial spectrum P MUsIC The horizontal angle and the pitch angle that make the spatial spectrum P The application can improve the robustness of the angle, thereby improving the positioning precision.

The application discloses a multi-path time delay estimation method based on orthogonal linear frequency modulation wave division multiplexing signals, and the process is as follows: an OCDM signal is transmitted by a transmitting end, a time domain receiving signal is obtained after the OCDM signal passes through a multi-path channel, a fractional domain Fourier transform (FrFT) is performed on the time domain receiving signal to obtain a fractional domain receiving signal, a covariance matrix of the fractional domain receiving signal is calculated after the fractional domain receiving signal is de-coherent, a subspace of the covariance matrix is decomposed and a spatial spectrum is calculated by using a MUSIC algorithm, and a time delay corresponding to a spectrum peak of the spatial spectrum is a time delay estimation value. The multi-path time delay estimation method disclosed by the application can accurately estimate a time delay of a dense multi-path channel with a Doppler effect, effectively reduces a relative root mean square error of time delay estimation, is still relatively accurate in estimating a time delay when the multi-path is dense, and has a certain resistance to a Doppler frequency offset.

This invention discloses a mobile radio spectrum monitoring system and method based on a quadruped robot platform, belonging to the field of radio direction finding technology. The system includes a quadruped robot body and a monitoring module mounted on it. The monitoring module includes: a seeker head, mounted above the robot body via an elevated frame, with a built-in array antenna for acquiring radio frequency signals; a signal processing chamber, containing an SDR receiver connected to the array antenna to receive signals; a pose sensor module to acquire the robot's pose; a data processing module to perform spatial spectrum direction finding on the signals and fuse the pose data, outputting the coordinates of the radiation source and transmitting them through a communication module; and a power supply chamber to power the system. This invention effectively integrates a quadruped robot with a radio monitoring system, improving the accuracy of transient signal capture and monitoring response efficiency. It can meet the needs of radio management departments for large-scale deployment and routine inspections in complex scenarios, contributing to the maintenance of electromagnetic space safety and order.

The application discloses a kind of even circle array one-dimensional spatial spectrum direction finding signal processing device, including data processing and covariance matrix module, MUSIC algorithm processing module, Capon algorithm processing module, processing result fusion module and DML algorithm processing module;The function of data processing and covariance matrix module is received signal pre-processing, and covariance matrix is obtained by calculation;MUSIC algorithm processing module is calculated using input covariance matrix to obtain the MUSIC spatial spectrum of receiving array;Capon algorithm processing module is calculated from covariance matrix to obtain the Capon spatial spectrum of receiving array;Processing result fusion module analyzes and judges the calculation result of MUSIC spatial spectrum and Capon spatial spectrum, whether MUSIC spatial spectrum needs iterative search, obtains the direction finding result of incoherent signal, and judges whether it contains coherent signal;DML algorithm processing module processes a pair of coherent signal DOA calculation, and outputs spatial signal direction finding result.This device can accurately calculate the DOA of incoherent signal;And on the basis of incoherent signal direction finding, a pair of coherent signal direction finding is completed.

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.

The application provides a multi-scale hyperspectral image reconstruction method fusing an adaptive guidance mechanism, which is used for realizing efficient and high-fidelity hyperspectral reconstruction and adapting to multi-scene application requirements. Specifically, the method constructs a spatial-spectral dual-branch interaction structure for decoupling modeling, and combines spectral relative position information to enhance relationship expression, thereby solving the problem of insufficient spatial-spectral coupling modeling. Further, the application proposes a spectral adaptive sparse mechanism combined with a mask, which adaptively selects and focuses on key spectral information, while suppressing redundant spectral information and improving feature discriminability, thereby adapting to spectral extraction requirements in different scenes. In addition, aiming at the problem that excessive smoothing in the spatial dimension leads to loss of detail information, the method designs a structure-guided feature refinement module, which enhances the recovery ability of edge and texture details through structure prior, thereby meeting the reconstruction requirements of high-precision scenes. Experimental results show that the method is superior to existing methods on multiple public data sets, and significantly improves spatial structure fidelity and spectral consistency, which is more suitable for actual application scene requirements.

The application provides a neural cell spectrum detection method and system based on deep learning, and belongs to the technical field of spectrum analysis, and comprises the following steps: constructing and jointly training a spectrum feature extraction network, a spatial context aggregation network and an abundance prediction network; obtaining a hyperspectral microscopic image of a neural cell sample to be detected for preprocessing to obtain a hyperspectral data cube; inputting the hyperspectral data cube into the pre-trained spectrum feature extraction network to output a deep spectrum feature map of each pixel; inputting the deep spectrum feature map into the pre-trained spatial context aggregation network to output a spatial-spectrum joint feature map; and inputting the spatial-spectrum joint feature map into the pre-trained abundance prediction network to output an abundance map of the neural cell sample to be detected as an abundance detection result of the neural cell sample to be detected. The application solves the problem that the traditional method is difficult to accurately separate and quantitatively analyze the abundance distribution of each subcellular component.

The application provides a hyperspectral super-resolution reconstruction method and system fusing phase priori and degradation perception, the method comprising: acquiring an observed image and a spectral response function matrix, and constructing an optimization objective function comprising a data fidelity term and a space-spectrum joint priori term; using a semi-quadratic splitting algorithm to decouple the objective function into three constraint terms of physical fidelity, spectral low rank and depth priori; using a Fourier phase-amplitude mixed processing model, a truncated singular value decomposition model and a double-branch proximal mapping network model to optimize and solve the three constraint terms respectively, to obtain and update a first auxiliary variable, a second auxiliary variable and a target hyperspectral image; through cyclic iteration until the iteration number reaches a number threshold, the target hyperspectral image output in the last iteration cycle is taken as a high-resolution hyperspectral reconstruction image. The application effectively improves the spatial texture recovery capability and spectral fidelity of the image.

This invention proposes a hyperspectral image classification method based on a multi-scale spiral scanning spatial-spectral dual-branch Mamba module, which mainly consists of four modules: a hyperspectral preprocessing module, a multi-scale spiral scanning spatial Mamba module, a bidirectional scanning spectral Mamba module, and an adaptive enhancement fusion module. It adopts a spatial-spectral dual-branch structure and achieves model lightweighting through a single Mamba layer. Specifically, the multi-scale spiral scanning spatial Mamba module integrates lightweight multi-scale convolution and center-first spiral scanning to effectively extract spatial features. The bidirectional scanning spectral Mamba module overcomes the unidirectionality of the state-space model through a bidirectional scanning mechanism, fully extracting spectral features. The adaptive enhancement fusion module achieves complementary enhancement and fusion of the two modules. This method systematically solves the problems of insufficient multi-scale modeling capability, high computational complexity, and poor adaptability to targets of different sizes in standard Mamba, achieving a significant improvement in classification accuracy and effectively reducing the computational burden.

This invention provides a method and system for spectral detection of neural cells based on deep learning, belonging to the field of spectral analysis technology. The method includes: constructing and jointly training a spectral feature extraction network, a spatial context aggregation network, and an abundance prediction network; acquiring hyperspectral microscopic images of the neural cell sample to be detected and preprocessing them to obtain a hyperspectral data cube; inputting the hyperspectral data cube into the pre-trained spectral feature extraction network to output a deep spectral feature map for each pixel; inputting the deep spectral feature map into the pre-trained spatial context aggregation network to output a spatial-spectral joint feature map; and inputting the spatial-spectral joint feature map into the pre-trained abundance prediction network to output an abundance map of the neural cell sample to be detected, which serves as the abundance detection result for the neural cell sample. This invention solves the problem that traditional methods struggle to accurately separate and quantitatively analyze the abundance distribution of various subcellular components.

This application provides a map generation method and system based on multi-source heterogeneous geographic information processing. It acquires three types of heterogeneous data: a geographic element attribute database, a geographic coordinate semantic database, and a geographic reference image database. Spatial spectrum reconstruction processing is then performed to generate a spatial spectrum cube. Belief propagation fusion processing is then used to construct a belief network from the spatial spectrum data. Combined with spatially constrained probability field optimization and D-S evidence synthesis, a geographic evidence chain is generated. Next, spatiotemporal field reconstruction processing is performed to separate the static and dynamic states of the geographic evidence chain and couple them into fields, constructing a hyperdimensional spatiotemporal map. Finally, holographic projection conversion processing is used to perform phase field compression and coherent diffraction reconstruction on the hyperdimensional spatiotemporal map, generating a holographic geographic projection map that supports 3D interaction. This method achieves the automatic generation of a structurally unified, highly reliable geographic map that supports dynamic behavioral reasoning and true 3D interaction from multi-source heterogeneous geographic data, significantly improving the decision-making value and cognitive efficiency of geographic information.

This application relates to the field of hyperspectral remote sensing application technology, and provides a hyperspectral methane detection method based on spatial-spectral joint analysis. First, a sample dataset is constructed, and simultaneously, a methane detection neural network is built to extract the spatial-spectral features of the methane plume from the sample dataset. Then, a preprocessing module based on fundamental laws is established to form a hard constraint mechanism, outputting standardized spatial-spectral features of the methane plume to obtain a predicted methane concentration distribution map. A physical information penalty term is constructed to form a soft physical constraint, minimizing the error between the predicted methane concentration distribution map and the pseudo-true label, thus incentivizing the network to generate a full-resolution methane concentration distribution map. Through a deeply fused spatial-spectral joint strategy, relying on the synergistic effect of the hard constraint mechanism and the soft physical constraint, effective suppression of background noise can be achieved in complex atmospheric environments and various background interference scenarios, significantly improving the accuracy and reliability of signal detection. The detection efficiency is high, making it suitable for large-scale and efficient processing of massive hyperspectral data.

The present application relates to a kind of cross-domain federal medical image classification method and system based on FedSSAC, belong to medical image processing and federal learning technical field.The steps are as follows: (1) each medical client calls local labeled brain tumor MRI slice data set;(2) construct FedSSAC space-spectrum adaptive cooperation framework model;(3) each medical client completes FedSSAC space-spectrum adaptive cooperation framework model training and verification based on local data, and qualified parameter is uploaded to server, and server updates global model parameter by performance perception global aggregation strategy and is issued, and iteration training is converged to model;(4) the preprocessed MRI image to be classified is input into the FedSSAC space-spectrum adaptive cooperation framework model of training completion, and brain tumor classification result is output.

The present application relates to hyperspectral image processing technical field, disclose a kind of fast medical hyperspectral image classification method, comprising: extracting several pixels as training sample from the medical hyperspectral image to be classified;Set initial neighborhood window scale;According to initial neighborhood window scale, randomly select joint training area from the medical hyperspectral image to be classified;The similarity result between each pixel in joint training area and training sample is obtained by joint classification algorithm calculation;By k times iteration cross-validation algorithm, the initial neighborhood window scale is optimized, and the best neighborhood window scale is obtained;Using the best neighborhood window scale and joint classification algorithm, all pixels of the medical hyperspectral image to be classified are classified.Cotangent mapping cosine similarity can effectively reduce the interference of heterogeneous pixels, and local similarity data gravity can adaptively balance the contribution of minority and majority, to ensure the full use of MHSI spatial spectral information.

The application discloses a hyperspectral classification method and system based on space-spectrum prototype feature learning, and the method comprises the following steps: S1, dimension reduction is performed on an original hyperspectral image χ to obtain a dimension-reduced hyperspectral image Z; a local space-spectrum neighborhood set F is constructed for each dimension-reduced pixel in Z i ; S2, some dimension-reduced pixels z i are randomly selected from Z as training samples, the rest of the dimension-reduced pixels are test samples, a training space-spectrum set F is constructed, and the elements in F are the local space-spectrum neighborhood sets corresponding to the dimension-reduced pixels in the training samples; based on F and corresponding training sample label data, an optimal space-spectrum prototype set P i and a linear projection W are calculated by using a space-spectrum prototype feature learning algorithm; S3, the optimal space-spectrum prototype set P i and the linear projection W are used to perform test classification on the test samples. The method improves the calculation efficiency, has higher classification precision under a small sample condition, and can be applied to the fields of hyperspectral classification and space remote sensing.

The application relates to a high-generalization image space super-resolution method based on a Kolmogorov-Arnold neural operator, which further proposes a coarse fitting module for spatial degradation modeling, a spectral degradation modeling module considering spatial spectral features, and a nonlinear feature fitting module (residual information reconstruction self-network); the application proposes a new framework which reconstructs a super-resolution degradation model, and improves the perception of the model for complex degradation by introducing a residual component in the optimization model.

This invention discloses a simulation and evaluation system for array signal processing in underwater sonar systems, belonging to the field of underwater sonar technology. The system includes: an underwater acoustic channel simulation module, used to generate an underwater channel model including ray bending and multipath effects and the channel response of each array element based on input marine environmental parameters using a ray tracing algorithm; a signal synthesis module, used to synthesize simulated multi-channel received signals of the array based on array parameters, target parameters, and channel responses; an algorithm processing module, integrating multiple underwater acoustic signal processing algorithms to process the received signals and output beamforming or spatial spectrum estimation results; and a performance evaluation module, which visualizes the processing results and calculates multi-dimensional performance indicators. This invention significantly improves the realism of the simulation, the completeness of the test scenario, and the guiding nature of the evaluation conclusions through high-fidelity physical modeling and closed-loop evaluation, effectively supporting sonar system design, algorithm verification, and performance prediction.

The application discloses a choir balance guidance method and system based on voiceprint recognition, relates to the technical field of digital audio signal processing, and comprises the following steps: extracting acoustic energy features and ontology voiceprint matching features of a current time window of multiple pickup channels, and calculating time sequence change rates of both relative to historical time windows; performing orthogonal state deviation judgment based on the time sequence change rates, and locking a target pickup channel interfered by a howling sound; determining a howling sound emission source channel and an absolute propagation time delay by cross-correlation ranging within a preset time delay constraint range; inversely calculating a physical propagation distance according to the time delay, and constructing a spatial spectrum dissipation filter in combination with a nonlinear acoustic dissipation model; performing phase shift and frequency response deformation constraint on a frequency domain signal of the howling sound source by using the time delay and the filter, and generating a physically deformed howling sound prediction signal; performing targeted cancellation on the frequency domain signal of the target channel and the prediction signal, reconstructing a pure acoustic energy parameter, and generating a balance guidance instruction; and the application can solve the balance guidance distortion problem caused by howling sound aliasing.