41 results about "Subspace decomposition" patented technology

The invention belongs to the technical field of drilling, and provides an active vibration reduction method and system based on self-adaptive adjustment of a drill rod load in order to effectively reduce vibration of a drill rod. A real-time feeding oil pressure signal is converted into real-time feeding thrust, and a real-time rotating oil pressure signal is converted into real-time rotating torque; filtering the original vibration signal by adopting a singular value subspace decomposition method to obtain a real vibration signal; according to the preset initial value of the feeding thrust and the preset initial value of the rotating torque, a neural network self-adaptive PID control algorithm is used for conducting self-adaptive comparison calculation, the vibration adjustment amount is obtained, and active vibration reduction is conducted according to the vibration adjustment amount; the system comprises six closed-loop control modules including a preset parameter input module, a signal acquisition module, a signal filtering module, a control algorithm module, a control output module and an execution module, accurate vibration reduction control is achieved, the bounce phenomenon of a drill bit is eliminated, the deviation error of a drill hole is reduced, meanwhile, stick-slip vibration is restrained, and the clamping stagnation failure rate of a drill rod is greatly reduced.

The invention belongs to the technical field of crossing of information and ocean science and technology, and discloses a UUV broadside parallel array covariance weighted fusion DOA estimation method. The method comprises the following steps: firstly, calculating auto-covariance and cross-covariance matrixes of two sub-arrays, carrying out weighted fusion on covariance matrixes of different sources through differential common-array expansion of a virtual aperture, then reconstructing a complete covariance matrix by respectively adopting a convex optimization method of trace regularization and nuclear norm regularization according to the characteristics of the fused matrix, and finally, carrying out weighted fusion on the covariance matrixes of different sources. The pitch angle and the azimuth angle of the target are jointly estimated by performing subspace decomposition on the constructed extension matrix, and the effectiveness of the method is verified by a simulation result. According to the method, the precision of parameter estimation is improved through data fusion, the degree of freedom is remarkably improved under a small number of physical array elements, high-precision azimuth estimation of the UUV on multiple targets in the underwater environment is achieved, the target detection capacity of the UUV is enhanced, and the method has high engineering application value.

The invention discloses a six-phase motor driver harmonic current suppression method based on multi-subspace control, and belongs to the technical field of motor control. In order to solve the problem that low-order harmonic current of a six-phase motor in a z1-z2 subspace is difficult to effectively suppress, the invention provides a scheme that a mathematical model of the six-phase permanent magnet synchronous motor is established, multi-subspace decomposition is carried out, and stator current is divided into a d-q subspace and the z1-z2 subspace; a double-closed-loop control architecture with a speed loop as an outer loop and a current loop as an inner loop is constructed, a d-q subspace adopts a PID regulator to realize fundamental current control, and z1-z2 subspaces realize suppression of fifth and seventh harmonic currents through a parallel structure of PI and a quasi-proportional resonance controller; and a PWM signal is synthesized through inverse transformation and dual three-phase space vector pulse width modulation, and an inverter is driven to realize stable output of fundamental wave energy and harmonic suppression. The method effectively reduces the harmonic distortion rate, improves the operation efficiency and stability, and is suitable for occasions of new energy vehicles, ship propulsion, rail transit and the like.

The invention discloses a joint calibration method for multiple micro inertial measurement components based on machine learning, and the method comprises the following steps: S1, obtaining original data of multiple inertial sensors, relates to the technical field of machine learning, and can effectively map high-dimensional data to multiple local subspaces by adopting principal component analysis and a clustering algorithm, so as to improve the accuracy of calibration of the multiple micro inertial measurement components; and the dimension of the local subspace is dynamically adjusted. In the process, through dynamic dimension reduction, the computing resource consumption of the data is effectively reduced. Particularly, on the aspect of high-dimension data processing, the problem of calculation bottleneck caused by too high data dimension in a traditional method is relieved by dynamically adjusting the dimension of the subspace. Each local subspace only pays attention to one specific error type. The calculation complexity caused by high-dimensional data is reduced; according to the method, the problem of high-dimension data is effectively relieved through subspace decomposition and dynamic subspace dimension adjustment.

The invention provides a digital quantum simulation method, which comprises the following steps of: performing symmetry analysis and subspace decomposition on a target Hamiltonian to obtain an effective Hamiltonian and a symmetry constraint condition; constructing a target unitary operator according to the effective Hamiltonian, and initializing a quantum circuit population of the target unitary operator; calculating entanglement characteristics of each quantum line in the quantum line population, performing structure variation on the quantum line population based on the entanglement characteristics, and performing line fragment crossing in combination with a symmetry constraint condition to obtain a candidate line set; performing line parameter optimization on the candidate line set based on the fidelity gradient to obtain an optimized line set, and extracting gate distribution structure features of the optimized line set; and non-uniform sampling is carried out on the optimized line set based on gate distribution structure characteristics to obtain a sampling line subset, an optimal quantum line is screened out from the sampling line subset based on the deviation between an experimental observation value and a theoretical expected value, and quantum simulation is carried out based on the optimal quantum line.

The invention discloses a data processing method and system based on Lie algebraic dynamics and an entropy modulation mechanism, and relates to the technical field of artificial intelligence and data processing, and the method comprises the steps: constructing a group action operator on a physical manifold, and carrying out the manifold enhancement of input data; the Hilbert subspace decomposition and decoupling of the feature channel are completed in parallel through a learnable decorrelation kernel and tensor contraction operation by using a matrix parallel elastic orbit layer; mapping the features to a symplectic geometric phase space, and introducing an adaptive symplectic respiration operator and a Hamiltonian kinetic equation to carry out physical conservation evolution; constructing a Lie algebra classifier, and determining a data category by calculating a Lie bracket transpose norm of a feature generator and a category base; and guiding model training by using an entropy modulation action quantity loss function containing physical potential energy. The method can solve the problems of poor generalization and weak anti-noise capability of a traditional deep learning model, and can be widely applied to the fields of power equipment monitoring, computer vision, natural language processing and the like.

The invention discloses a self-adaptive subspace projection suppression method based on optical pump magnetometer external noise, and belongs to the technical field of neural magnetic imaging (MEG) signal processing.The method comprises the steps that brain magnetic signals are collected through a wearable optical pump magnetometer array and preprocessed, and a signal and noise subspace decomposition model is established; integrating a model-driven noise modeling method and a data-driven noise modeling method, and constructing a noise subspace base vector; constructing a signal subspace basis vector in combination with a guide field matrix and event correlation analysis; identifying residual noise components based on event correlation analysis, and perfecting noise subspace modeling; signal noise separation is realized by adopting a hierarchical projection strategy; and analyzing the time correlation of internal and external space signals, and constructing a time projection matrix to remove residual interference to obtain a clean MEG signal. According to the invention, various types of external noise interferences can be effectively suppressed, the signal-to-noise ratio and the reliability of brain magnetic signals are remarkably improved, and the method is particularly suitable for practical application of a wearable optical pump magnetometer system.

The invention discloses a subspace joint parameter estimation algorithm for constructing an analytic signal based on Hilbert transform in high-precision multi-surface phase-shift interference, belongs to the technical field of high-precision optical interference measurement, and aims to solve the problems of inaccurate frequency estimation caused by spectrum leakage and a fence effect in a traditional algorithm. And the problem that a modern spectrum estimation algorithm cannot directly process real-value light intensity signals is solved. The method comprises the following steps: firstly, performing Hilbert transform on a real-value light intensity sequence to construct an analysis signal; then, a Hankel matrix is constructed based on the analysis signal, and subspace decomposition is carried out; further, taking the obtained high-precision frequency as prior information, and performing joint correction on the amplitude and the phase by using a double-section phase difference correction method; and finally, reconstructing the surface topography according to the corrected phase. According to the invention, the limitation of spectrum leakage and the like is overcome, the joint fine positioning of frequency, amplitude and phase is realized, and the precision, robustness and measurement repeatability of interference parameter demodulation are obviously improved.

The application discloses a method and system for generating hyperspectral images based on hyperspectral and multispectral image fusion. Y The method comprises the following steps: performing subspace decomposition to extract left low-rank vectors U of the hyperspectral image to be generated; Z The method comprises the following steps: performing subspace decomposition to extract left low-rank vectors U of the hyperspectral image to be generated; U And right low-rank vectors V of the hyperspectral image to be generated are generated through the trained deep generation network; V The left low-rank vectors U and the right low-rank vectors V are fused to generate the hyperspectral image X The system comprises optical elements and imaging sensors for acquiring input hyperspectral images and multispectral images. Y The system comprises optical elements and imaging sensors for acquiring input hyperspectral images and multispectral images. Z The application can generate hyperspectral images based on hyperspectral and multispectral image fusion, and reduce the cost and efficiency of hyperspectral image acquisition.

The application belongs to the technical field of information and ocean science, and discloses a UUV side parallel array covariance weighted fusion DOA estimation method. First, the self-covariance and mutual covariance matrices of two sub-arrays are calculated, then the virtual aperture is extended by difference co-array, the covariance matrices of different sources are weighted and fused, then, according to the characteristics of the fused matrix, the trace regularization and kernel norm regularization convex optimization methods are used respectively to reconstruct the complete covariance matrix, finally, the pitch angle and azimuth angle of the target are jointly estimated by subspace decomposition of the constructed extended matrix, and the simulation results verify the effectiveness of the method. The application improves the parameter estimation accuracy through data fusion, significantly improves the degree of freedom under a small number of physical array elements, realizes high-precision azimuth estimation of multiple targets in the underwater environment of the UUV, enhances the target detection capability of the UUV, and has high engineering application value.

The invention belongs to the field of digital signal time-frequency analysis, and provides a frequency super-resolution time-frequency analysis method based on eigenvalue decomposition, which comprises the steps of intercepting a signal sequence along a time axis, constructing a virtual matrix, carrying out eigendecomposition on a covariance matrix, constructing a noise matrix, calculating pseudo-spectrum functions and combining the pseudo-spectrum functions along the time axis to form a time-frequency graph. The method does not depend on the number of time domain sampling points, geometric structure information of signals is extracted through subspace decomposition insensitive to noise, limitation of an STFT time window is broken through, high-frequency resolution can still be achieved under the condition of a short window, the time-frequency contradiction problem of STFT is relieved, and the method has the advantages of being simple in structure and convenient to operate. And a new technical approach is provided for accurate detection and parameter extraction of non-cooperative signals in an optical down-conversion electric field measurement system.

This invention discloses a time-frequency cooperative Doppler frequency offset suppression method and system for underwater acoustic OFDM communication, belonging to the field of underwater acoustic communication. The method includes: designing a novel channel adaptive tracking resampling factor estimation method, which performs cross-correlation processing only between two specific synchronization signal segments of the received signal, enabling it to adaptively track the time-varying channel and significantly improving the robustness of the communication system in underwater acoustic environments; based on the estimated resampling factor, performing time-domain resampling operations to transform non-uniform frequency offset into uniform frequency offset, thereby eliminating the impact of time-domain scale transformation on the underwater acoustic signal; and designing a residual uniform frequency offset suppression method based on subspace decomposition, using frequency domain eigenvalue decomposition to finely suppress the residual uniform frequency offset of the underwater acoustic signal. Combining the advantages of time-domain resampling and frequency-domain eigenvalue decomposition, it performs excellently in suppressing inter-carrier interference of underwater acoustic signals in highly mobile scenarios, significantly improving signal detection accuracy.

This invention discloses a sparse Bayesian target azimuth estimation method based on far-field dictionary reconstruction under strong near-field interference. The method includes subspace decomposition of the observation data to construct a signal subspace projection operator; projecting and normalizing the far-field dictionary to obtain a projected reconstruction dictionary, which replaces the far-field dictionary in sparse Bayesian learning, and jointly performing sparse Bayesian iterative estimation with the near-field dictionary; finally, outputting the far-field target azimuth estimation result. This method effectively partitions the subspace and constructs a projection matrix accordingly to suppress the impact of strong near-field interference on far-field azimuth estimation, significantly improving the far-field target azimuth estimation capability of the sparse Bayesian method under strong near-field interference conditions.

The present invention provides a method for precise positioning in a terrestrial NLOS environment based on spatial multivariate information fusion. The method comprises: step 1: constructing a multipath signal reception model based on a given positioning station and known reflectors; step 2: building a spatial point model, and constructing an error-containing multipath signal reception model based on the multipath signal reception model and the spatial point model; step 3: performing a Fourier transform on the error-containing multipath signal reception model; step 4: calculating the covariance matrix of the received signal, and performing subspace decomposition on the covariance matrix to obtain a noise subspace; step 5: utilizing the orthogonality of the signal subspace and the noise subspace to construct an objective function for the noise subspace, wherein the position corresponding to the minimum value of the objective function is the estimated value of the target position; and step 6: fusing information from the positioning station at P moments to obtain a final estimate of all target positions.

The invention discloses an old and ancient book cover image retrieval method based on dynamic partitioning and multi-modal feature fusion, and relates to the technical field of image recognition, old and ancient book cover images are used as query vectors to be input into a retrieval model, and a matched book list corresponding to old and ancient books is obtained according to the query vectors; the training process of the retrieval model is as follows: performing visual feature extraction and text feature extraction on the cover image of the ancient and old book, and partitioning; inputting the blocked visual features and text features into a cross attention mechanism, and generating an original high-dimensional fusion feature vector including a layout text structure and image layout information; performing subspace decomposition and quantization coding on the original high-dimensional fusion feature vector by adopting a product quantization algorithm to obtain a compressed feature piece vector, and constructing an ancient and old book feature database according to the compressed feature piece vector; taking cover images of the old and ancient books as input query vectors, indexing the old and ancient book database by adopting a three-level progressive query architecture, and returning a matched book list; the trainable parameters of the retrieval model are jointly optimized by constructing a total loss function; according to the retrieval method, multi-level understanding and digital reconstruction of ancient book covers are achieved, and rapid retrieval is achieved through efficient index construction.

The invention discloses a communication fault positioning method based on comparative learning, and the method specifically comprises the steps: obtaining link state data, node operation data and alarm data, and building a unified time index to generate a communication state feature sequence; performing vector coding and normalization to generate an initial feature representation set; executing sample division and sample pair construction to generate a positive and negative sample pair set; subspace decomposition is executed to generate a fault feature subspace and an environment feature subspace, and a fault prototype vector set, a prototype drift sequence and a constraint feature representation set are generated; performing similarity calculation and contrast loss calculation to generate a gradient vector set, and performing projection decomposition on conflict gradients to generate a corrected gradient set; executing parameter updating according to the correction gradient to generate a communication state feature representation set; and calculating the distance between the feature representation and the fault prototype, determining a fault type identifier, and outputting a fault positioning result. According to the invention, multi-source fusion comparison positioning is realized, the precision is high, and the stability is strong.

This invention proposes a beamforming method for ISAC systems based on subspace decomposition and scalar optimization. The invention mainly includes the following steps: (1) deriving the Fisher information matrix for the target angle, time delay, radial velocity, and target reflection coefficient; (2) inverting and diagonalizing the Fisher information matrix to obtain the expression for the joint Cramé-Rao bound; (3) constructing a beamforming optimization problem with the goal of minimizing the joint CRB; (4) decomposing and simplifying the covariance matrix into a scalar optimization problem using the channel subspace characteristics; (5) providing closed-form optimal solutions for beamforming vectors for different antenna configurations; and (6) reconstructing the beamforming vectors based on the closed-form optimal solutions to obtain a transmit waveform that meets both sensing accuracy and communication requirements. This invention can significantly improve the parameter estimation accuracy for moving targets, effectively balance sensing and communication performance, and has low computational complexity, making it convenient for practical engineering deployment.

The application discloses an image risk concept continuous erasing method and system based on feature orthogonality. In the first erasing session, the sample features of the image classes to be forgotten and the image classes to be reserved are decomposed in subspaces by singular value decomposition, and the forgetting subspace and the reserved subspace are constructed respectively. In the forward propagation process of the image classification model, the sample features of the image classes to be forgotten are orthogonally projected into the forgetting subspace to weaken their discriminability, and the sample features of the image classes to be reserved are aligned to the reserved subspace to enhance their stability. In the next erasing session, a new data deletion request task is obtained, the forgetting subspace is expanded and the reserved subspace is contracted, the cumulative erasing among multiple tasks is realized, and the integrity of the reserved knowledge is maintained. The feature layer orthogonal constraint mechanism constructed by the application can effectively balance the irreversible forgetting and the stability of the reserved knowledge, and is suitable for the risk concept continuous erasing demand of continuous image data deletion requests in real scenes.

The invention discloses a decomposition deconvolution beam forming method based on subspace clustering, and belongs to the field of direction of arrival estimation in passive detection. According to the invention, the problem of poor target discovery capability of the existing deconvolution beam forming algorithm is solved. According to the spectral clustering-based subspace decomposition method provided by the invention, the covariance matrix can be adaptively decomposed into the strong signal subspace and the weak signal subspace according to the size of the characteristic value without estimating the number of information sources, so that the problem of array gain reduction of a traditional deconvolution beam forming algorithm in a strong interference scene is solved; and the target discovery capability is improved. According to the DOA estimation method provided by the invention, null constraint deconvolution beam forming and a signal-to-noise ratio-based fusion algorithm are introduced, so that energy leaked to weak signal subspace components by interference can be effectively suppressed under non-ideal conditions, the influence of strong interference on a weak target can be effectively suppressed, and the robustness of the algorithm and the target discovery capability are improved. The method can be applied to direction-of-arrival estimation in passive detection.

The invention relates to the technical field of target state detection, and discloses a multi-moving target state anti-interference detection method based on an MIMO OFDM communication system, which comprises the following steps: a base station based on the MIMO OFDM communication system transmits a downlink OFDM detection signal, and if the signal is reflected by a moving target and an interference source, receiving data is obtained; estimating the angles of the moving target and the interference source; designing a receiving beam based on the estimated moving target angle and interference source angle through a beam former, enabling the receiving beam to be aligned with a target signal direction, introducing an interference receiving gain upper bound as a constraint, and obtaining a receiving signal only containing all target information; and demodulated receiving signals are processed by using a target state joint solving method based on subspace decomposition and rotation invariance, and joint estimation of the speed and the distance of each moving target is completed respectively. According to the method, the problem of insufficient precision of joint detection in a multi-dimensional domain in the prior art is solved, and the method has the characteristics of low complexity and high precision.

The invention relates to a fast open-loop GNSS-R height measurement method and system based on space-time frequency combination, and the method comprises the steps: collecting multi-frequency GNSS signals, carrying out the coarse time delay and coarse Doppler frequency offset estimation of each multi-frequency GNSS signal, and carrying out the compensation of the multi-frequency GNSS signals through coarse Doppler frequency offset; based on the coarse time delay and the compensated multi-frequency GNSS signal, carrying out forming processing on a wave beam in a time domain direction, and constructing a space-time-frequency joint vector; through a spatial domain smoothing technology, rank increasing is carried out on the autocorrelation matrix of the space-time-frequency joint vector, and the problem of rank deficiency of the autocorrelation matrix is solved; performing feature analysis on the autocorrelation matrix according to a subspace decomposition method, and estimating direct view path time delay, specular reflection path time delay and a direct view path incoming angle; and deducing a satellite elevation angle according to the direct-view path incoming angle, and inverting the height from the receiver to the ground surface through the delay inequality of the direct-view path and the mirror reflection path and the satellite elevation angle. According to the technical scheme of the invention, high-precision effective estimation of the height of the receiver can be realized quickly with low complexity.

The invention discloses a multi-modal sentiment analysis method based on progressive comparative learning, and belongs to the technical field of sentiment analysis, and the method comprises the steps: carrying out the feature extraction of text, audio and video modals in a multi-modal data set; separating generality and characteristics between modes, and performing subspace decomposition; optimizing the text by using the feature subspaces of the audio and the video, and carrying out multilayer progressive contrast learning to obtain optimized text features; enhancing subspace features of the audio and the video by using the optimized text features; feature stacking and dimension reduction are carried out, emotion weights are calculated, and emotion perception weighted fusion and prediction are carried out based on the emotion weights. According to the method, the problems of modal imbalance, noise interference, low feature quality, insufficient utilization of comparative learning and lack of emotion pertinence of a fusion strategy in the prior art are solved. According to the method, the feature discrimination is higher, modal imbalance and noise interference can be effectively relieved, the fusion process is more targeted, emotion clues can be accurately captured, and high-performance emotion analysis can be realized.

The invention provides an array signal processing method and device, electronic equipment and a storage medium, and relates to the technical field of radar signal processing, receiving signals of a non-uniform array are mapped into receiving data of a virtual uniform array, and a covariance matrix is reconstructed by using a neural network model based on the data, so that the array signal processing efficiency is improved. And finally, estimating the direction of arrival of the signal through subspace decomposition and polynomial rooting operation, so that the problems of low estimation precision and narrow application range caused by the fact that a non-uniform array structure does not meet application conditions of a traditional algorithm, covariance matrix estimation is greatly influenced by signal conditions and subspace discrimination is insufficient in the prior art can be solved. The technical effects of adapting to a non-uniform array scene, improving the accuracy and robustness of covariance matrix estimation under a wide signal-to-noise ratio, enhancing the distinction degree of a signal and a noise subspace, further improving the direction-of-arrival estimation precision and resolving power and having physical interpretability are achieved.

This invention proposes a fusion subspace DOA estimation method based on sparse Bayesian learning. The implementation steps are as follows: first, an overcomplete steering vector dictionary matrix is ​​constructed, and a sparse representation model is built. Then, the signal is reconstructed using the sparse Bayesian learning algorithm. After obtaining the reconstructed signal, a signal covariance matrix is ​​constructed. Finally, eigenvalue decomposition is performed on the covariance matrix to construct a spatial spectral function, and the peak value of this spectral function is searched across the entire angular range. This invention integrates the SBL algorithm and the MUSIC algorithm. The MUSIC algorithm is used to perform subspace decomposition on the reconstructed covariance matrix, extracting the noise subspace and utilizing its orthogonality with the steering vector, thereby improving the angular resolution of the SBL algorithm. This makes this invention applicable to achieving high-resolution DOA estimation under conditions of few or even single snapshots.

The invention discloses an image risk concept continuous erasing method and system based on feature orthogonality. The method comprises the following steps of: performing subspace decomposition on sample features of an image category needing to be forgotten and a reserved image category through singular value decomposition in a first erasing session, and respectively constructing a forgotten subspace and a reserved subspace; in the forward propagation process of the image classification model, orthogonally projecting sample features of an image category needing to be forgotten to a forgotten subspace so as to weaken the discrimination, and aligning sample features of a reserved image category to a reserved subspace so as to enhance the stability of the reserved subspace; and obtaining a new data deletion request task in the next erasure session, expanding the forgetting subspace and shrinking the reserved subspace to realize accumulated erasure among multiple tasks, and meanwhile, keeping the integrity of the reserved knowledge. The feature layer orthogonal constraint mechanism constructed by the invention can effectively balance forgetting irreversibility and retention stability, and is suitable for the requirement of continuously erasing the risk concept of the continuous image data deletion request in a real scene.

The invention discloses a super-resolution multipath time delay estimation method for Fourier precoding OCDM signals in a dynamic scene, and the method comprises the steps: carrying out the Fourier precoding of a pilot symbol, generating a precoding orthogonal linear frequency modulation wavelength division multiplexing signal through modulation, and eliminating the coupling effect between the time delay and Doppler frequency shift in a time-frequency double-selection channel; doppler frequency shifts of different paths are directly estimated according to the position offset of the received pilot signals in the Fresnel domain, and the number of the multiple paths is determined; and calculating a covariance matrix of the received signal, carrying out decoherence processing, carrying out subspace decomposition on the covariance matrix by using a multiple signal classification algorithm to obtain a spatial spectrum, and determining a multipath time delay estimation value according to a spectrum peak position of the spatial spectrum. The method is suitable for underwater acoustic mobile communication and environment perception with Doppler frequency shift, the relative root-mean-square error of time delay estimation can be remarkably reduced in a high-speed state, and a reliable solution is provided for super-resolution time delay estimation in a dynamic scene.

This invention discloses a guided process construction method based on industrial modeling, comprising the following steps: transforming the target industrial process construction problem into a Markov decision process model; generating intrinsic reward signals based on the novelty of the target industrial process path, historical access confidence, or model prediction error; assigning extrinsic reward signals to each stage or subtask of the target industrial process through a multi-layered extrinsic reward mechanism; inputting the intrinsic and extrinsic reward signals together as the optimization objective into a deep Q-network based on subspace decomposition; and automatically generating an industrial process structure and parameter configuration scheme that satisfies the predetermined optimization objective or process constraints based on the output of the deep Q-network based on subspace decomposition. This invention enables efficient and intelligent optimization and automatic modeling of industrial process schemes in sparse reward and high-dimensional complex process scenarios through a multi-layered reward mechanism and a deep Q-network based on subspace decomposition.

The application relates to a wide-band sensing method based on a MIMO-based MWC sub-Nyquist sampling structure, which comprises the following steps: a MIMO-based MWC wide-band sampling front end is used to model a signal in an mCSL matrix; a static covariance matrix is constructed by using the correlation between column vectors of the matrix; subspace decomposition is performed on the static covariance matrix to obtain eigenvalues corresponding to signals and eigenvalues corresponding to noises, and noise reconstruction is performed; according to the orthogonality of the signals and the noises, a calculation vector based on the mCSL matrix structure is obtained based on the reconstructed noises, and the calculation vector is sorted; support sets corresponding to the first K values of the calculation vector are used as negative frequency points where PU users are located, and all center frequency points of the PU users are obtained according to the symmetry of real signal spectrum. The application can improve the spectrum recovery effect.

This invention discloses an off-grid DOA estimation method and system for polar impulse noise environments, belonging to the field of polar underwater acoustic detection. The method includes: obtaining an array received data matrix based on signals received through a uniform linear array with half-wavelength spacing; initializing the maximum number of iterations, column full-rank random matrices, and row full-rank random matrices; iteratively solving the improved mixed correlation entropy of the residual fitting error matrix as the objective function for subspace decomposition optimization based on the maximum mixed correlation entropy criterion to obtain the optimized array received data matrix; and using an iterative sparse projection algorithm to jointly estimate the sparse signal matrix and grid offset based on a new off-grid model constructed using multi-order Taylor expansion terms, thereby estimating the target's DOA. This invention overcomes the grid mismatch problem to achieve high-precision estimation of target location and exhibits good performance in both Gaussian noise and impulse noise environments.

The application discloses a structure settlement visual measurement method and system based on deep learning compensation, and relates to the technical field of building settlement measurement. The method comprises the following steps: according to a mobile detection platform, driving a detection component to collect a structure region and determining structure data; constructing a settlement measurement component by spatial decomposition and implicit causal analysis of settlement dimensions, motion dimensions and interference dimensions; performing background domain segmentation on the structure data, executing subspace decomposition feature recognition and implicit causal coupling analysis, and determining settlement data pairs; performing directional projection and three-dimensional mapping through a three-dimensional projector, realizing three-dimensional distance measurement, and generating and updating a settlement curve display. The technical problems of the existing structure settlement measurement, such as the difficulty in effectively correcting the spatial displacement measurement error caused by attitude change and environmental interference, and the insufficient distance measurement accuracy, are solved, the technical effect of realizing high-precision non-contact distance measurement and dynamic compensation of structure settlement displacement is achieved, and the stability of spatial distance measurement and the environmental adaptability are improved.