98 results about "Signal subspace" patented technology

The invention belongs to the crossing field of information and ocean science and technology, and discloses a DOA estimation method for a UUV broadside parallel co-prime array. According to the method, firstly, a virtual extension array is generated by utilizing an array auto-covariance and cross-covariance matrix, then, Toeplitz completion is carried out on'holes' in the virtual array through trace norm regularization and nuclear norm regularization constraints, a complete covariance structure is recovered, and finally, an extension matrix is constructed, and rotation invariance of a signal subspace of the extension matrix is utilized, so that the covariance structure of the virtual array is obtained. The pitch angle and the azimuth angle of the target are jointly solved, automatic angle pairing is achieved, and the effectiveness and the reliability of the method are verified through simulation results. According to the method, aiming at the application challenges that the UUV broadside array space is limited and the underwater environment is complex, all virtual array elements of the broadside parallel co-prime array are fully utilized, the array freedom degree and estimation precision are remarkably improved, the target detection capacity of the UUV is effectively enhanced, and the method has high practical engineering application value.

The invention provides a sound wave signal self-supervised learning enhanced robust noise reduction processing method and system, and relates to the technical field of signal processing, and the method comprises the steps: carrying out the feature enhancement of an initial time-frequency representation through a dynamic adaptive mask strategy, constructing a self-supervised reconstruction task based on the mask time-frequency representation, separating noise and signal subspaces in a semantic manifold space, and carrying out the self-supervised learning enhanced robust noise reduction. And establishing a probability propagation network in combination with time domain continuity characteristics to model a local dependency relationship, and finally generating a noise reduction weight and realizing semantic fidelity optimization. The method can effectively improve the noise reduction effect and semantic integrity of sound wave signals in a noise complex environment.

The invention discloses a vector hydrophone array orientation estimation method for performing tensor decomposition by using a propagation operator, relates to the technical field of vector hydrophone array orientation estimation, and discloses a vector hydrophone array orientation estimation method for performing tensor decomposition by using a propagation operator. The method comprises the following steps: firstly, constructing a three-dimensional array manifold tensor composed of an array direction matrix and a vector hydrophone output matrix; respectively expanding received signal tensors according to three modes, solving a propagation operator based on a column block covariance matrix, constructing a normalized signal subspace, establishing a spatial spectrum function with a noise subspace, and obtaining a pitch angle and an azimuth angle of a sound source through spectrum peak search; according to the method, high-order singular value decomposition is avoided, the operand is greatly reduced, meanwhile, high resolution and low sidelobe direction finding performance are kept, and the method is suitable for a real-time underwater acoustic direction finding system of a ship-borne platform, a buoy platform and an unmanned platform.

The invention provides a near-field electromagnetic source positioning method based on array signal processing, and relates to the technical field of electromagnetic signal detection and positioning, and the method comprises the steps: constructing an array receiving signal model based on the wave-front characteristics of an electromagnetic wave spherical surface, and employing a steering vector for the construction; a sensor array is used for collecting near-field radiation source signals, and amplitude calibration processing is carried out on the near-field radiation source signals to form an array data set. According to the near-field electromagnetic source positioning method based on array signal processing, high-precision three-dimensional positioning of a near-field electromagnetic source is realized by combining amplitude calibration, characteristic decomposition and joint optimization processing through an array receiving signal model based on electromagnetic wave spherical wavefront characteristics. According to the near-field electromagnetic source positioning method based on array signal processing, a multi-dimensional optimization strategy is introduced in the signal subspace extraction and parameter estimation process, the estimation precision of the distance, the pitch angle and the azimuth angle is improved, and the positioning stability and robustness in a complex electromagnetic environment are improved.

A voice activity detection method and system and a voice enhancement method and system are provided. A voice presence probability of a target voice signal present in microphone signals may be determined by calculating a linear correlation between a signal subspace where the microphone signals are located and a target subspace where the target voice signal is located. The voice enhancement method and system may be used to calculate filter coefficients based on the voice presence probability, so as to perform voice enhancement on the microphone signals. The calculation accuracy of the voice presence probability is improved, and the voice enhancement effect is also improved.

The invention discloses a very high frequency broadband radiation source positioning method based on energy weighted subspace fitting, and relates to the technical field of signal processing and radiation source imaging, and the method comprises the steps: carrying out the DFT processing of a lightning very high frequency broadband signal collected by an array, and obtaining any narrowband component; then characteristic decomposition is carried out on covariance matrixes of X-axis and Y-axis sub-arrays according to array receiving signals, main characteristic vectors are taken as signal sub-spaces, in order to emphasize the signal sub-spaces with higher energy, characteristic values are used for weighting, weighted signal sub-spaces are constructed, then the signal sub-spaces are fitted by using a least square method, and the signal sub-spaces with higher energy are obtained; the method comprises the following steps: further constructing a sub-band energy weighted spatial spectrum adapted to a very high frequency broadband signal, then carrying out spectrum peak search on the weighted spatial spectrum to obtain a spatial angle of a spectrum peak, and finally calculating the direction of arrival of a radiation source according to a spatial angle relation to obtain an azimuth angle and an elevation angle. According to the invention, aiming at a lightning very-high-frequency weak radiation source positioning problem, a lightning channel can be inverted more clearly.

This document describes techniques and systems for radar systems with an angle determination process for sparse uniform arrays. The described radar systems include a processor and an antenna that can receive electromagnetic energy reflected by an object in a surrounding environment. The antenna includes a one-dimensional (ID) or two-dimensional (2D) sparse array. The processor can use the received electromagnetic energy to determine a signal subspace associated with the object that includes an invariance equation. Using an estimated solution of the invariance equation, the processor determines a solution of the invariance equation. The solution of the invariance equation is used to determine an angular phase associated with the object. The processor can then use the angular phase to determine an angle associated with the object. In this way, the described angle determination process enables a radar system with a sparse array to efficiently determine an angle associated with an object without blind spots.

The application discloses a kind of real-time detection method and system of food pollutant based on intelligent sensor, comprising: through the multimodal sensor array of deployment in food processing production line key station, sensor response signal is collected;Baseline response under the condition of no pollution is predicted by establishing dynamic baseline prediction model;Signal separation network based on variational decoupling autoencoder is constructed, residual signal is mapped to low-dimensional latent space by sharing encoder and is divided into baseline error subspace and pollutant signal subspace, respectively by two independent decoders reconstructing baseline prediction error signal and pollutant candidate signal;Multi-scale time-frequency transform is carried out to pollutant candidate signal to identify pollutant type and estimate concentration;Through distributed intelligent agent cooperation, pollution traceability and hierarchical early warning are carried out;Cross-scene adaptive calibration is realized using meta-learning framework.The application realizes the real-time decoupling of pollutant signal and matrix change signal in food processing process.

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 invention discloses a holographic MIMO near-field channel estimation method based on a spatial correlation matrix, and the method comprises the steps: carrying out the modeling of a 3D H-MIMO channel based on a non-uniform spherical wave model, and building an H-MIMO near-field channel model; calculating a spatial correlation coefficient between antenna units at a base station end, introducing a generalized single-ball scatterer model, and deducing to obtain a near-field spatial correlation matrix; eigenvalue decomposition is carried out, and a low-dimensional signal subspace is constructed; designing a pilot frequency matrix, wherein the pilot frequency time slot number in the pilot frequency matrix is set as the rank of the low-dimensional signal subspace; and estimating the low-dimensional signal subspace coefficient to obtain a channel estimator of the low-dimensional signal subspace. According to the channel estimation method, spatial correlation prior information is effectively utilized, and the pilot frequency overhead and the calculation complexity are remarkably reduced.

The invention provides an angle capturing method based on a two-dimensional MUSIC algorithm and related equipment, and relates to the technical field of wireless communication. The method comprises the following steps: receiving a sampling signal and a radio frequency signal, and obtaining a plurality of snapshot data according to the radio frequency signal; calculating a covariance matrix of the radio frequency signal based on the snapshot data, performing eigenvalue decomposition on the covariance matrix, and determining a signal subspace and a noise subspace according to the obtained eigenvalue; calculating a two-dimensional MUSIC spatial spectrum based on the noise subspace; calculating a dynamic threshold, and obtaining a plurality of candidate angles according to the dynamic threshold; performing image rejection processing on each candidate angle to obtain an effective candidate angle, accumulating the effective candidate angle into a two-dimensional statistical histogram, and processing to obtain a two-dimensional probability distribution diagram; selecting a first estimation angle from the two-dimensional probability distribution map; and determining an angle capture state of the target based on the first estimated angle and a second estimated angle at the previous moment. By adopting the method, the angle capturing accuracy can be improved.

The invention discloses a high-impedance fault detection method based on transient signal spectrum differences, and belongs to the technical field of power distribution network fault detection. Transient zero-sequence current signals of all lines of a power distribution network are collected, and high-resolution spectrum characteristic spectrums corresponding to all the lines are generated through the separation step of signal subspaces and noise subspaces; establishing a frequency spectrum matrix difference quantification model, converting the frequency spectrum characteristic spectrum into a two-dimensional data matrix, and quantifying frequency spectrum difference characteristics among different lines by adopting a matrix norm calculation method to obtain a difference coefficient; based on a double-layer discrimination threshold system, setting a fault similarity coefficient as a first-layer discrimination threshold, and screening out a potential fault line combination; and setting a fault judgment coefficient as a second-layer judgment threshold value, performing difference comparison on the potential fault line and the normal line, and identifying a high-impedance fault line. The influence of normal transient disturbance such as load switching and capacitor bank switching is eliminated by using the characteristic of transient signal frequency spectrum difference.

This invention belongs to the field of signal processing, and particularly relates to direction-of-arrival (DOA) estimation for electromagnetic and sonar signals. Specifically, it is a subspace-based DOA estimation method based on a distributed algorithm, which can be used for passive localization and target detection. The method includes the following steps: S1, establishing a distributed array model at the receiver; S2, calculating the signal subspace of the received signal; S3, calculating the weight w(θ); S4, calculating the spatial spectrum and performing a spatial domain search to find the DOA value. The proposed method achieves distributed solution of the algorithm, thus fundamentally avoiding the shortcomings of centralized algorithms. Simultaneously, it maintains good estimation performance, similar to that of centralized algorithms, and outperforms distributed MUSIC methods at low signal-to-noise ratios.

The invention discloses a Bernoulli filter-based single target detection and DOA tracking method and system, and relates to the field of target tracking. The method solves the problems that a traditional tracking algorithm after detection cannot jointly detect the target when the underwater target repeatedly enters under the conditions of low signal-to-noise ratio and small snapshot, and the tracking effect is unstable. The method comprises the steps of constructing a Bernoulli random finite set model, defining a single target state as a dynamic description target state or a dynamic description target state, predicting the target existence probability by using a Bernoulli Markov process, and generating a prediction state distribution particle set composed of new particles and survival particles through particle filtering. A hydrophone array signal is decomposed into a noise subspace observation model and a signal subspace observation model, a generalized likelihood function after exponential weighting is used for updating a target posteriori existence probability and a particle weight, and an equal-weight particle set is generated through resampling. And judging whether the target exists or not according to the single-target posterior existence probability, thereby realizing single-target DOA joint detection and tracking.

The application relates to the field of radio astronomy signal processing, and discloses a radio frequency interference reduction method and device for a phased array radio telescope, wherein the method comprises the following steps: acquiring relative position information of a plurality of navigation satellites, determining potential interference satellites from the plurality of navigation satellites according to the relative position information; acquiring a wideband signal collected by the phased array radio telescope, and determining a plurality of interfered subbands in the wideband signal; matching the interfered subbands with the potential interference satellites, and determining subband interference information of the interfered subbands according to a matching result; acquiring a signal subspace and an interference subspace of the interfered subbands, and then determining a skew projection matrix of the interfered subbands; and simultaneously performing interference reduction and digital beam forming on the interfered subbands according to the skew projection matrix and the signal subspace. The technical scheme provided by one or more embodiments of the application can realize continuous interference reduction of the phased array radio telescope on the in-orbit satellites.

The invention discloses a feature subspace orientation estimation method, device and equipment of an unknown signal source number in a phase modal domain. The feature subspace orientation estimation method comprises the following steps: receiving sound pressure signals emitted by signal sources in a space by adopting a uniform circular ring-shaped sensor array; decomposing the sound pressure signal based on a phase modal decomposition theory to obtain a multi-order modal signal and a phase modal domain guide vector; constructing a phase modal domain signal covariance matrix by using the multi-order modal signal, decomposing the characteristics of the phase modal domain signal covariance matrix, and extracting a minimum characteristic value as a noise power approximate estimation value; based on the noise power and a limited larger integer, approximately solving related parameters of the signal subspace and the noise subspace to obtain a feature subspace MUSIC scanning vector under an unknown signal source number; and according to the feature subspace MUSIC scanning vector and the phase modal domain signal covariance matrix under the unknown information source number, calculating an orientation spectrum, and realizing feature subspace orientation estimation of the unknown information source number. The method does not need to know the number of information sources in advance, and improves the orientation estimation performance.

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 application discloses a method for suppressing main lobe interference of a multi-station radar system based on oblique projection. The method aims to solve the problem that the estimation of noise subspace is not accurate due to the energy leakage of interference, so that the noise subspace is no longer orthogonal to the interference subspace, and the orthogonal projection anti-interference performance is invalid. The implementation steps of the application include: directing the receiving antenna beams of each radar station in the multi-station radar system to the target and the area where the jammer is located; aligning the echo baseband signal vectors of each radar station in the multi-station radar system in the time domain to obtain a receiving signal matrix of the radar system; generating a covariance matrix; estimating a signal subspace; constructing an oblique projection operator; and multiplying the receiving signal matrix of the radar system by the oblique projection operator to obtain a signal after interference suppression. The application has the advantage that the suppression type interference signal entering from the main lobe of the radar station antenna can be effectively suppressed in a low signal-to-noise ratio scene.

The invention provides a gain phase error calibration DOA (direction of arrival) estimation method for a movable antenna array, and aims to optimize local gain and phase error problems caused by calibration errors in an actual system. On the basis of a two-stage structure, firstly, a rotation-invariant signal subspace is constructed, an estimation problem of a gain phase error is converted into a constrained least square problem, a closed-form solution is obtained by using a Lagrangian multiplier method, and joint estimation and compensation of the error are realized. And then, in the second stage, constructing a noise reduction covariance matrix and introducing an estimated gain phase error for compensation, and finishing high-precision direction-of-arrival estimation in combination with a MUSIC algorithm. A simulation experiment shows that compared with an existing method, the self-calibration method provided by the invention shows more excellent direction of arrival estimation performance in a movable antenna array. The method does not need extra error distribution priori knowledge, has good practicability, and is suitable for application scenes needing accurate DOA estimation, such as wireless sensing and radar.

The application belongs to the technical field of target positioning, and particularly relates to a 2D-DOA estimation method of a reconfigurable intelligent surface assisted MISO system. The method establishes a far-field signal model of the RIS assisted MISO system, and the far-field signal model is composed of M RIS panels, K target signals and a receiving end. According to the far-field signal model, a receiving data expression is constructed, a meshless sparse reconstruction is performed by using an atomic norm, and a sparse reconstruction signal is obtained. The obtained sparse reconstruction signal is expressed in the form of a Hankel matrix, singular value decomposition is performed on the Hankel matrix, and a signal subspace and a noise subspace are obtained. In combination with a MUSIC algorithm, the azimuth and the elevation of the target are obtained by using spectral peak search.

The application provides a two-dimensional DOA estimation method for arbitrary array single-base MIMO radar based on data rearrangement, and relates to the technical field of array sensor direction finding. The rank structure of a coherent signal covariance matrix is recovered by rearranging a received data matrix through arbitrary array element positions of a transmitting array and a receiving array; S2: eigenvalue decomposition is performed on the signal covariance matrix to obtain a signal subspace; S3: reliable rough estimation is obtained through rotation invariance; S4: an integer ambiguity vector corresponding to spatial phase ambiguity caused by array element spacing greater than half a wavelength is solved; S5: the phase of an array flow pattern with phase ambiguity is compensated, and an accurate estimation value of the 2D-DOA of the target is calculated; the high-precision 2D-DOA estimation problem in a complex scene of a coherent signal source, arbitrary array geometry and phase ambiguity caused by part of array element spacing greater than half a wavelength is solved, unambiguous and high-precision angle parameter estimation is realized, and the estimation accuracy and algorithm reliability are improved.

The invention relates to the technical field of electric power system signal processing, in particular to an electric power system broadband oscillation intelligent monitoring and data transmission method, system and device and a storage medium. The method comprises the following steps: carrying out modal decomposition on a broadband oscillation signal by adopting variational modal decomposition, rejecting a noise dominant component and reserving an effective modal component, constructing a self-adaptive fixed-order TLS-ESPRIT algorithm based on a singular value adjacent growth ratio, adaptively determining a signal subspace order, establishing an improved variational auto-encoder to carry out data compression processing, and carrying out data compression processing. Label data is used as an additional condition to be fused into a coding process, the modal aliasing problem of a traditional method in complex signal processing is solved, and accurate and automatic identification of broadband oscillation parameters in a strong noise environment is achieved. The limitation that a traditional auto-encoder is poor in adaptability to new frequency components is overcome, efficient data compression is achieved on the premise that reconstruction precision is guaranteed, and the problem of communication bandwidth limitation under high-speed sampling is solved. And a complete technical chain from signal preprocessing to parameter identification to data transmission is formed.

The application provides a robust denoising processing method and system enhanced by acoustic wave signal self-supervised learning, relates to the technical field of signal processing, and comprises the following steps: performing feature enhancement on an initial time-frequency representation through a dynamic self-adaptive mask strategy; constructing a self-supervised reconstruction task based on the mask time-frequency representation; separating noise and signal subspaces in a semantic manifold space; combining time-domain continuity features to establish a probability propagation network to model local dependent relationships; and finally generating denoising weights and realizing semantic fidelity optimization. The application can effectively improve the denoising effect and semantic integrity of acoustic wave signals in a noise complex environment.

This invention discloses a single-shot direction-of-arrival (DOA) estimation method based on hyperbolic tangent kernel correlation entropy. The method involves establishing a single-shot sampling signal model; constructing a low-order matrix based on hyperbolic tangent kernel correlation entropy; constructing an orthogonal projection matrix to obtain a single-shot weighted signal subspace fitting equation based on hyperbolic tangent kernel correlation entropy; initializing a quantum locust population and setting parameters; calculating the fitness value of the quantum locust's mapped state position and recording the quantum position corresponding to the mapped state position with the largest fitness value; updating the quantum locust's social position and adaptive weight coefficients to generate Fibonacci weights; updating the quantum locust's quantum position and global optimal position using a simulated quantum rotation gate according to the locust and Fibonacci search strategy; calculating the iteration to the maximum number of iterations and outputting the mapped state position of the last generation of globally optimal quantum locusts as the DOA estimation result of the single-shot direction finding. This invention can effectively perform direction finding for both independent and coherent sources under impulsive noise conditions.

The invention discloses a wave direction angle analysis method and equipment, and belongs to the field of wave test simulation. The method comprises the following steps: 1) generating a target area wave height data array; 2) performing forward and backward smoothing on the wave height data array of the target area to obtain a doubled data array; 3) calculating a covariance matrix of the doubled data array; 4) calculating an optimized covariance matrix by using a spatial smoothing method; 5) calculating all eigenvalues and eigenvectors of the optimized covariance matrix; 6) arranging according to the sizes of the characteristic values, distinguishing signal subspaces / noise subspaces, and constructing a noise matrix; 7) performing translation and reciprocal weighting processing on the characteristic value of the noise subspace to obtain a weighted noise matrix; 8) constructing a spectral function by using an orthogonal relationship between the wave steering vector and the weighted noise matrix, and 9) calculating the spectral function corresponding to all angles, wherein the angle corresponding to the peak value of the spectral function is the oblique wave direction estimation angle.The method can quickly and accurately calculate the wave field direction angle in real time.

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 invention provides a three-dimensional joint parameter estimation method based on Wi-Fi (Wireless Fidelity) beam forming feedback information (BFI). The three-dimensional joint parameter estimation method based on the Wi-Fi beam forming feedback information (BFI) is characterized in that the three-dimensional joint parameter estimation method based on the Wi-Fi beam forming feedback information (BFI) is provided; the method comprises the following steps: firstly, acquiring BFI of a direct path and a target reflection path between transceivers; secondly, constructing the BFI into a three-dimensional matrix about three dimensions of an antenna, a subcarrier and a data packet, performing smooth decoherence processing on the matrix, and calculating a covariance matrix of the smoothed matrix; secondly, performing characteristic decomposition on the covariance matrix to obtain a signal subspace and a noise subspace; and finally, constructing a spectral function, and obtaining an angle of departure (AOD) between a direct path and a target reflection path, relative time delay and relative Doppler frequency shift through spectral peak search. Based on the three parameters obtained through estimation, the method can be applied to scenes such as positioning or breathing monitoring. According to the method, complete channel state information (CSI) does not need to be accessed, the CSI can be directly extracted from the communication process, and the method has natural robustness for carrier frequency offset and sampling frequency offset, and is easier to realize large-scale popularization and application.

The application relates to a source number estimation method based on orthogonal matching pursuit and signal subspace matching, and relates to a source number estimation method. The application aims to solve the problem that in an underwater acoustic environment, the signal-to-noise ratio of array snapshot data is usually not high, and is easily disturbed by colored noise and coherent sources, and for a high-speed moving target, the effective snapshot number is small, thus leading to low accuracy of traditional source number estimation. The application uses an orthogonal matching pursuit method to iteratively solve a support vector, and uses a characteristic vector corresponding to a maximum characteristic value of a residual to construct two subspaces, and realizes source number estimation through a signal subspace matching criterion. Simulation results show that compared with existing source number estimation methods, the application method has a lower requirement for a signal-to-noise ratio, performs better under small snapshot conditions, and is not sensitive to coherent sources and colored noise. Lake test data processing results show that the method can effectively estimate the number of fixed and moving underwater acoustic targets.

The invention relates to a sensing method, device and system, communication equipment and a storage medium. The sensing method comprises the following steps: receiving first information, wherein the first information is used for indicating a first moment and a second moment; and transmitting second information to the first communication device, the second information including subspace information obtained by projecting a signal subspace of the channel at the second time point to a noise subspace of the channel at the first time point, the subspace information being used to estimate a perception amount of a scatterer or a target that varies between the first time point and the second time point. According to the embodiment of the invention, the interference of the known scatterer / target can be reduced or even eliminated, the scatterer / target which changes within the given time is concentrated, a predefined event can be triggered according to the change of the scatterer / target, soft combination can be realized under a cooperative sensing framework, and the sensing precision can be improved.

The application relates to a kind of based on reconstruction subspace's super-resolution two-dimensional signal angle of arrival estimation method, comprising the following steps: S1, covariance matrix is constructed;S2, signal subspace matrix and noise subspace matrix are established;S3, construct two-dimensional multiple signal classification algorithm spatial spectrum expression;S4, based on eigenvalue decomposition obtains multiple eigenvalues and corresponding multiple eigenvectors;S5, based on eigenvalue, eigenvector is sorted, and constitutes to be eliminated matrix;S6, reconstruction subspace matrix is constructed, and S6 is repeated to carry out iterative calculation, and reconstruction subspace matrix is obtained Reconstruction subspace matrix set is formed;S7, simultaneously reconstruction subspace matrix set and spatial spectrum expression, obtain new spatial spectrum function set;S8, to new spatial spectrum function set is jointly estimated, and signal angle of arrival is obtained.Compared with prior art, the application has the advantages of high estimation accuracy.