55 results about "Matching pursuit algorithms" patented technology

The invention relates to a multi-dimensional transmission data compression sensing method and system for urban emergency rescue, and the method comprises the steps: obtaining multi-modal data, carrying out the normalization processing and sparse representation, and obtaining the sparse coefficient of each modal; constructing a block diagonal random matrix for each modal sparse coefficient to perform independent compression sampling, generating a low-dimensional observation value before coding, and performing arithmetic coding and low-density parity check code error correction coding to generate an anti-interference code stream; and performing low-density parity check code decoding and arithmetic decoding on the anti-interference code stream to obtain a decoded low-dimensional observation value, performing iteration by using an orthogonal matching pursuit algorithm, recovering sparse coefficients of each mode, performing reconstruction to obtain text, image and audio features, and projecting the text, image and audio features to a shared semantic space to obtain a shared semantic space. And calculating a joint semantic weight through a cross-modal attention mechanism, and generating a multi-modal fusion result with consistent semantics. The method disclosed by the invention provides an efficient, robust and semantic collaborative end-to-end solution for urban emergency rescue.

The invention discloses a multipath effect suppression method for indoor visible light positioning, relates to the technical field of indoor visible light communication and positioning, and aims to solve the problems in the prior art, the method comprises the following steps: constructing a fractional order chaotic signal generator and starting to generate a chaotic sequence signal; the chaotic sequence signals are modulated and loaded to an LED array and a driving circuit thereof, and optical signals carrying fractional order chaotic characteristics are generated and transmitted; after being propagated indoors, the optical signal is acquired by a photoelectric detector, is preprocessed and then is transmitted to a field FPGA (Field Programmable Gate Array); in the FPGA, based on a compressed sensing theory, modeling the received signal and constructing an adaptive sensing matrix to obtain a compressed observation signal; iteratively recovering sparse channel response from the compressed observation signal by adopting a path focusing guide orthogonal matching pursuit algorithm; and the FPGA extracts main path information from the reconstructed sparse channel response, and calculates a final positioning coordinate in combination with an arrival time positioning model. And in indoor visible light positioning, the method has a good capability of resisting a multipath effect.

The invention relates to the technical field of underwater acoustic communication, in particular to a multi-channel underwater acoustic OFDM (Orthogonal Frequency Division Multiplexing) communication method and system based on joint optimization sparse channel estimation. Selecting sparse signals and designing a measurement matrix; a step of performing dimension reduction on the original signal by using a compressed sensing technology; a step of performing sparse recovery on the observation signal and the measurement matrix by using an orthogonal matching pursuit algorithm, including a step of performing sparsity detection by using global approximate message passing GAMP; based on a high-probability support set output by the GAMP, accurate reconstruction is carried out through a local orthogonal matching pursuit LOMP algorithm, sparse optimization is carried out on signals, and multi-channel underwater acoustic OFDM communication based on joint optimization sparse channel estimation is achieved. According to the method, the traditional channel estimation process is simplified by utilizing the sparse characteristic of the channel, and the performance and efficiency of an underwater acoustic communication system are greatly improved.

The invention relates to the technical field of electrical variable measurement, in particular to a self-adaptive power quality monitoring method for a power cabinet, which comprises the following steps of: starting a low-frequency acquisition unit arranged at a monitoring node of the power cabinet, and acquiring a low-frequency coarse scanning voltage signal of the power cabinet in a current monitoring period at a preset low-frequency sampling rate; according to the method, compressed observation vectors are transmitted, sparse basis inversion calculation is executed by using an orthogonal matching pursuit algorithm, high-precision power quality time domain waveform data are reconstructed, rich high-frequency transient details are restored under the condition of low-data-volume transmission, distortion feature analysis is carried out based on reconstructed waveforms, disturbance types are identified, and a monitoring analysis report is generated and uploaded. The real-time dynamic adjustment of the sampling scale and the matrix structure along with the signal fluctuation state is realized, the optimal balance of the data compression ratio and the signal reconstruction quality is achieved, the contradiction between the storage and transmission bandwidth limitation of massive high-frequency data in a complex power grid environment is solved, and the sensitivity for transient abnormality is improved.

The invention relates to the technical field of wireless communication, and discloses a robust semantic communication method, device and equipment based on sparse vector coding, and the method comprises the steps: extracting continuous semantic features of original data through a semantic encoder model; discretizing the continuous semantic features into index bits through a vector quantization method by using a learnable feature dictionary; the index bit is mapped into a sparse vector by adopting sparse vector coding, and the sparse vector is sent after being expanded by a codebook; and a receiving end identifies the index through a multipath matching pursuit algorithm and reconstructs the original data. According to the method, a semantic feature extraction model, a vector quantization technology and sparse vector coding are fused, and gradient approximation, joint loss function optimization and a transmission parameter dynamic adjustment mechanism are designed, so that the problems of poor digital transmission compatibility and weak channel adaptability of traditional semantic communication are effectively solved; and the semantic transmission reliability and the system resource utilization efficiency under the dynamic channel condition are improved.

The invention discloses a compressed sensing coherent signal DOA estimation method based on Toeplitz reconstruction, and the method comprises the steps: carrying out the forward and reverse smoothing operation of a covariance matrix of a received signal, obtaining a new covariance matrix, carrying out the Toeplitz reconstruction of the covariance matrix, enabling the rank of the covariance matrix of the received signal to be recovered to the number of information sources, not to be affected by the signal correlation, and carrying out the estimation of the DOA of the compressed sensing coherent signal. And the azimuth estimation performance is improved. The method comprises the following steps of: firstly, reconstructing a covariance matrix by using Toeplitz, then performing eigenvalue decomposition by using the covariance matrix after Toeplitz reconstruction, and obtaining a DOA (Direction of Arrival) estimated value of a signal in combination with an orthogonal matching pursuit (OMP) algorithm. Simulation results show that compared with a traditional DOA estimation algorithm, the coherent signal DOA estimation method can accurately estimate the coherent signal DOA, has better estimation precision and resolution capability, and does not cause reduction of spatial resolution.

The invention relates to the technical field of radar target characteristic analysis, in particular to a radar near-field target polarization characteristic analysis method based on a vector Green function. Firstly, a target three-dimensional scattering point model is constructed, a coordinate-based scattering coefficient is defined, a full-polarization observation mode is designed, a near-field measurement matrix is calculated based on a vector Green function, and an observation model is constructed. Secondly, designing a fusion far-field constraint orthogonal matching pursuit (OMP) algorithm, realizing high-precision inversion of scattering coefficients, calculating a target far-field single-station RCS (HH / HV / VV mode) through dynamic polarization-based projection and phase compensation, and establishing a near-far field scattering characteristic mapping model; according to the method, near-field three-dimensional imaging and far-field polarization characteristic analysis are fused, the problem of insufficient cross polarization component recovery in a traditional method is solved, and the method has the advantages of high-resolution imaging, multi-physical field collaborative optimization, adaptive noise suppression and the like, and can be applied to the fields of stealth target recognition, radar cross section testing, unmanned aerial vehicle navigation and the like.

The invention discloses a multi-parameter cooperative monitoring method for sulfur hexafluoride insulation equipment, and relates to the technical field of equipment monitoring, original parameters are collected, and the original parameters comprise a sulfur hexafluoride concentration value, a sulfur hexafluoride density value and an original partial discharge signal; performing discharge preprocessing on the original partial discharge signal to obtain a partial discharge signal; constructing a point spread function matrix, and performing sparse reconstruction on the partial discharge signals by combining an orthogonal matching pursuit algorithm to obtain coordinates and discharge quantity of a partial discharge source; based on the sulfur hexafluoride concentration value, the sulfur hexafluoride density value, the coordinates of the partial discharge source and the discharge quantity, threshold judgment is carried out, and a grading early warning result is obtained. According to the invention, the early discovery capability and fault positioning precision of the insulation defect of the large sulfur hexafluoride insulation equipment are obviously improved, and reliable technical support is provided for equipment state evaluation and intelligent operation and maintenance.

The application discloses a radar high-resolution imaging method based on compressed sensing. The application comprises the following steps: step 1, an imaging model is established according to the joint block sparse characteristics of a random sequence stepped echo signal; step 2, a criterion of a block sparse signal space matching pursuit (BSSMP) algorithm is determined; step 3, the BSSMP algorithm is constructed according to the criterion; step 4, a sensing matrix Θ is constructed, so that the column full rank is satisfied, and a column set T∪T k is represented in the sensing matrix Θ; step 5, a condition that all the indexes selected by the BSSMP in the k+1th iteration are correct is established under the premise that at least K-r correct support elements are selected in the first k iterations of the BSSMP algorithm; and step 6, a condition and a required number of times for reconstructing X by the BSSMP algorithm are given. The application can effectively recover a signal, the established model fully considers the structural characteristics of the block sparse and joint sparse of the sequence echo signal, realizes joint processing of multidimensional signals, improves the imaging performance, and has high application value.

The embodiments of the present application relate to the field of electromagnetic space signal processing technology, and in particular to a method for intelligently estimating the number of signal sources of time-frequency overlapping multi-signals under non-Gaussian interference, comprising: sparsely decomposing the acquired time-frequency overlapping signal into multiple matching signals through a matching pursuit algorithm, and using a nonlinear transformer to reduce the influence of alpha stable noise on the time-frequency overlapping signal, using multiple matching signals and the original signal to form a virtual multi-channel signal, and constructing a generalized correlation matrix; performing data preprocessing operations including eigenvalue decomposition and similarity transformation on the generalized correlation matrix to obtain a processed generalized correlation matrix; inputting the processed generalized correlation matrix into an improved U-Net network to obtain a segmentation result output by the improved U-Net network, and realizing intelligent estimation of the number of signal sources of time-frequency overlapping multi-signals under non-Gaussian interference based on the segmentation result. The method has high estimation accuracy, and the estimation performance is not affected by the modulation mode and the spectrum aliasing degree, and has high robustness.

The present invention provides an improved method and system for denoising high-frequency ultrasound based on a multipath matching pursuit algorithm. The method includes: acquiring a high-frequency ultrasound detection signal of a to-be-tested sample; constructing a discrete overcomplete dictionary according to the high-frequency ultrasound detection signal, and training the discrete overcomplete dictionary; reconstructing the high-frequency ultrasound detection signal by using a trained dictionary and using a multipath matching pursuit algorithm, and obtaining a global optimal atom; performing interpolation on the global optimal atom, and constructing a consecutive atomic library; and reconstructing the high-frequency ultrasound detection signal in the consecutive atomic library according to a parameter of the global optimal atom, to complete signal denoising. In the present invention, a signal-to-noise ratio and detection precision of a high-frequency ultrasound signal are improved, so that a reflected signal and a position of a microdefect inside a sample can be observed more effectively.

The present invention proposes a method for estimating angles when the antenna spacing exceeds half a wavelength, which is used for angle measurement when the antenna spacing exceeds half a wavelength. The method comprises the following steps: 1) constructing an OFDM-based angle measurement system; 2) initializing a transmitter to send an OFDM signal to a receiver; 3) constructing a signal reception model at the receiving end; 4) introducing a reference angle to iteratively project the signal; 5) using the proposed power-based matching pursuit algorithm to perform parameter estimation; and 6) determining the angle of the direct path. The present invention effectively solves the problem that the angle cannot be accurately measured when the antenna spacing exceeds half a wavelength, and improves the angle measurement accuracy, and is suitable for scenarios such as personnel positioning in indoor environments.

This invention discloses a non-cooperative multi-user localization method in near-field scattering environments, comprising two stages: signal source acquisition and user identification. When the number of users and the transmitted pilot signals are unknown, the method leverages the system's inherent characteristics and machine learning algorithms to achieve multi-user localization in complex scattering environments. In the first stage, utilizing the spatial orthogonality between signals from different locations in the near field, a synchronous orthogonal matching pursuit algorithm based on a deep convolutional neural network (CNN) is used, with a stop iteration indication provided, to acquire the positions of all users and scattering points. In the second stage, a signal energy-assisted k-means clustering algorithm is used, simultaneously incorporating the distance between data points and signal energy information into the cluster center update criterion to accurately identify users. This invention effectively solves the problem of non-cooperative multi-user localization in near-field scattering environments.

The invention provides an improved channel estimation method based on combination of an RIP criterion and an SOMP algorithm, relates to the technical field of channel estimation of an RIS auxiliary system, is suitable for a channel estimation algorithm of an RIS auxiliary millimeter wave large-scale MIMO system, and aims to solve the problems that a traditional SOMP algorithm lacks sparseness priori information and an atom selection strategy is rigid. According to the method, a sparseness estimation method based on the RIP criterion is innovatively designed when atom number selection of initial iteration is set in the SOMP algorithm, a result serves as the initial atom selection number, and the atom number in the subsequent iteration process is dynamically adjusted by using the residual energy ratio of two iterations of the SOMP algorithm; experiments show that compared with traditional matching pursuit algorithms such as an OMP algorithm, an SOMP algorithm and an StOMP algorithm, the method has obvious advantages, and support is provided for the SOMP algorithm to be more suitable for practical application.

The invention particularly relates to a sparse array DOA (direction of arrival) estimation method based on an optimized array manifold dictionary, and the method comprises the steps: firstly carrying out the layout optimization of an initial uniform area array through combining a genetic algorithm, constructing an eight-element sparse array structure, and maintaining a directional diagram which is close to a full array while greatly reducing the number of array elements; performing energy screening on multichannel observation signals acquired by the sparse array, and extracting a dominant frequency band to improve an effective signal-to-noise ratio; and an optimized array manifold dictionary is constructed based on a phase compensation mechanism and an amplitude weighting mechanism, and the internal correlation of the dictionary is reduced by minimizing off-diagonal elements of a dictionary Gramer matrix. And on the basis, sparse reconstruction is carried out on the optimized dictionary by using an orthogonal matching pursuit algorithm, so that a direction-of-arrival estimation result of the sound source signal is obtained. According to the scheme, inherent direction finding fuzziness and grating lobes of the sparse array are effectively suppressed, the direction estimation precision is improved, and high reliability and high robustness are still kept under the condition that the number of array elements is remarkably reduced.

The invention discloses a circular antenna array-based maximum-scale MIMO (Multiple Input Multiple Output) channel estimation method and a circular antenna array-based maximum-scale MIMO channel estimation system, an uplink communication link of a time division duplex-based communication system is considered, users are randomly distributed in a three-dimensional space, and a hybrid precoding architecture is adopted. Different from a previous method which only considers a polar domain of a user plane angle and a distance to represent a near-field channel, the method also considers an elevation angle of a user, uses a ball domain codebook to represent the near-field channel, analyzes how to sample the elevation angle, an azimuth angle and the distance in a ball domain to reduce column coherence of a transformation matrix, and improves the accuracy of the near-field channel. In addition, a synchronous orthogonal matching pursuit algorithm based on a sphere domain codebook is provided, and finally, the effectiveness of the algorithm is verified under the condition of users distributed in any space.

The present invention relates to a human key point detection method, device and medium based on orthogonal matching pursuit. The method includes: inputting a to-be-detected image containing a person into a trained target detector to obtain a cropped image of a single person; setting the annotation coordinates of human key points; scaling the cropped image to a set size and inputting it into a trained feature encoding backbone network to obtain a sensing matrix; and performing sparse recovery on the annotation coordinates of human key points by using the orthogonal matching pursuit algorithm according to the sensing matrix to obtain the positions of human key points of the person in the to-be-detected image. Compared with the prior art, the present invention reduces the computational burden while ensuring relatively high computational accuracy and has high detection efficiency.

The invention discloses a millimeter wave large-scale MIMO adaptive-fast convergence sparse recovery algorithm, which is an adaptive multi-dimensional joint correlation synchronous orthogonal matching pursuit algorithm. According to the algorithm, a plurality of atoms are selected in parallel in single iteration, so that the convergence process is remarkably accelerated. Moreover, an adaptive termination criterion can be realized without prior information such as channel sparseness and noise power. Simulation results show that the convergence speed of the algorithm is increased by about 50% compared with that of a traditional SOMP algorithm, and a normalized mean square error equivalent to that of the traditional SOMP algorithm can be obtained under the condition of lacking channel sparseness and noise power prior information. Especially in an environment with a signal-to-noise ratio of 8-23dB, even if channel noise power information does not exist, sparseness estimation errors can be effectively reduced, and the method is superior to a traditional algorithm. Therefore, the algorithm provided by the invention has strong self-adaptability and can effectively cope with the dynamic change of the channel.

The invention provides a fast estimation method for ultrasonic thickness measurement sound time of a large-curvature cable insulation layer, and belongs to the technical field of ultrasonic measurement of the cable insulation layer, the method combines short-time Fourier transform (STFT), an improved grey wolf algorithm (IGWO) and a matching pursuit algorithm (MP), and fits ultrasonic echo signals based on the matching pursuit algorithm on the basis of a Gabor function model. The algorithm firstly utilizes short-time Fourier transform to optimize an echo parameter search range, then utilizes an improved grey wolf algorithm to replace traversal calculation in matching pursuit, and improves echo parameter search efficiency and estimation precision. The method can effectively solve the problems of insufficient precision and low efficiency in ultrasonic traditional acoustic time estimation and thickness estimation, and has better estimation precision and stability in an acoustic system containing frequency dispersion and phase shift, so that the method meets the application requirements of real-time online thickness measurement.

The present invention discloses a method for estimating the parameters of a UAV frequency hopping signal based on GSACMP, which mainly solves the problem in the prior art that the amount of data required to estimate the key parameters of a non-cooperative UAV frequency hopping signal is large and is affected by non-Gaussian noise. Its implementation scheme includes: establishing the intercepted UAV frequency hopping signal under the influence of non-Gaussian noise as a segmented form model; based on the generalized sparsity adaptive correlation entropy matching pursuit algorithm, weighting the selection process of atoms in the iterative process to obtain sparse vectors corresponding to all segmented signals; obtaining the center frequency of each segmented signal based on the sparse vector and the signal characteristics of the compressed domain; and estimating the frequency hopping time of the non-cooperative UAV frequency hopping signal by differentiating the center frequency of each segmented signal and using the sliding window method. The present invention optimizes the sparse vector estimation process, improves the estimation performance of the UAV frequency hopping signal parameters under non-Gaussian noise, and can be used in a non-cooperative UAV frequency hopping communication system or a corresponding software radio communication system.

This invention proposes a joint optimization and sparse signal processing method based on an orthogonal frequency division multiplexing (OFDM) system, belonging to the field of non-destructive measurement. It solves the problems of balancing high resolution and anti-interference capability, and the trade-off between real-time performance and measurement accuracy. The method includes: receiving OFDM signals and performing time-frequency decomposition; decomposing the signal into multiple orthogonal subcarriers using a fast Fourier transform; dynamically disabling interfered subcarriers based on their signal-to-noise ratio (SNR); performing Bayesian phase compensation on the retained subcarriers; iteratively optimizing the subcarrier switching states and phase compensation parameters, dynamically adjusting the SNR threshold to separate signal and noise; reconstructing the sparse signal using compressed sensing technology; and improving ranging and imaging resolution using an orthogonal matched pursuit algorithm. It is mainly used in fields such as underground target detection and dynamic radar sensing networks.

The invention relates to a mine micro-seismic signal noise reduction method based on sparse decomposition. The method starts with priori knowledge analysis of mine micro-seismic signal characteristic waveforms, and comprises the following steps: firstly, selecting Laplace wavelet parameters which are most matched with the micro-seismic signal characteristic waveforms through a correlation filtering method, and constructing a Laplace wavelet parameter dictionary according to the Laplace wavelet parameters; secondly, sparsely reconstructing a characteristic waveform of the micro-seismic signal by combining an orthogonal matching pursuit (OMP) algorithm; in consideration of large calculation amount and long time of a correlation filtering method, a whale optimization algorithm (WOA) is used to carry out rapid global parameter optimization. The analysis result of the simulated micro-seismic signal and the actually measured signal shows that the characteristic waveform of the micro-seismic signal can be effectively reconstructed by the provided method, the noise reduction of the micro-seismic signal is realized, and the method has certain anti-interference capability on noise; compared with a common ensemble empirical mode decomposition (EEMD) method, the sparse noise reduction method based on the self-adaptive Laplace wavelet dictionary provided by the invention has certain superiority.

The present application relates to the technical field of signal processing, and provides a kind of catenary non-stationary vibration signal sparse decomposition method, the method comprises: collecting impact vibration response signal, and carrying out denoising pretreatment;Establish parametric impact response atom model, and construct over-complete parametric atom dictionary;Using matching pursuit algorithm and combining sparse decomposition algorithm, obtain a series of linear combination of sparse atom components;According to the best matching atom set and its corresponding parameter vector obtained in iteration process, the incident wave component set and the reflected wave component set are obtained, and the decoupled incident wave signal and reflected wave signal are reconstructed.The present application realizes the accurate separation and reconstruction of rigid catenary mixed vibration waveform by constructing over-complete parametric atom library containing frequency modulation factor, combined with matching pursuit algorithm.

The invention discloses an ultrasonic image reconstruction method and device based on OMP, equipment and a medium. The method comprises the following steps: acquiring an initial time-space domain radio-frequency signal of a target object acquired by using an ultrasonic transducer array, and decomposing to obtain an initial space domain radio-frequency signal and an initial time domain radio-frequency signal; respectively processing the initial spatial domain radio frequency signal and the initial time domain radio frequency signal by adopting a pseudo-random sampling mask to obtain a target spatial domain radio frequency signal and a target time domain radio frequency signal; performing sparse reconstruction on the target spatial domain radio frequency signal and the target time domain radio frequency signal based on an orthogonal matching pursuit algorithm to obtain a spatial reconstruction result and a time reconstruction result; and determining an ultrasonic image reconstruction result of the target object according to a fusion result of the spatial reconstruction result and the time reconstruction result. According to the scheme, dynamic ultrasonic time super-resolution enhancement is realized by utilizing an orthogonal matching pursuit algorithm and combining with spatio-temporal information, and low-cost frame rate enhancement is realized while the ultrasonic image quality is ensured.

The present invention belongs to the technical field of moving sound source separation, and specifically relates to a Circle-Doppler-let transform method and a sound source separation method, including the following steps: The Circle-Doppler-let transform method includes: S1: Construct a circular motion model, set relevant parameters, and partition the parameters to obtain a parameter set; S2: Perform time-frequency adjustment based on the parameter set to generate a Circle-Doppler-let function library Function1; S3: Collect a sound signal x(t), use the matching pursuit algorithm and Function1 to perform sparse representation on the sound signal x(t), perform an inner product operation to find the basis function with the maximum inner product, and obtain a basis function library Function2 after iteration; The sound source separation method includes screening the basis functions in the basis function library Function2 that meet the requirements of the screening interval and storing them to obtain Function3, and then linearly combining the basis functions in Function3 to obtain a reconstructed signal x1(t). The present invention proposes a Circle-Doppler-let transform, which can be applied to the sparse representation and sound source separation of circular motion sound sources. Compared with the prior art, it has stronger in-band noise separation ability; only requires a single microphone, and the use cost is low.

Computing systems and computer-implemented methods for removing brain stimulation artifacts in neural signals are disclosed. The method makes use of a matching pursuit algorithm to accurately extract the stimulation artifact. The disclosed method removes the stimulation artifact associated with individual stimulation pulses without needing additional information from previous stimulation pulses or other electrodes. The disclosed method is compatible for use with various stimulation frequencies, does not need any filtering, and can recover neural signals almost immediately after stimulation. The disclosed method is compatible for use in has great potential in closed-loop systems used in various neurological diagnostic and therapeutic procedures.

The invention provides a gas-bearing property semi-quantitative characterization method and a gas-bearing property semi-quantitative characterization system for solid-flow-gas two-stage collaborative decoupling of a deep coal reservoir. The method comprises the following steps: firstly, acquiring frequency-expanded high-resolution seismic data through time-depth calibration and nonlinear superposition data processing by using logging and post-stack seismic data; using a matching pursuit algorithm to obtain ABV, LFG and other viscoelastic parameters; secondly, on the basis of pre-stack sub-angle gather conversion, P-domain Fatti elastic parameters are calculated, and solid-fluid decoupling gas-bearing data are obtained through fitting rotation; and then, under the constraint of a zoning phase belt, establishing a gas-liquid decoupling GID indication parameter and a gas-bearing probability body, and finally, dividing a rich gas region and a differential gas region through a GID threshold value to realize semi-quantitative evaluation of the gas-bearing property.

The invention discloses a target local RCS (radar cross section) measurement method based on a matching pursuit algorithm, which comprises the following steps that: a probe scans and acquires target back scattering echo information through a'transmitting and receiving 'rotary table, the matching pursuit algorithm is carried out on echo electric field data, the scattering point distribution condition of a target is acquired, and the high-precision RCS value of the target is acquired through inversion. In a turntable scanning system, initialization parameters are included; obtaining target scattering echo data; extracting an equivalent scattering center of the target; selecting local scattering points of the interested part; performing inversion to obtain a far-field scattered field of the target; measuring the calibration body by using the same method; and calibrating and calculating the real RCS of the target. Measurement is carried out at the scattering far field of the target, distribution of two-dimensional space scattering points of the target is obtained based on the matching pursuit algorithm, the RCS value of a local device of the target at a certain angle along with frequency change can be calculated, and the electromagnetic scattering characteristic of the target can be reflected more quickly and accurately.

This invention discloses a blind estimation method for frequency-hopping signal parameters based on atomic features. The method involves: first, windowing, compressed sampling, and atomic feature extraction of the frequency-hopping signal; then, roughly estimating the hopping time based on the distribution of atomic features within different windows; next, precisely estimating the hopping time, hopping period, and hopping speed using a block-diagonalized Fourier orthogonal matrix; and finally, estimating the frequency of each hop signal using an orthogonal matching pursuit algorithm, and obtaining the frequency-hopping pattern of the entire sample by concatenating each hop signal. This invention can adjust the search step size and compression ratio to change the computational complexity and estimation accuracy, adapting to different signal-to-noise ratio (SNR) environments. In high SNR environments, increasing the search step size and compression ratio effectively reduces computational complexity, while in low SNR environments, decreasing the search step size and compression ratio effectively improves estimation accuracy.

The invention discloses a non-cooperative multi-user positioning method in a near-field scattering environment, which comprises two stages of signal source acquisition and user identification, and realizes multi-user positioning in a complex scattering environment by means of mining of own characteristics of a system and a machine learning algorithm when the number of users is unknown and pilot signals are sent, and in the first stage, the number of users is not known, and the number of users is not known. The spatial orthogonality between signals from different positions in a near field is utilized, a synchronous orthogonal matching pursuit algorithm based on a deep convolutional neural network CNN is used, iteration stopping indication is provided, and the positions of all users and scattering points are obtained; and in the second stage, a k-means clustering algorithm assisted by signal energy is used, and the distance between data points and signal energy information are simultaneously incorporated into an updating criterion of a clustering center so as to accurately identify the user. According to the invention, the problem of non-cooperative multi-user positioning in a near-field scattering environment is effectively solved.