308 results about "Compressed sampling" patented technology

The invention discloses a multi-user collaborative spectrum sensing method based on a single bit compression sensing technology. Based on the current situation of a low actual occupancy rate of wireless spectrum resources, a plurality of control units (CUs) use a single bit complex signal compression sensing technology respectively, after compressed sampling is performed to broadband analog base band signals sent by a master user, the obtained compressed sampling results are transmitted to a financial centre (FC) respectively, and the FC performs collaboration reconstruction to corresponding frequency domain signals of the broadband analog base band signals to obtain occupancy conditions of spectrum resources. Compared with traditional sensing methods, the simplicity of actual quantitative operation is fully considered, the bit speed rate needed by compressed spectrum sensing is further reduced, the computation complexity of each sensing user is reduced, and multi-user collaborative spectrum sensing and single user independent sensing are compared, so that good sensing effects can be obtained.

The invention discloses a power system harmonious wave compressed signal reconstruction and detection method based on compressed sensing. The power system harmonious wave compressed signal reconstruction and detection method based on compressed sensing comprises the steps that harmonious wave original signals of a power system and a measurement matrix with a binary sparse random measurement matrix serving as a power system harmonious wave data compressed sample are sent to a frequency mixer for compressed sampling in a simulation domain, then A/D conversion is carried out on the simulation signals, and a compressed sampling value is obtained; it is determined that a compressed sensing sparse base is a discrete Fourier transform base; initialization of fundamental wave filtering is carried out; fundamental wave filtering is carried out; the frequency, amplitude and phase of a fundamental component are detected; the fundamental wave constituent in the compressed sampling value is filtered away; parameter initialization is carried out on a spectrum projection gradient method; a sparse vector estimation value of a harmonious wave component is reconstructed through the spectrum projection gradient method; the frequency, amplitude and phase of the harmonious wave component are detected; reconstruction of the harmonious wave original signals of the power system is finished. The power system harmonious wave compressed signal reconstruction and detection method based on compressed sensing overcomes the defect that all existing recovery algorithms do not take the influence of the fundamental wave component in the harmonious wave signals on signal reconstruction into account, so that the recovery effect is not ideal.

The invention discloses a time-frequency diagram processing method and system for mechanical equipment monitoring vibration signals. The time-frequency diagram processing method comprises the steps that firstly, linear or bilinear time-frequency transformation is carried out on collected mechanical equipment monitoring vibration signals to obtain a time-frequency diagram TFRs, and average threshold processing is carried out on the time-frequency diagram TFRs to obtain a sparse time-frequency diagram STFRs; secondly, time-frequency compression sampling is carried out on the STFRs in a random sampling mode to obtain a matrix M<STFR > with dimensionality being k times the dimensionality of an original matrix; thirdly, the parallel class FISTA proximal decomposition method is used for reconstructing an STFRs* through iterative computation. The time-frequency diagram processing system comprises a monitoring front-end machine and a server connected with the monitoring front-end machine. The monitoring front-end machine comprises an A/D data collection module, a time-frequency transformation module, an average thresholding module and a time-frequency compression sampling module and is connected with a vibration sensor. The server comprises an on-line data storage module and a reconstruction algorithm module. According to the time-frequency diagram processing method and system, the dimensionality of the reconstructed time-frequency diagram is greatly reduced, data storage and transmission are facilitated, and time-frequency analysis technology can be widely applied for mechanical equipment fault real-time quantitative analysis and diagnosis.

The invention discloses a multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method, which relates to the technical field of information and communication. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method is provided for solving the problem of recovering an original multiband signal from multiple observed value vectors with unknown sparsity after continuous-limited module conversion through sampling by a modulated broadband converter under an Xampling framework. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method comprises the steps of: conducting self-adaptive estimation on sparsity of a signal; updating the sparsity with a given step length factor through repeated iteration so that the sparsity gradually approaches the actual sparsity of the signal; correcting a support set through a backtracking thought and a minimum mean square criterion; stopping iteration until an residual error is less than a set threshold value; and finally reconstructing an original multiband signal through pseudo inverse operation by utilizing the obtained complete support set. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method can achieve the analog reconstruction of the multiband signal based on compressed sensing.

The invention relates to a coefficient-random-permutation-based compressive sensing method for image coding, which comprises the following steps of: 1) blocking an original image, and performing block-based sparse conversion; 2), reorganizing block sparse conversion coefficients obtained by the conversion of the step 1 according to positions to obtain coefficient vectors corresponding to different positions; 3), performing an intra-group coefficient random permutation operation on each coefficient vector obtained by the step 2 respectively; 4), sequentially extracting a coefficient from each coefficient vector permutated by the step 3 to form the coefficient vectors of which sizes are the same as those of blocks of the original image and which correspond to the positions; 5), performing compressed sampling code expression on each coefficient vector produced by the step 4); and 6), reconstructing the original image by an inverse process of the process. By the method, the measurement rate of image compressed sensing can be remarkably reduced, and the quality of the reconstructed image can be improved.

The invention relates to a random sampling analog circuit compressed sensing measurement and signal reconstruction method, which belongs to the field of electronic system test and fault diagnosis. Aiming at a fault signal having a sparsity distribution characteristic per se or in an orthogonal space in an output response of an analog circuit, a test node is selected according to a circuit topology structure, circuit output responses are randomly sampled under a distributed sensor test network, response signals are expressed in a sparse way on a transform domain by utilizing discrete orthonormal basis, compressed sensing measurement of the sparse signals is completed under observability matrix projection, and when the recovery rate of signal reconstruction by randomly compressed sampling points reaches more than 80 percent, the compressed measurement values of the circuit output responses are effective, can form a characteristic set and can be used for analog circuit fault diagnosis. The method solves the problems that the traditional analog signal sampling occupies a large number of hardware resources, large signal reconstruction calculated amount and the like; and the random sampling compressed sensing measurement method is utilized to improve the efficiency of electronic system testing.

The invention provides a variable step size regularized adaptive compressed sampling matching pursuit method, specifically relates to a reconstruction method based on compressed sensing, and a solution aims at the prior two problems in the regularized orthogonal matching pursuit (ROMP) method. One of the problems is that the sparseness K is difficult to be obtained in reality, then the method provides that the adaptive idea of the sparse adaptive matching pursuit (SAMP) is applied in ROMP; the other problem is that once an atom which is selected in a support set will not able be deleted, then the method provides that the backtrack idea of the compressed sampling matching pursuit (CoSaMP) is applied in ROMP. In addition, the method adopts a stage orthogonal matching pursuit (StOMP) idea to set the threshold condition so as to stop the iteration, and the problem including lack of accuracy and over estimated caused by the fixed step size of the introduced SAMP is improved. The method provided by the invention greatly improves the reconstruction performance, estimated accuracy and application range of the reconstruction method based on the compressed sampling.

The invention discloses a channel estimation method for a large-scale MIMO system based on an improved distributed compressed sensing algorithm. Pilot information is transmitted through NT antennas ata single cell base station and received at NR single antenna user terminals. The method includes the steps of S1, calculating a measurement vector y of the pilot information received at each user receiving terminal, establishing a compressed sensing mathematical model for the pilot information transmission process according to the sparsity consistency of channels of the large-scale MIMO system, and constructing a sensing matrix [Phi]; S2, obtaining a block structure sensing matrix [Psi] through block structure transformation, and reconstructing a block sparse signal g through a reconstructionalgorithm; and S3, reconstructing a sparse signal h by using a block structure adaptive compressed sampling matching pursuit algorithm. According to the invention, the time domain sparsity consistency of the channels of the large-scale MIMO system is utilized, the channel impulse response is reconstructed by using the block structure adaptive compressed sampling matching pursuit algorithm, the estimation can be performed when the sparsity is unknown, and the use of the pilot frequency can be reduced.

The invention relates to a compressively sampling and receiving system for impulse ultra-wideband signals, which is characterized by comprising a multichannel sampling unit for distributing a plurality of channels for the impulse ultra-wideband signals to perform parallel sampling, a measuring waveform generator for respectively sending measuring waveforms to channels of the multichannel sampling unit, and a digital rear-end receiving processing unit for receiving measured values sampled by the multichannel sampling unit. The impulse ultra-wideband signals are continuous multiframe signals; the measuring waveforms generated by the measuring waveform generator are irrelevant to impulse ultra-wideband receiving signals which enter the multichannel sampling unit; the channels respectively linearly project the impulse ultra-wideband receiving signals according to the measuring waveforms generated by the measuring waveform generator; and the digital rear-end receiving processing component processes the measuring values sampled by the multichannel sampling unit. The compressively sampling and receiving system for the impulse ultra-wideband signals does not require high sampling speed, long delay line or exact channel estimation, and can fully exploit the potential of the high-speed communication and the high-precision distance measurement of the Infrared Radiation-Ultra Wide Bandwidth (IR-UWB).

The invention discloses an extraction method of visual salient regions based on multiscale relative entropy. The extraction method of the visual salient regions based on the multiscale relative entropy comprises extracting different global color feature characteristic patterns and direction characteristic patterns from input images, carrying out gaussian pyramid decomposition and multiscale normalization on the global color feature characteristic patterns and the direction characteristic patterns, respectively computing color partial relative entropy and direction partial relative entropy of respective feature space, respectively normalizing results of the different feature space, carrying out linearity superposition of the results of the different feature space, linearly adding color saliency maps and direction saliency maps together, carrying out two-dimensional gaussian smoothing, and therefore extracting visual salient regions. Compared with a traditional method, the extraction method of the visual salient regions based on the multiscale relative entropy takes a full account of color features and direction features reflecting global significance and relative entropy reflecting partial novelty, adopts a method of multiscale analysis, and has the advantages of being effective and reliable. The extraction method of the visual salient regions based on the multiscale relative entropy is capable of being used for self-adaption compressed sampling and target detection of natural images, remote sensing images and the like, and is wide in application prospect of lowcost imaging devices.

The invention relates to the field of signal sampling and signal detection, and in particular relates to a compressed sampling signal detection method based on a modulated wideband converter which detects the signals after obtaining the sampling value of a sub-Nyquist sampling rate through compressed sampling passive radar and electronic reconnaissance signals. The method comprises the steps of setting parameter, processing the signal entering an MWC structure with M parallel channels; computing the covariance matrix of a sampling matrix; getting the characteristic value of the covariance matrix and normalizing; computing the correlation coefficient of the normalization characteristic value; and judging whether the signal exists according to the obtain correlation coefficient and a threshold. With the application of the compressed sampling signal detection method based on the modulated wideband converter into the detection of broadband signals, the sub-Nyquist sampling can be ensured, and less data is used for storing under the premise of ensuring that the target can be detected out, and thereby the storage resources of a receiver are saved.

A compressed sampling matrix decomposition-based infrared small target detection method in the technical field of image processing comprises the following steps: 1, inputting an infrared small target image, solving the information entropy of the image and performing rank estimation according to the information entropy of the image; 2, performing column vectorization on the image, and performing compressed sampling by using a Walsh Hadamard matrix to obtain a measured value; 3, performing image reconstruction and meanwhile restoring a background and a target; 4, performing binarization processing on the infrared small target image and detecting the infrared small target. By adopting the method, detecting the infrared small target in a compression domain is realized, data processing capacity and storage capacity are reduced by compressed sampling, the separation of the target from the background is realized after reconstruction, both the target and the background are obtained, the obtained target part has very high gain of signal to noise ratio, the background and noise are effectively restrained, and the infrared small target can be detected only by simple binarization processing in subsequent processing.

The invention discloses a compressed sensing observation matrix constructing method based on a sparse Hadamard matrix, which comprises the following steps of: A, establishing a first sparse Hadamard matrix set, wherein each first sparse Hadamard matrix in the first sparse Hadamard matrix set is shown in the specficiation; B, obtaining a second sparse Hadamard matrix set according to the first sparse Hadamard matrix set, wherein each second sparse Hadamard matrix in a second sparse Hadamard matrix set is shown in the specification; C, decomposing the sparse Hadamard matrixes to obtain matrixes with forms of the A and the B steps; D merging the matrixes with the forms of the A and the B steps to obtain the sparse Hadamard matrix; and E, extracting preset lines randomly to obtain the observation matrix. The observation matrix obtained through the compressed sensing observation matrix constructing method disclosed by the invention can reduce computation complexity of compressed sensing in compressed sampling and reconfiguration recovery of data. In addition, the observation matrix is more convenient for hardware realization.

The invention provides an analog signal compressed sampling method. The method includes the steps of providing a compression measurement matrix on the basis of a framework, carrying out synchronous transformation on a compression sampling value sequence and the compression measurement matrix, eliminating the influence of correlation of a matrix vector equation on reconstruction performance of detected analog signals, carrying out reconstruction on the detected analog signals according to the equation, and obtaining sampling sequences of the detected analog signals. When an integrator carries out integration on demodulated signals within a sampling time period, the integrator does not carry out restoration processing. Therefore, the analog signal compressed sampling method solves the problem of signal incompletion sampling caused by charging time of the integrator, and improves the performance of an analog information transpression sampling system.

The invention discloses an ocean remote sensing image water color and water temperature monitoring method based on compression sampling. The monitoring method comprises the following steps of: obtaining original remote sensing data of ocean water color or water temperature to be detected, and carrying out compression sampling, sparse transformation and image processing on the original remote sensing data to obtain a water color or water temperature data set; sequentially conducting denoising and reconstruction processing on the water color or water temperature data set through an SOM (self-organized mapping) algorithm to obtain a processed water color or water temperature data set; conducting denoising processing on the processed water color or water temperature data set and reconstructing missing image data by utilizing the continuous interpolation property; and outputting and displaying the reconstructed missing image data. The monitoring method utilizes the compression sampling principle for reducing the amount of processed data, and utilizes the advantages of nonlinear estimation of the SOM algorithm as well as linear estimation and continuous interpolation of an improved empirical orthogonal decomposition (EOF) algorithm and the like so as to improve the efficiency of reconstructing missing images, expand the scope of the reconstructed images, and enable the precision and efficiency of ocean remote sensing water color and water temperature monitoring to be high.

The invention discloses a hyper-spectral compression perception reconstruction method based on nonlocal total variation and low-rank sparsity, and mainly solves the problems in the prior art that reconstruction accuracy is low and the effect is poor after compressed sampling of hyper-spectral data. The hyper-spectral compression perception reconstruction method comprises the steps that 1. the hyper-spectral data are inputted and vectorized; 2. the vectorized hyper-spectral data are sampled so that sampling data are obtained; 3. initial reconstruction of the sampling data is performed; 4. the initially reconstructed data are clustered; 5. the sampling data are classified according to the type of image elements so that various types of sampling data are obtained; 6. a secondary reconstruction model is constructed; and 7. The secondary reconstruction model is solved according to various types of sampling data so that the optimal data of secondary reconstruction are obtained, and the data act as the final reconstruction data. The idea of nonlocal total variation and clustering is introduced on the basis of low-rank sparse reconstruction so that the hyper-spectral compression perception reconstruction method has advantages of high reconstruction accuracy and great effect and can be used for hyper-spectral data imaging.

The invention relates to the field of initiative imaging, and provides an initiative imaging system based on compressed sampling. The initiative imaging system comprises a pulsed light signal generating device used for generating pulsed light signals, a light imaging device used for imaging the pulsed light signals generated by the pulsed light signal generating device, a light collecting device used for collecting reflected light formed by a pulsed light signal irradiation target scene processed by the light imaging device, a first light modulator used for carrying out first spectrum modulation on the reflected light collected by the light collecting device, and an image reconstructing device used for carrying out image reconstruction on the pulsed light signals modulated by the first light modulator. According to the initiative imaging system, a digital micromirror array and other mechanical structures in a traditional single-pixel imaging system are removed, and the imaging speed is greatly increased.

The invention provides a method for compensating for a low-pass filter of a compressed sampling system. The method includes the steps that a sampling value error ye of the actual low-pass filter of the compressed sampling system is obtained by inputting a known tested signal x (t), a nonideal low-pass filter FIR coefficient error e is estimated, and then a nonideal low-pass filter FIR coefficient h is obtained; an FIR coefficient hd for correcting the filter is estimated on the basis, and then the corrected nonideal low-pass filter is made to be turned into an ideal low-pass filter; meanwhile, sampling frequency of the compressed sampling system is increased, the oversampling coefficient of the compressed sampling system is R, sampling values of channels are sampled, and then the sampled sampling values are filtered through a compensating filter. It is guaranteed that the sampled and filtered sampling values are equal to sampling values obtained through the ideal low-pass filter, and therefore the problem that large errors exist in the reconstruction process of a tested signal sequence x* (n) caused by the nonideal low-pass filter in the compressed sampling system is solved.

The invention discloses a compressed-sensing-based de-artifact fusion method of a high-dynamic-range image. Compressed sampling is carried out on an inputted multi-exposure image sequence; reconstruction is carried out by using a reconstruction method to obtain multi-exposure image sequence after compressed sensing; and then an image set after compressed sensing is normalized, multi-exposure imagede-artifact fusion based on PatchMatch and a rank minimization algorithm is carried out on the image set to obtain a high-dynamic-range (HDR) image of a target. According to the invention, on the basis of latest research results of K-SVD dictionary learning, compressed sensing and de-artifact fusion, the sampling rate, the storage space and the computational complexity are reduced effectively; and a de-artifact de-blurring HDR image is obtained.

The invention provides an MWC compressed sampling wideband digital receiver PDW forming method based on an FPGA, and belongs to the field of electronic countermeasures in information and communicationengineering. Firstly, a signal is transferred to a baseband by a mixing module, a baseband compressed sampling signal is obtained by a low pass filter, and then deceleration extraction is performed to greatly reduce the data size so as to facilitate hardware implementation. The extracted signal is input to a CORDIC module to extract amplitude and a phase, sub-band frequency is measured by using aphase difference frequency measurement method, and a sub-band where the signal is located is calculated by a frequency calculation module in a multichannel signal parallel channelization mode so as to obtain absolute carrier frequency of the signal. Pulse extraction is performed by using a threshold judgment method, and a pulse width is calculated from a signal arrival time and a signal extinction time extracted from pulses. By adoption of the MWC compressed sampling wideband digital receiver PDW forming method provided by the invention, the feasibility of physical implementation of PWD dataforming of a digital receiver based on a MWC compressed sampling structure is verified, and a theoretical and hardware implementation foundation is laid for subsequent implementation of the FPGA basedon the overall system of the novel receiver.

The invention provides a method for modal analysis and structural impact monitoring based on compressed sampling and belongs to the technical field of structural health monitoring. According to the invention, a compressed sampling technology is used to collect impact load signals; then, modal analysis is conducted on compressed sampling data of the collected impact load signals by a singular value decomposition method, so that modal parameters of the structure are acquired; and finally, parameters including sizes and positions of impact loads are acquired through a signal reconstruction method based on the compressed sampling data. According to the invention, the problem about poor accuracy existing in structural modal parameter acquisition of a complicated structure based on modeling as well as the problem about overlarge data size of multiple sensors could be solved. The method has the advantages that a sensor network arranged on a to-be-monitored structure is used for impact monitoring; the structure can be monitored rapidly and accurately in real time; energy consumption is low; the size of stored and transmitted data is small; data loss is reduced during storage and transmission, etc.

The invention discloses a broadband compressed spectrum sensing method based on sub-band matching selection. In a spectrum sensing stage, a secondary user carries out compressed sampling to received analog signals through an analog information converter, thus obtaining few compressed sampling data; frequency domain sparsity signals corresponding to the signals obtained by carrying out Nyquist sampling to the analog signals are reconstructed through an iteration mode; then the secondary user judges the occupation state of each sub-band on the reconstructed frequency domain sparsity signals by an energy detection method; the method has the advantages that the single frequency point matching selection is changed into the sub-band matching selection; the sub-bands of a primary user can be determined; the iterations of the algorithms in the spectrum reconstruction can be greatly reduced; the spectrum reconstruction time is shortened; the residual errors are updated by a regularized least square method; therefore, the spectrum reconstruction accuracy and robustness can be further improved; and the broadband spectrum sensing performance is improved.

The invention provides a compressed video capture and reconstruction system based on structured sparse dictionary learning. The system comprises a structured sparse dictionary learning module, a video signal sensing module and a reconstruction processing module. The structured sparse dictionary learning module firstly acquires a training set through a sub-space clustering method, then, a dictionary is acquired through a linear sub-space learning method and minimized-block-relevant block sparse dictionary learning method, the sensing module projects video signals in an image block mode, and acquired data are finally decoded and reconstructed in the reconstruction processing module. Compressed sampling is provided, the distributed progressive structure of the video sampling process is combined, the reconstruction accuracy and efficiency are improved for the special structure of a structured sparse dictionary matrix, the sampling efficiency of the video signals is greatly improved, reconstruction gains are acquired compared with other methods under different sampling compression ratios, and meanwhile the good expandability is achieved.

The invention discloses a beam forming device based on sparse signals and a method of the device. The beam forming device comprises an array element receiving antenna, a multi-input analog adder, an analog multiplier, a pseudorandom sequence generator, an integrator and an A/D convertor, wherein weight coefficients of the multi-input analog adder are submitted to random Bernoulli distribution. After sampling, data reconstruction steps are as follows: 1), main diagonal elements are taken, and sampling data of (K1+1)th row are constructed; 2), the main diagonal elements are taken, and sampling data of (K1+2)th row are constructed; 3); sampling data of left (K2-2) rows are constructed sequentially; and the data of all the rows are summed. According to the beam forming device and the method, a compressed sampling technology is adopted, so that broadband signals can be sampled at a low sampling rate; an airspace projection measurement matrix selects a random Bernoulli array, a phase shifter and a multiplier are not required; a multi-channel structure and a sampling data reconstruction manner are used during time-domain observation projection; and more sampling data comprising a large amount of information can be acquired by the aid of fewer channels.

The invention relates to an abdominal organ dynamic contrast enhanced magnetic resonance imaging method based on compressed sensing. The method concretely includes the steps: 1), a magnetic resonance imaging pulse sequence includes a three dimensional gradient echo excitation pulse, a space coding gradient and a signal relaxation sequence, the three of which are explained respectively as follows: 1.1) setting parameters of a radio frequency excitation pulse of a three dimensional gradient echo sequence; 1.2) optimizing a choose-layer phase coding kz and an inner-layer phase coding ky respectively, that is to say, carrying out sub-sampling according to a CS theory, the frequency coding direction kx being fully sampling; and 1.3) applying spoiled gradient to an x gradient direction, a y gradient direction and a z gradient direction in terms of the signal relaxation sequence; 2) a magnetic resonance imaging system carries out compressed sampling of k-space data of all phases of DCE-MRI scanning for an abdominal organ on the basis of a CS optimized magnetic resonance imaging pulse sequence, and obtains original sampling data of a time sequence; and 3) CS reconstruction of the original sampling data is conducted, that is to say, a DICOM image of the abdominal organ is reconstructed and obtained on the basis of a non-linear algorithm with a minimized 1<1> normal form. The method can be widely applied to abdominal organ dynamic contrast enhanced magnetic resonance imaging.

The invention discloses a multi-data compression tracking algorithm-based local fault remote diagnosis method for a rotary machine, and the method comprises the steps of S1, analyzing possible faultsof equipment and corresponding fault feature information, and collecting a mechanical vibration signal and a rotating speed signal of an equipment end; S2, intercepting a section of time domain signalat the equipment end, and using a shift constant K-SVD learning method for carrying out pattern training; S3, performing real-time compressed sampling on the acquired vibration signal data accordingto a compressed sensing principle; S4, carrying out remote transmission on the path, the rotating speed working condition information and the compressed and sampled data which are obtained by the equipment end through training and learning; S5, at a receiving end, constructing a shift invariant sparse dictionary by using the pattern, and recovering fault characteristics by using the compressed data of the three channels on the same sensor through a multi-data compression tracking algorithm at the same time; and S6, determining the fault problem of the equipment according to the extracted faultfeature information. The method provided by the invention can quickly extract fault features and solve the problem of long-distance transmission of a large amount of data.