67 results about "Bayesian compressive sensing" patented technology

The invention discloses an indoor wireless terminal positioning method based on Bayes compression sensing. The method includes the steps of performing grid division to an indoor area where wireless terminals needed to be positioned are located and ensuring the size of each grid equal to the needed positioning accuracy ; collecting the signal intensity from each wireless access point at each grid point respectively and uploading the signal intensity to a central server; enabling all the signal intensity data of a same wireless access point collected at all grid points to correspond to positions of corresponding grid points by the aid of the central server so as to acquire a location finger-print map of each wireless access point respectively; collecting the signal intensity from each wireless access point at the wireless terminal needed to be positioned in real time and uploading the signal intensity to the central server; and performing compressive sampling to received real-time signal intensity data collected at the wireless terminal needed to be positioned by utilizing the position finger-print map by the aid of the central server, performing multitask Bayes combined compression reconstruction algorithm to the signal intensity after being subjected to the compressive sampling, and finally acquiring a real position of each wireless terminal needed to be positioned.

The invention provides a distributed source center direction-of-arrival estimation method based on Bayesian compressed perception, and belongs to the technical field of wireless mobile communication. The invention mainly aims to solve the problem concerning inherent error of center direction-of-arrival estimation when the center angle of arrival of an information source is not on an angle sampling grid. According to the invention, an antenna array composed of parallel uniform linear arrays is arranged; an approximate array data reception model of a distribution source is established; the space-domain angle is sampled; a parameterized over-complete redundant dictionary is constructed by using an array steering vector so as to make the problem of distributed source center direction-of-arrival estimation converted into the problem of sparse matrix equation solving; a Bayesian compressed perception method is adopted to solve the equation set and obtain the most sparse solution of an unknown sparse vector; and the estimated value of the center direction of arrival is obtained according to the one-to-one correspondence relationship between sparse solutions and space-domain angles. The method of the invention is low in computing complexity, and has the characteristics of high resolution and accuracy under the condition of a small number of snapshots.

The invention discloses a multipath utilization through-the-wall radar imaging method based on the Bayes compression perception. A single station single transceiving antenna frequency stepping radar system is used for emitting pulse strings in multiple positions parallel to a wall face and acquiring echo signals; compression sampling is performed on the echo signals by randomly measuring a matrix so as to obtain relatively few measurement signals; then, by combining a dictionary construction compression perception model designed based on the multipath reflection characteristics of the wall face, the Bayes compression perception is used for reconstructing information of scenes after the wall face from the measurement signals; and finally the reconstructed information is sued for finishing through-the-wall radar imaging. According to the invention, cost is reduced; recording time is shortened; by use of the Bayes compression perception technology, calculation speed is quite fast, and required observation data are quite few; and the precision of images obtained through combination of the multipath utilization method is quite high and occurrence of false shadows is effectively inhibited.

The invention provides a method for Bayes compressed sensing signal recovery based on a self-adaptive measurement matrix and relates to the field of the information and communication technology. The method aims at solving the problem that an existing compressed sensing signal recovery method is low in accuracy. Based on the design of the self-adaptive measurement matrix in compressed sensing and combined with the Bayes compressed sensing algorithm, a design scheme of the compressed sensing method is obtained. The method is characterized in that the designed measurement matrix can be generated in a self-adaptive mode according to different signals, the purposes of determinacy and storage of the matrix are both achieved, and combined with the Bayes compressed sensing recovery algorithm of a relevant vector machine, the priority of a layered structure is introduced. The design scheme passes simulation verification, it is confirmed that the good signal recovery effect can be obtained, and the error range of recovered signals can be evaluated. The method is suitable for wireless signal transmission occasions in the information and communication technology.

The invention belongs to the technical field of wireless communications, and particularly relates to a method for reconstructing signals in an ultra-wide-band communication system by the adoption of a low-complexity improved Bayes compressed sensing algorithm. The method comprises the steps that firstly, sectioning and filtering are conducted on received signals, then a measuring matrix is used for conducting linear combination on the filtered signals again, after a series of relatively simple iterative operations, the expansion coefficient of original signals under the characteristic base can be measured, and therefore reconstruction of the original signals is realized. According to the method for reconstructing the signals, on one hand, the Bayes compressed sensing algorithm is improved by fully utilizing the characteristics of the characteristic base, the restorability is improved, more importantly, the complex matrix inversion process in the Bayes compressed sensing algorithm is avoided, and particularly, when the length of the signals is large and the order of the matrix is large, the signal restoration operation complexity can be effectively lowered.

The invention provides a compressed sensing ultra-wide band channel estimation method and system based on a cluster position set. The compressed sensing ultra-wide band channel estimation method includes the steps of channel cluster information acquisition and channel impact response estimation. The compressed sensing ultra-wide band channel estimation method has the advantages that the channel estimation method is composed of two stages, in the previous stage, the cluster structured characteristic of a channel is estimated, and feedback information is provided; in the later stage, cluster positions estimated in the previous stage are taken as the prior constraint in a channel impact response restructing algorithm, the quasi-static characteristic and the structured characteristic of the channel are fully used in the two stages, according to the quasi-static characteristic, in a period of time, the channel changes slowly, and it can be approximately thought that the channel does not change. According to the estimation method, based on a Bayes compressed sensing frame, it is unnecessary to know the sparse degree of the channel, the cluster position structure characteristic of the channel is integrated into the restructing algorithm, the complexity of implementing the estimation method is low, and the estimation performance is promoted to a certain degree in comparison with a traditional method.

The invention provides a radar target recognition method based on a sparse feature. The method is used for improving the pertinence of data recognized by a radar target and reducing the computation burden of data processing by taking the sparse feature of a radar target image as a radar target image training sample and the recognition feature of a radar target to be measured; then, a sparse linear equation of the radar target to be measured is established according to the sparse feature of the training sample; then, the sparse linear equation is solved by using a Bayes compressed sensing algorithm, and the radar target to be measured is recognized by means of a compressed sensing theory instead of target azimuth estimation, so that the recognition complexity is lowered, and the recognition accuracy is prevented from depending on the target azimuth estimation; and meanwhile, the radar target recognition based on the compressed sensing theory also has favorable recognition performance under a noise environment, thus the problems that a radar target recognition system in the prior art is more complex and limited in recognition accuracy are solved, and the aims of increasing the processing efficiency for radar target recognition and improving the recognition accuracy are achieved.

The invention relates to a millimeter wave channel estimation method based on Bayesian compressive sensing, which comprises the following steps: fixing a sensing matrix, and repeatedly training a sequence to get a multi-measurement-channel matrix and an observation matrix; getting a complex model of multiple measurement vectors according to the multi-measurement-channel matrix, separating the virtual and real parts of the complex model of multiple measurement vectors, and converting the complex model of multiple measurement vectors into a real model of multiple measurement vectors, and vectorizing the real model of multiple measurement vectors to get a real model of single measurement vector, wherein the real model of single measurement vector satisfies the Gauss likelihood distribution; setting the initial values of hyper parameters in the Bayesian learning process and an iteration stop condition; calculating the channel posterior probability distribution containing hyper parameters and the mean and covariance; and solving the hyper parameters through an expectation maximization algorithm until the iteration stop condition is satisfied. The error of millimeter wave channel estimation can be reduced.

The invention belonging to the field of the wireless communication technology relates to a channel estimation algorithm of a multi-user large-scale multiple-input multiple-output (MIMO) system in a frequency division duplexing (FDD) mode. According to joint sparsity of a multi-user large-scale MIMO channel, channel estimation and reconstruction are carried out by approximate replacement and iterative estimation algorithms based on a Bayesian compressed sensing theory. With the method provided by the invention, the process of direct matrix inversion of the posterior probability of X obtained by the EM algorithm is avoided, so that the operation complexity is reduced, the operation load is reduced, the operation speed is increased, and the operation precision is improved.

The invention discloses a Bayes compressed sensing based broadband radar echo signal parameter estimation method. The method includes the following steps: a step of quadrature compressed sampling for compressed sampling of broadband signals to obtain lower-speed in-phase and quadrature compressed measurement; a step of time delay-scale factor two-dimensional discretization dictionary construction for sparse representation of compressed measurement; and a step of Bayes compressed algorithm for sparse reconstruction of an original signal and further estimation of target parameters. The Bayes compressed sensing based broadband radar echo signal parameter estimation method effectively improves the performance of joint estimation of broadband echo signal time delay-scale factors and solves the problem that the sparse reconstruction performance is affected by high correlation of a two-dimensional discretization dictionary.

The invention discloses an SVD (singular value decomposition) noise reduction based Bayes compressed sensing radar data integrating method. The method includes steps of acquiring echo wave data of a radar target at different frequency points and in different azimuth angles and adding noise of different degrees into echo wave data obtained through simulation for standby use for testing; utilizing aSVD noise reduction method, performing denoising treatment on the obtained radar echo wave signals; utilizing a Bayes compressed sensing method for performing data integration on sparse frequency band radar echo wave data subjected to denoising treatment and reconstructing full frequency band echo wave data; performing reverse synthetic aperture radar imaging by utilizing the full frequency bandradar echo wave data reconstructed through data integration. According to the invention, denoising treatment is performed on input radar echo wave before multi-frequency-band radar data integration, so that relative errors of the Bayes compressed sensing method in full-frequency-band radar signal reconstruction is reduced distinctively and radar imaging resolution and accuracy are improved.

The invention discloses a broadband radar target reecho denoising method based on Bayes compressed sensing, and mainly aims to solve the problems that in the prior art a broadband radar target is not precisely described, the reconstruction error is relatively largely influenced by noise priori, and the real-time property is not achieved. The method comprises the following steps: (1) acquiring single-time repeated range image; (2) estimating noise power according to the single-time repeated range image, and initializing noise variance by using the noise power; (3) initializing basis vectors and hyper-parameters, and calculating the average value of covariance sums; (4) selecting basis vectors and updating the hyper-parameters from a super-resolution basis matrix to update covariance, average values and noise variance; (5) recovering the single-time repeated range image by using the super-resolution basis matrix and the average values. By adoption of the method, the stability of signal to noise ratio and the reconstruction error of the broadband radar target reecho to noise prior is improved, the real-time property requirement is met, and radar echo of moving targets such as planes and automobiles are subjected to noise inhibition under the noise background.

The invention relates to a vector-auto-regression-based Bayes compressed sensing feedback method in an MIMO system. The vector-auto-regression-based Bayes compressed sensing feedback method includes the following steps: (1) building an MIMO channel model, (2) building a VAR model to achieve channel prediction, and (3) reducing the feedback speed through Bayes compressed sensing. Compared with the prior art, the vector-auto-regression-based Bayes compressed sensing feedback method has the advantages that the relation between adjacent CSIs is described by introducing the VAR model, space-time compression is introduced to reduce the size range of a channel, and the dimensionality of channel vectors is reduced.

The invention discloses a non-reference image quality objective evaluation method based on Bayesian compression perception. In this method, the reconstructed image is reconstructed from the input distorted image by using the reconfigurable property of Bayesian compressed sensing method, the structure similarity index of the input distorted image and the reconstructed image is calculated, and the average value of the structure similarity index of the whole image is extracted as the similarity feature of image quality evaluation. The input distorted image is normalized, the uniform local binarypattern histogram of the normalized image is extracted as the uniform local binary pattern feature, and according to the similarity feature extracted from Bayesian compressed perception and the uniform local binary mode histogram feature, the image quality evaluation feature is obtained. The image quality evaluation feature is sent to the support vector regression machine for training and testing,and the image quality evaluation result of the input distorted image is obtained. This method utilizes the reconfigurable property of Bayesian compressed sensing method to evaluate the image quality,and improves the precision of image quality evaluation.

The invention discloses a face recognition method integrating a kernel and Bayesian compressed sensing. The face recognition method comprises a face recognition system and is characterized by including performing local binary pattern feature extraction, kernel space mapping and Bayesian compressed sensing classification. According to the arrangement, advantages of the compressed sensing plan and the Bayesian method are integrated; according to the compressed sensing plan, original images can be reconstructed by solving a sparse coefficient matrix, by the aid of limiting of prior information of the Bayesian method to the sparse coefficient matrix, influence of noise is avoided to a certain extent, error range is estimated, and the images are reconstructed effectively; influence of factors including light, shielding objects and expression changes to face recognition is overcome, and recognition rate is increased to 99% of the highest; reconstruction recognition is achieved by means of sparse coefficient matrix, and running speed is higher than that of a support vector machine.

The invention provides a compression sensing and convex optimization-based near-field sparse antenna array optimization method. The method comprises the following steps of: 1, determining a template pattern, having any shape, in a radiation near-field region needing radiating near-field region; 2, establishing a near-field sparse optimization model; 3, solving a near-field sparse problem by adoption of a Bayesian compression sensing theory; and 4, adding limiting to a minimum inter-array element distance through post-processing and convex optimization. The compression sensing and convex optimization-based near-field sparse antenna array optimization method is disclosed for the first time; and under the premise that a radiation near-field region pattern generated by adoption of the method is consistent with a given template pattern, the antenna array element number can be remarkably reduced when being compared with traditional uniform antenna arrays for generating template patterns.

The invention discloses a broadband distributed Bayes compression spectrum sensing method. The method is applied to detection of broadband spectrum occupation condition in a cognitive radio system and is a new method for spectrum sensing in a broadband range by use of a Bayes compression sensing technology. The actual distribution condition, with which Bayes parameters are in compliance, is considered under the spectrum sensing condition of the distributed multiple cognitive users. Due to introduction of a Laplace hierarchical prior model, experience information occupied by spectrum is fused in the detection process. Cooperative detection of multiple cognitive users is fully considered, reconstruction errors brought about by compression sensing are reduced to the greatest extent through a distributed joint reconstruction algorithm, and the spectrum occupation condition of each sub-band can be detected rapidly and accurately.

The invention discloses a Bayesian-compressed-sensing-algorithm-based sparse array optimization method. The method comprises the following steps: (1), converting an array sparse problem into a Bayesian probability matching problem of a reference beam of an array, and solving a correlation vector machine to obtain an initial sparse array; (2), performing first-order Taylor approximate expansion onthe obtained initial sparse array to increase a position offset of the array element position, and optimizing the sparse array with the increased position offset; and (3), defining a minimum array element spacing value, combining array element points of which the spacing is smaller than the minimum array element spacing value to achieve the purpose of constraining the minimum array element spacing, and finally calculating a weight coefficient of an array element through a convex optimization technology. The sparse array optimization method has relatively high calculation efficiency, the same beam pattern performance is obtained by adopting a smaller number of transducers; meanwhile, the minimum spacing of array elements of the sparse array is constrained on a reasonable numerical value.

The invention discloses a distance-Doppler imaging method based on multi-task Bayes compression sensing and mainly solves problems of high complexity and low resolution existing in the prior art. The method comprises steps that 1), a pulse modulation sequence is emitted by a radar to a target scene, and an echo signal is further received by the radar; 2), a target scene distance scope and a Doppler scope are divided at equal intervals, discrete sampling for the echo signal is carried out, and an echo matrix is constructed; 3), an auxiliary variable is cited, and an optimization solution function is constructed; and 4), the optimization function is solved, and the distance-Doppler imaging result of the scene is acquired. According to the method, scene distance-Doppler dimensions are reconstructed, not only are algorithm complexity reduction and resolution improvement realized, but also complex parameter setting is not required, and the method can be applied to fields of baffle field monitoring, missile guidance and space detection.

The invention relates to a method for detecting cycle slips in positioning signals, and in particular to a tightly coupled GPS / INS (Global Positioning System / Inertial Navigation System) cycle slip detection and repair algorithm based on Bayesian compressive sensing. The algorithm consists of two parts, the first part is creating a tightly coupled GPS / INS navigation system positioning model, and also overcomes the defect that satellite navigation can be easily blocked by terrains and that INS navigation errors can be accumulated as time goes by, so that the respective advantages can be sufficiently exerted; the second part is constructing a sparse cycle slip detection and repair model, firstly inter-station differentiation and epoch differentiation are carried out on an original carrier observation quantity, so that a carrier phase double-difference model is obtained, secondly cycle slip signal detection based on compressive sensing is obtained on the basis of the double-difference model, and finally cycle slip signals are repaired. The algorithm can effectively decrease the error rate of GPS cycle slip detection and increase the accuracy of GPS cycle slip repair, so that the positioning precision of GPS differential positioning is increased, and the algorithm has a wide application prospect.

The invention discloses a sound source identification method based on Bayesian compressed sensing. The method comprises the following main steps: 1) establishing a sound source identification system based on Bayesian compressed sensing; 2) each microphone respectively monitoring time domain analog sound pressure signals of N equivalent sound sources; 3) converting the received time domain analog sound pressure signals into digital sound pressure signals p by a multi-channel data collector; 4) acquiring a transfer matrix A between the sound sources and a microphone array sensor; 5) establishinga prior probability distribution function model of the digital sound pressure signals p and a to-be-identified sound source q; 6) establishing a posterior probability distribution function model of the to-be-identified sound source q; 7) updating hyper-parameters of the to-be-identified sound source q; and 8) a data processor performing iterative computation on the hyper-parameters of the to-be-identified sound source q by using a parameter updating formula to obtain an identification result of the to-be-identified sound source q. According to the invention, the disadvantage of narrow applicable frequency range of TRESM and WBH methods is effectively overcome, and the frequency range of sound source identification is widened.

The present invention provides a low-complexity signal detection method under generalized spatial modulation. The method comprise the steps of (1) carrying out parametric modeling on a sending signal, and using a Bayesian compressive sensing iterative method with simplified complex to solve a parameter vector, (2) using signal sparsity information to carry out pre detection on an obtained activation antenna sequence set, and repositioning a partially wrong activation sequence, and (3) using an estimated activation antenna sequence set to carry out linear equalization and demodulation on the sending signal. According to the method, the accuracy of signal detection is greatly improved to approach the ideal result of maximum likelihood, extremely low computational complexity is ensured at the same time, and the method is also applicable to underdetermined and higher order modulation systems.

The invention discloses a bioluminescence tomography reconstruction algorithm based on a multitask Bayes compressed sensing method and belongs to the field of medical image processing. The bioluminescence tomography reconstruction algorithm based on the multitask Bayes compressed sensing method comprises the steps that firstly, the intrinsic relevance between multiple spectra is explored according to the rule of transmission of model light of a high-order approximation model in biological tissue and based on the multitask learning method, the intrinsic relevance between the multiple spectra is taken as prior information to be infused in the reconstruction algorithm so as to reduce the morbidity of BLT reconstruction, and finally, three-dimensional reconstruction of a fluorescent light source is achieved on the basis. Compared with other bioluminescence tomography reconstruction algorithms, the bioluminescence tomography reconstruction algorithm based on the multitask Bayes compressed sensing method has the advantages that the intrinsic relevance between the multiple spectra is further fused, the morbidity of BLT reconstruction is reduced, accurate reconstruction and positioning of the fluorescent light source are achieved, and the calculation efficiency can be greatly improved.

The invention discloses a method for adaptive multi-task Bayesian compressive sensing for direction-of-arrival estimation (AMT-BCS-DOA for short). The method which relates to the technical field of hydroacoustic engineering or oceanographic engineering comprises three parts: part one, carrying out sparse representation of a hydroacoustic target signal; part two, executing an adaptive multi-task Bayesian compressive sensing algorithm; and part three, executing an energy threshold elimination method to improving the direction estimation precision and completing DOA estimation. The second part of putting forward the adaptive multi-task Bayesian compressive sensing algorithm serves as the core part; the first part is a preprocessing part of the core part; and the third part is the output content processing part of the core part. According to the (AMT-BCS-DOA method, on the basis of the multi-task learning idea, the influence on the direction estimation precision by the noise environment is reduced. Meanwhile, the observation number can be adjusted adaptively according to incoming wave signal number changing, so that the observation number is saved and the direction estimation precision is improved. The method is suitable for a hydroacoustic positioning environment changing rapidly.

The invention discloses an adaptive beamformer design method based on a Bayesian compressed sensing algorithm, and the method comprises the following steps: (1) enabling a planar array element to receive a sound wave incident signal, wherein the sound wave incident signal comprises a target signal and an interference signal, constructing an adaptive beamformer based on the sound wave incident signal, and adopting the Bayesian compressed sensing algorithm to perform direction-of-arrival estimation on the sound wave incident signal; (2) reconstructing according to the direction of arrival of theinterference signal and the Capon spatial spectrum of the incident signal of the incident sound wave to obtain an interference and noise covariance matrix; (3) defining a maximum sidelobe horizontalconstraint value, constraining a sidelobe of the target beam pattern according to the maximum sidelobe horizontal constraint value, and calculating an array element weight coefficient of the adaptivebeamformer through a convex optimization method according to the interference and noise covariance matrix. And the output signal-to-interference ratio is higher.

The invention discloses an environment data acquisition method, system, computer device and storage medium. The method comprises: acquiring environment acquisition data of a partial area in an area tobe acquired; and performing data reconstruction on the environment acquisition data of the partial area according to a Bayesian compressive sensing algorithm to obtain the environment data of the area to be acquired. According to the environment data acquisition method, system, computer device and storage medium, sensors used for acquiring the environment data are deployed on a shared conveyance,so that the environment data is automatically acquired and uploaded when a user uses the shared conveyance, and the environment data is finally reconstructed by the cloud, thereby displaying the environment condition of the whole area in real time. Since the sensors are deployed on the shared conveyance and move along with the shared conveyance, the environment data of most areas can be acquiredinstead of the environment data of fixed points, and data reconstruction is performed after uploading the environment data, so that the environment data of the whole area can be reconstructed according to the partial environment data in the area, thereby realizing dynamic monitoring of the whole area.

The invention discloses a satellite navigation signal capturing method and device; wherein the method comprises the steps: carrying out the correlation of a digital intermediate frequency signal X anda local complex carrier signal, and generating a zero intermediate frequency complex signal S; performing correlation operation on the zero intermediate frequency complex signal S and local codes ofdifferent initial phases to obtain a correlated signal Y; performing piecewise integration on the signal Y to generate an integrated signal Yr; randomly sampling the signal Yr to obtain a signal Ys; knowing the observation matrix phi s and the signal Ys, and generating a frequency signal theta by adopting a fast Bayesian compressed sensing reconstruction algorithm; when the frequency signal thetameets a preset threshold range, judging that signal capture succeeds; the frequency offset searching range and searching precision are improved, and the capturing sensitivity is high.

The invention discloses an optimal design method for a multi-connected domino irregular sub-array plane. The optimal design method comprises the following steps: 1, determining a dictionary matrix through array plane aperture distribution and multi-connected domino sub-array types; 2, acquiring a sampling matrix of the optimal scanning directional diagram; 3, selecting an initial point based on amulti-task Bayesian compressed sensing algorithm; 4, solving and optimizing the dictionary matrix by using an iterative convex relaxation algorithm; and 5, checking whether an iteration result meets an accurate coverage condition or not, and outputting a final solution or backtracking the step 4. According to the optimal design method, a convex relaxation iteration-based set coverage model solvingmethod is creatively provided, so that the 0 / 1 integer programming problem in the sub-array layout can be effectively solved; sub-array layout schemes meeting the accurate paving condition can be effectively and optimally selected, and the output design result can meet the requirements for aperture efficiency maximization and relatively excellent scanning electrical performance at the same time;and different modular multi-connected domino sub-array design schemes can be customized according to different requirements.

The invention discloses a Bayesian compressive sensing imaging method based on generalized Pareto distribution, thereby improving performance of an existing inverse synthetic aperture radar. Accordingto the method, in inverse synthetic aperture radar imaging based on Bayesian compressive sensing, and the generalized Pareto distribution is used as prior information, thereby improving reconstruction performance of an algorithm. Compared with prior art, the Bayesian compressive sensing imaging method is advantageous in that signal reconstruction can be performed through smaller number of measurement values or on the condition of higher sparsity. An image with higher imaging quality can be obtained through simulated and actual measured inverse synthetic aperture radar data.

The invention discloses a digital watermarking method based on structured Bayesian compressive sensing; according to the method, sparse priors are set in a Bayesian compressive sensing model with signal structured priors introduced, such that Bayes gains better performance to restore signals, and attack resistance of digital watermarks is improved. The digital watermarking method based on structured Bayesian compressive sensing is easy to implement, is friendly to the displaying of carrier images, and has good transparency and robustness.