572 results about "QR decomposition" patented technology

A method for the estimation of the state variables of nonlinear systems with exogenous inputs is based on improved extended Kalman filtering (EKF) type techniques. The method uses a discrete-time model, based on a set of nonlinear differential equations describing the system, that is linearized about the current operating point. The time update for the state estimates is performed using integration methods. Integration, which is accomplished through the use of matrix exponential techniques, avoids the inaccuracies of approximate numerical integration techniques. The updated state estimates and corresponding covariance estimates use a common time-varying system model for ensuring stability of both estimates. Other improvements include the use of QR factorization for both time and measurement updating of square-root covariance and Kalman gain matrices and the use of simulated annealing for ensuring that globally optimal estimates are produced.

The present invention describes a method of closed loop MIMO communication utilizing implicit or explicit channel state information (CSI) at the transmitter and the receiver. The transmitter performs linear pre-processing (for example, QR decomposition or bi-diagonal decomposition or Jacobi rotations, and / or sporadic SVDs) on a channel matrix, and the receiver mitigates the mutual interference between the streams by performing MMSE processing on the received signals. The MMSE matrix is computed with respect to the processed channel that may estimated by the receiver through preprocessed pilot signals. The transmitters preprocessing is of much lesser cost and complexity than full SVD.

A MIMO receiver is provided with a preprocessor for performing QR decomposition of a channel matrix H wherein the factored reduced matrix R is used in place of H and Q*y is used in place of the received vector y in a maximum likelihood detector (“MLD”). The maximum likelihood detector might be a hard-decision MLD or a soft-decision MLD. A savings of computational complexity can be used to provide comparable results more quickly, using less circuitry, and / or requiring less consumed energy, or performance can be improved for a fixed amount of time, circuitry and / or energy. Where the MLD uses approximations, such as finite resolution calculations (fixed point or the like) or L1 Norm approximations, the reduced number of operations resulting from using the reduced matrix results in improved approximations as a result of the finite resolution operations. Other methods of reducing the channel matrix might be used for suitable and / or cumulative advantages.

The invention discloses an internet information product recommending method based on matrix decomposition. The method comprises the following steps of: 1) obtaining the user scoring record to the information product; 2) obtaining the social relationship record between internet users; 3) respectively building a scoring matrix and a social matrix according to the types of a target user and a target product; 4) learning a user feature vector and a product feature vector through a matrix decomposition technology; 5) calculating the scores of different products scored by the target user according to the feature vectors, so as to recommending the favorite products of the user according to the scores. In the method, analysis on user social relationship is introduced, and personalized product recommendation is provided for the target user based on the production type information. The calculation is simple and quick, and the method has better expandability and adaptability, so that the method is suitable for highly dynamic and immense amount of product-oriented recommendation for the internet users.

A multiple-input multiple-output (MIMO) communication system is provided for communication between a transmission apparatus having a plurality of transmission antennas and a reception apparatus having a plurality of reception antennas. The reception apparatus performs singular value decomposition (SVD) on channel information for a channel established to the transmission apparatus, and feeds back the SVD-decomposed channel information to the transmission apparatus. The transmission apparatus receives the SVD-decomposed channel information from the reception apparatus, performs QR decomposition on the received SVD-decomposed channel information, and sets multiple transmission antennas to be allocated to the channel, thereby performing beamforming.

Provided are a multi-dimensional detector for a receiver of an MIMO system and a method thereof. The multi-dimensional detector includes a first symbol detecting unit for calculating symbol distance values using an upper triangular matrix (R) obtained from QR decomposition to detect an mth symbol; a symbol deciding unit for deciding a symbol having a minimum distance value among the calculated symbol distance values from the first symbol detecting unit; and a second symbol detecting unit for calculating symbol distance values using an updated received signal y and the upper triangular matrix R to detect a (m−1)th symbol.

In a multiple-input multiple output (MIMO) system, high-rate data transmission is achieved by dividing the original data stream into several parallel data substreams, each of which is transmitted from a corresponding transmit antenna (spatial multiplexing) and received by multiple receive antennas. The number of spatial streams depends on the number of antennas. In a receiver, a search-tree based QR Decomposition-M (QRD-M) algorithm is used. According to the invention, multiple spatial signal streams received from a MIMO channel are pre-ordered based on modulation alphabets of said received spatial signal streams prior to performing a QRD-M detection.

The invention discloses a multi-user multiple input multiple output (MU-MIMO) transmission method in a wireless communication system, a base station and a user terminal. The method comprises the following steps that: the base station receives detection pilot frequency SRS of N users to perform channel estimation and acquires downlink channel information according to the channel estimation result and channel reciprocity of the system, wherein N is more than 1; the base station performs quick response (QR) decomposition on the downlink channel information, acquires a multi-user beamforming (MU-BF) matrix P (i) of the ith user from a Q matrix acquired through decomposition, and acquires a downlink single-user beamforming (SU-BF) matrix V (i) of the ith user further, wherein i=1, ...,N; and the base station performs beamforming processing on transmitting data of the ith user according to the MU-BF matrix P (i) and the SU-BF matrix V (i). The method and equipment acquire beamforming matrixes for uplink and downlink MU-MIMO transmission by means of the channel reciprocity of the system and the QR decomposition, and the MU-MIMO transmission performance can be improved.

The invention provides a method for tomography velocity inversion based on angle domain common imaging gathers under a complicated condition. The method comprises the following steps of: by using a velocity model obtained by common velocity analysis as an initial velocity model, extracting the angle domain common imaging gathers based on a double-square-root continuation pre-stack depth migration method; picking a rest depth difference on the extracted angle domain common imaging gathers, and calculating a travel residual; picking a horizon interface on a migration section surface, building a reference velocity model needed by the ray-tracing forward, calculating a sensitivity matrix by using an undulating surface ray tracing method; creating an inversion equation set to which a regularization factor is added by using the travel residual and the sensitivity matrix; solving the equation set by using an LSQR (Least-squares QR decomposition algorithm) and updating the velocity model; and extracting the angle domain common imaging gathers by using the updated velocity model based on the double-square-root continuation pre-stack depth migration method.

A strong maneuver-based target tracking method comprises the following steps: initializing parameters, interactively inputting models, judging the covariance matrix, filtering in parallel, updating the model probabilities, interactively outputting the models, filtering in a fixed-delay smoothing manner, and judging whether the state updating is completed; on the basis of the IMM algorithm, the IMM algorithm for recalculating the weight is used, that is, RIMM. The method not only uses the model probabilities, but also takes full advantage of the filtering covariance matrix, so that the tracking accuracy is higher. In addition, an SRCK method is used in the filter forecasting stage, and adopts the spherical integrating criterion and the radial integrating criterion. Compared with a wide UKF algorithm in nonlinear filtering, the target tracking method optimizes the sigma point sampling strategy and the weight distribution in the UKF. Meanwhile, QR factorization is introduced in SRCKF, so that the matrix squiring operation is avoided, and the filtering stability is improved. On the basis, the filtering in a fixed-delay smoothing manner is also introduced, so that the real-time property and the accuracy of target tracking are further improved.

The invention discloses a collaborative recommendation method based on social context regularization. The collaborative recommendation method comprises the following steps of: 1) firstly, extracting a user object matrix and a socialization relation matrix, wherein during the collaborative recommendation, the user object matrix is defined by using a grading matrix of a user on an object, a clicking frequency of the user on the object or a visit relation, and the socialization relation is a relation, generated by some behaviors of the user, between the user and other users in the system; 2) filling the user object matrix by using a low-rank matrix decomposition method with the social context regularization and recommending N objects to each user by using a result matrix; and 3) adjusting the weight of the social context restraint during matrix decomposition in the consideration of difference among different users. By the method, the problems of single recommended information of the conventional collaborative filtering recommendation algorithm and inaccurate recommendation result caused by dilution of the user object matrix are solved; furthermore, compared with the conventional method, the method has the advantage of obviously enhancing the recommendation result accuracy.

The invention discloses a linear precoding method in a multiuser multi-input and multi-output (MIMO) system, which comprises the following steps: determining the interference channel transmission matrix HK in the system of each user according to the channel estimation results, wherein, k is user index, k is in the range from 1 to K, and K is the user number served by the system base station simultaneously in the same band range; carrying out the QR decomposition to the conjugate transposed matrix HK<H> of a random user's interference channel transmission matrix HK, and forming the user's linear precoding matrix T<k> according to the QR decomposition result; and carrying out the linear precoding to each user's emission signal s<k> respectively by utilizing the formed linear precoding matrix T<k>.

Receiving apparatus and method in a Multiple-Input Multiple-Output (MIMO) wireless communication system are provided. The receiver having N-ary receive antennas includes a decomposer for decomposing a channel matrix to a matrix Q and a matrix R through a QR decomposition; a detector for determining a candidate group of an n-th phase by estimating a plurality of transmit signal vectors by substituting a plurality of transmittable symbols into symbol combinations of a candidate group of a (n−1)-th phase as an n-th symbol and detecting (n+1)-th through N-th symbols using characteristics of the matrix R; a calculator for calculating square Euclidean distance values between the transmit signal vectors and a receive signal vector; and a determiner for determining the candidate group of the n-th phase by selecting transmit signal vectors having the smallest square Euclidean distance value among the transmit signal vectors.

The invention discloses a broadband sub-matrix adaptive beamforming method based on sub-band decomposition, which mainly resolves the problem that computation burden in the prior art is high, and broadband interference signals cannot be processed and suppressed adaptively. The implementation process of the broadband sub-matrix adaptive beamforming method includes steps: 1) dividing a total matrix into a plurality of sub-matrixes, leading the sub-matrixes to be aligned with local beam pointing by the aid of microwave synthesis of a phase shifter, and obtaining sub-matrix synthesis data; 2) selecting a prototype filter and obtaining corrected analyzing and comprehensive filter banks via a cosinusoidal modulation filter bank; 3) under-sampling the sub-matrix synthesis data after the sub-matrix synthesis data pass through the analyzing filter banks, solving an adaptive weight in a narrow band and performing sub-band beamforming in the narrow band; and 4) up-sampling signals after sub-band beamforming, leading the up-sampling signals to pass through the comprehensive filter banks, and summating data of the comprehensive filter banks to obtain data after broadband adaptive beamforming. The broadband sub-matrix adaptive beamforming method has the advantages that the dimension of hardware is small, computation burden is low and broadband interference signals can be suppressed adaptively. In addition, the broadband sub-matrix adaptive beamforming method can be used for adaptive beamforming of a broadband phased array radar.

The invention discloses a gear fault diagnosis method based on the SVD decomposition and noise reduction and correlation EEMD entropy features. The method includes utilizing an acceleration vibration sensor to acquire experimental platform gear vibration signals including four types of faults, namely gear normality, gear tooth breaking, gear tooth missing and gear wearing; performing noise reduction on the signals, of four gear states, containing simulated strong noise background of Gaussian white noise by the SVD decomposition method with correlation analysis and noise ratio optimization; decomposing the four types of noises by the EEMD method after noise reduction, and selecting valid IMF components according to correlative coefficients; performing sample entropy calculation on the valid IMF components, and establishing feature vectors composed of the IMF samples; identifying the four different types of gear faults through a PNN neural network. The method is effective and is capable of recognizing the gear fault types on the strong-noise background effectively.

The invention belongs to the technical field of wireless communication, in particular relating to a signal detection method and a device for a multiple-input-multiple-output wireless communication system. In the invention, the received signal vectors and the estimated channel information are used for detecting and recovering the sent signal vectors. The device of the invention comprises a preprocessing unit, a group of K-Best processing units and a group of zero-forcing processing units, wherein the preprocessing unit carries out real number decomposition and QR decomposition on a channel matrix to form a tree search process; the K-Best processing units extend all child nodes of the reserved father node at the upper layer and calculate the Euclidean distance, and finally, K nodes with theminimum Euclidean distance are reserved and transferred to the next layer; and the zero-forcing processing units extend the child nodes with the minimum Euclidean distance increment in the reserved father node at the upper layer. In addition, a generation technology to be selected, a candidate value sharing structure, a shifting technology, and the like, are adopted. The invention reduces the computation complexity of the MIMO signal detection and increases the speed of the MIMO signal detection.

The invention discloses a Freeman decomposition and homo-polarization rate-based polarized synthetic aperture radar (SAR) image classification method for mainly solving the problems of higher calculation complexity and poor classification effect in the prior art. The method comprises the following steps of: (1) inputting a covariance matrix of polarized SAR data; (2) performing Freeman decomposition on the input matrix to acquire three types of scattering power matrixes of plane scattering, dihedral angle scattering and volume scattering; (3) performing initial division on the polarized SAR data according to the three types of scattering power matrixes; (4) calculating the homo-polarization rate of all pixel points of the polarized SAR data of each class; (5) selecting a threshold value, and dividing the polarized SAR data of each class in the step (3) into 3 classes according to the homo-polarization rate, so that the whole polarized SAR data are divided into 9 classes; and (6) performing repeated Wishart iteration and coloring on the division result of the whole polarized SAR data to obtain a final color classification result graph. Compared with the classical classification method, the method has the advantages that the division of the polarized SAR data is stricter, the classification result is obvious and the calculation complexity is relatively low.

Signals in a multi-channel, impaired communication system are post-processed at the receiver. A triangular matrix Decision Feedback Demodulator (DFD) at the receiver extracts channels without requiring delivery of receiver parameters to the transmitter. Multi-Input Multi-Output (MIMO) processing matrices and DFD parameters are computed by first applying matrix transformations to diagonalize the noise covariance matrix of the multiple channels received at the receiver. QR decompositions (i.e., decompositions into orthogonal and triangular matrices) are then applied to the main channels to obtain triangular channel matrices. The noise-diagonalizing transformations and QR decompositions are then combined to form the MIMO postprocessing matrices and DFD parameters. MIMO postprocessing matrices and DFD parameters are computed from training data and then adapted during live data transmission.

An affine transformation analysis system and method is provided for matching two images. The novel systolic array image affine transformation analysis system comprising a linear rf-processing means, an affine parameter incremental updating means, and a least square error fitting means is based on a Lie transformation group model of cortical visual motion and stereo processing. Image data is provided to a plurality of component linear rf-processing means each comprising a Gabor receptive field, a dynamical Gabor receptive field, and six Lie germs. The Gabor coefficients of images and affine Lie derivatives are extracted from responses of linear receptive fields, respectively. The differences and affine Lie-derivatives of these Gabor coefficients obtained from each parallel pipelined linear rf-processing components are then input to a least square error fitting means, a systolic array comprising a QR decomposition means and a backward substitution means. The output signal from the least square error fitting means then be used to updating of the affine parameters until the difference signals between the Gabor coefficients from the static and dynamical Gabor receptive fields are substantially reduced.

The present invention relates to a method and device for detecting a transmission signal on the basis of a received signal by applying a division and detection algorithm. An embodiment of the invention provides a method of detecting a transmission signal including: obtaining a unitary matrix and an upper triangular matrix by performing a sorted QR-decomposition algorithm with respect to a matrix indicating a channel state; calculating a vector y by multiplying a transpose matrix of the unitary matrix by the received signal Y; dividing the upper triangular matrix R into a plurality of sub-upper triangular matrices and dividing the calculated vector y into a plurality of sub-vectors so as to correspond to the divided plurality of sub-upper triangular matrices; and detecting a lattice point corresponding to each of the divided sub-vectors using the divided plurality of sub-upper triangular matrices.

The invention relates to a hand action identification method based on surface electromyography (sEMG) decomposition. The method comprises the following two parts including sEMG decomposition and a hand action identification method based on MUAPT (motor unit action potential train), wherein the sEMG decomposition is carried out to obtain the MUAPT and consists of sEMG signal preprocessing, sEMG peak detection and Gaussian mixture model (GMM) clustering; and the hand action identification based on the MUAPT comprises characteristic extraction, principal component analysis (PCA) dimensionality reduction, LDA (Linear Discriminant Analysis) classification and the like. Under a condition that only one channel sEMG is used, motor unit action potential information obtained by the sEMG signal decomposition is used for identifying hand action, an identification rate of single-channel sEMG is effectively improved, and the hand action identification method has important theoretical significance and practical application value.

A transmitting device Fourier-transforms symbols in a transmission symbol sequence, maps the Fourier-transformed symbols to subcarriers, inverse-Fourier-transforms the mapped symbols, and transmits the inverse-Fourier-transformed symbols from multiple transmitting antennas. A receiving device Fourier-transforms received signals, extracts signal components mapped to the subcarriers, and estimates the symbols transmitted via the subcarriers by applying a QR decomposition algorithm to the extracted signal components. The receiving device obtains a unitary matrix QH such that the product of the unitary matrix QH, a weight matrix W determining a correspondence between the transmission symbol sequence and the subcarriers, and a channel matrix H becomes a triangular matrix R, and estimates candidates of the symbols transmitted from the transmitting antennas based on the unitary matrix QH and the triangular matrix R.

A processor is adapted for performing a QR-decomposition. The processor has a program memory, a program controller, connected to the program memory to receive program instructions, and at least one processing unit. The processing unit includes a CORDIC calculation block, and has a distributed memory structure, with separate memory blocks for storing respective parameter values.

A self-integrity monitoring method suitable for a satellite-borne navigation receiver can reflect statistics detection amount of monitoring data quality by utilizing a pseudo-range information structure of the satellite-borne navigation receiver and detect faulted satellites according to the index requirements of a system and a distribution structure detection threshold obeyed by statistics amount. The self-integrity monitoring method suitable constructs the statistics detection amount of an odd-even space through QR decomposition of a primary observation array and based on the least square method, and carries out usability judgment, fault detection and fault identification in the odd-even space. The self-integrity monitoring method has higher response speed, saves more resources and is more suitable for satellite-borne application.

Provided is a maximum likelihood decoding method of separating and estimating multiple transmitted signals transmitted by multiple transmitter antennas from multiple received signals received by multiple receiver antennas, comprising: a first step of generating a channel matrix based on channel impulse responses corresponding to the received signals; a second step of dividing the generated channel matrix into multiple sub-matrices, of identifying parts of the received signals corresponding to the sub-matrices obtained through the division, and of transforming the sub-matrices obtained through the division, by using inverse matrices of the sub-matrices obtained through the division; a third step of applying QR decomposition to the transformed sub-matrices to obtain triangular matrices, and of transforming the received signals of the parts by using the obtained triangular matrices; and a fourth step of determining one combination candidate for the parts of the transmitted signals corresponding to the transformed received signals.

The invention provides a stable and fast real-time registration method for depth maps shot by the kinect in real time and video shot by a color camera. According to the method, the step of estimating internal parameters of a depth camera is removed, and the stability of an algorithm is enhanced while the parameters to be solved are reduced. A linear optimization frame is used for solution, the globally optimal solution can be obtained in one step, and thus the calculation efficiency of the algorithm is greatly improved. Although the step of estimating the internal parameters of the depth camera in a traditional algorithm is removed, the executing efficiency for mapping depth information to the video is not affected, and the depth information captured by the kinect can still be mapped to the video in real time. Moreover, because the defined hybrid parameters have good mathematical properties, the internal parameters of the depth camera can still be determined in a reverse mode with a matrix QR decomposition technology to be used by other algorithms.

The invention relates to a multi-source information fusion method based on a factor graph. The multi-source information fusion method aims to realize full-source positioning and navigation without relying on satellite navigation in a complex environment, takes an inertial navigation system as the core, utilizes all available navigation information sources, and performs rapid fusion, optimal configuration and self-adaptive switching on asynchronous heterogeneous sensor information. A factor graph model is constructed by means of recursive Bayesian estimation, the factor graph is broadened by means of a variable node and a factor node of the system after measurement information of different sensors are acquired, state recursion and updating are completed based on a set cost function, and thefactor graph optimization problem is solved through sparse QR decomposition by adopting an increment smoothing method. The multi-source information fusion method effectively solves the time-varying state space problem generated between carrier motion and measurement availability, can calculate a solution of precise navigation according to dynamic changes of a carrying platform, realizes plug-and-play of multiple sensors, and meets the requirements of carriers changing in complex environment and different tasks.

A communication node that relays signals between a source node and a destination node includes (a) a first unitary matrix calculation unit configured to calculate a first unitary matrix based on a first channel between the source node and the relay node, (b) a second unitary matrix calculation unit configured to calculate a second unitary matrix based on a second channel between the relay node and the destination node, (c) a transformation matrix estimation unit configured to estimate a transformation matrix based on a triangular matrix derived from QR decomposition of the first and / or second channel matrix, (d) a relaying signal generator configured to generates a relaying signal by multiplying a received signal by at least one of the first unitary matrix, the second unitary matrix, and the transformation matrix, and (e) a transmission unit configured to transmit the relaying signal to the destination node.