571 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.

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 m^th 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 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>.

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.

The present invention discloses a detection method, a receiver, and an apparatus in a multiple-input multiple-output single-carrier block transmission system. Through sorted QR decomposition (Sorted QR Decomposition, SQRD) decomposition based on a minimum mean squared error (Minimum Mean Squared Error, MMSE) criterion, an matrix inverse operation in minimum mean squared error equalization is avoided, and complexity is significantly reduced. In addition, through layer-by-layer soft interference cancellation detection after the SQRD decomposition and further iterative processing, better detection performance may be obtained. Especially, inter-stream interference suppression may be performed in first iteration. Compared with a conventional parallel iterative soft interference cancellation detection algorithm, with the same number of times of iteration, especially under the limit that a receiver can only support a small number of limes of iteration, the inter-stream interference suppression has more evident advantages, and an advantage in the aspect of cost performance is very obvious.

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.

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 Q^H such that the product of the unitary matrix Q^H, 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 Q^H and the triangular matrix R.

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.

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.