201 results about "Cholesky decomposition" patented technology

The invention provides a satellite lithium ion battery residual life prediction system and a satellite lithium ion battery residual life prediction method based on RVM (relevance vector machine) dynamic reconfiguration, and relates to a lithium ion battery residual life prediction system and a lithium ion battery residual life prediction method. The uncertainty expression of the lithium ion battery predication is realized, and the lithium ion battery residual life prediction method is more applicable to satellite system environment with limited resources. A dynamic reconfiguration module of the prediction system comprises a reconfiguration unit A and a reconfiguration unit B, the reconfiguration unit A and the reconfiguration unit B realize the time sharing multiplex of logic resources of the dynamic reconfiguration module, and the RVM training and predication is realized; and the Gaussian kernel function flowing water calculation is realized by a multistage flowing water segmented linear proximity method and a parallel computing structure, and the computational efficiency is enabled to be fully improved. The inverse calculation of symmetric positive definite matrices is realized by a Cholesky decomposition method, the computing resources consumption is reduced by a multiplying and gradually decreasing device, and the computing delay is reduced. Experiments show that the system and the method have the advantages that FPGA (field programmable gate array) finite computing resources are utilized for realizing the computational accuracy similar to a PC (personal computer) platform, the four-times computing efficiency improvement relative to the PC platform is obtained, and the utilization rate of hardware resources is effectively improved through dynamic reconfiguration strategies.

A method and arrangement for use in a single user detector in a CDMA multi-path system, comprising providing a finite impulse response (FIR) filter; and configuring the FIR filter to derive at least a first, then a second, of the following parameters for single user detection: R (auto-correlation) function; L (Cholesky decomposition) value; X value; Y value; and S value. In this way, five of the steps required to implement the single user detector can be implemented using an FIR filter, simplifying the method and arrangement required to implement the single user detector.

An algorithm for computing an efficient, reduced complexity, windowed optimal linear time domain equalizer for a dispersive channel comprises the steps of determining a window of maximum energy in the impulse response of length equal to or less than a number of cyclic prefix samples associated with a received digital data signal, computing the corresponding inside and outside matrices, performing an inverse Cholesky decomposition of the inside matrix, creating a resultant matrix as the product of the outer and the upper and lower square root inner matrix, followed by Householder reduction and QL transformation to thereby compute the time domain equalizer as the linear transformation of the eigenvector corresponding to the smallest eigenvalue at the receiver. The smallest eigenvalue is determined using the aforementioned orthogonal transformations without determining all the eigenvalues efficiently but without the loss accuracy associated with iterative methods like the conventional power method. The algorithm may be most conveniently implemented, for example, in the form of a thirty-two bit digital signal processor at a data receiver.

The invention discloses an FPGA (Field Programmable Gata Array)-based unscented kalman filter system mainly solving the problem that the traditional unscented kalman filter hardware has great implementation difficulty and poor instantaneity and comprising a covariance matrix Cholesky decomposition model A, a Sigma point generation module B, a time updating module C, an observation and prediction module D, a part-mean value and covariance matrix computation module E, a population mean value computation module F, a population covariance matrix computation module G, an observation and predictioncovariance matrix inversion module H, a gain computation module I and a state quantity and state covariance matrix estimating module J, wherein the module A generates K group of column vector to the module B and the B, C, D and E modules are connected in series and respectively comprise K submodules adopting a parallel arithmetic modular construction; the F and G modules receive and process the Kgroup of results of the module E and the processed results pass through the modules H, I and J in sequence to obtain the present result. The invention has the advantages of quick filter speed and easy hardware implementation and can be used for target tracking and parameter estimation.

The invention provides a method for processing global position system (GPS) deformation monitoring data. The method for processing the GPS deformation monitoring data comprises (1) obtaining present epoch observation data and broadcast ephemeris data of a base station and a monitoring station, and forming a double-difference observation equation by using phase observation values; (2) setting a maximum deformation quantity between two epochs; (3) adopting a Cholesky to resolve and construct an ambiguity search space; (4) searching the ambiguity, and enabling a variance ratio to be larger than 3; (5) obtaining fixed solution coordinates X, Y and Z of a single epoch whole cycle ambiguity and a convariance matrix of the single epoch whole cycle ambiguity of a monitoring point; (6) using an average gap method to carry out deformation detection on a deformation value between a present epoch and a previous epoch; (7) after finishing the detection of (6), using a robust least square static solution by superposition of two epoch observation equations as a result of the present epoch, or obtaining a result of the present epoch according to a robust sequential adjustment. The method for processing the GPS deformation monitoring data is accurate.

The invention belongs to the technical field of power system dispatching automation, particularly relates to a robust scheduling method considering uncertainty of wind power and load prediction, whichcomprises the following steps of: describing the correlation between input load fluctuation and a prediction error of wind power output by using a correlation coefficient matrix method, and converting random variables with correlation into mutually independent random variable matrixes by using a Cholesky decomposition method; Constructing a probability density model of prediction errors of wind power output and load fluctuation by adopting non-parameter kernel density estimation; Introducing the direct-current power flow model into a power system dispatching model, and establishing an objective function and a constraint condition under the condition of uncertain factors by taking the minimum total dispatching operation cost of the system as an objective function of the model; adopting Benders decomposition method to solve a UC main problem of a robust SCUC problem, a network security check sub-problem of the UC main problem under a basic scene, and a network security check sub-problemunder an uncertain scene of new energy power generation and load.

The invention is related to an equalizer comprising, for instance: means for creating a first covariance matrix and defining a Cholesky decomposition matrix of an inverse matrix of the first covariance matrix, means for removing selected covariance components from the Cholesky decomposition matrix, means for computing the inverse of a sub-matrix, which represents the common part of the first covariance matrix and a second covariance matrix, which includes covariance estimates of a second observation time, by using the aid of the Cholesky decomposition of the inverse matrix of the first covariance matrix, means for estimating interference from a received signal at a second observation time and determining additional covariance components on the basis of the estimation and means for creating the Cholesky decomposition of the inverse matrix of the second covariance matrix by using unitary rotations.

The invention discloses an SVM efficient spectrum sensing method based on a Cholesky decomposition sampling covariance matrix. The method is implemented by adopting the following steps: S1, a covariance matrix of a Cholesky decomposition sensing signal is used to construct a statistical quantity; S2, the statistical quantity is marked with a label; S3, the statistical quantity and the corresponding label are used as a training sample set, and samples are trained by adopting an SVM algorithm to obtain a convex quadratic programming problem; S4, the convex quadratic programming problem is solvedby adopting a sequence optimization algorithm to obtain an SVM classifier; and S5, states of main users are classified by adopting the SVM classifier. According to the method, through the preprocessing of the Cholesky decomposition sensing signal sampling covariance matrix, the correlation between a PU signal and a noise can be reduced, and the number of sample sets is reduced, so that the distance between the PU signals and the noise is increased, the detection probability is improved, and the spectrum sensing complexity is reduced.

The invention discloses a receiving device and method in TD-SCDMA syste, the invention gives out a data demodulating device and method when TD-SCDMA is in the same frequency net to realize effective receiving of area under interference of the same frequency, so requirement of TD-SCDMA can be fulfilled. It includes: data separator which separates data received training sequence part and data part; channel evaluator which evaluates channel swash response based on training sequence of area and training sequence separated by data separator; multi-area code-track activating evaluator which evaluates activating code-track; multi-area channel activating response processor which eliminates noise and evaluates power of noise; multi-area system matrix generator, multi-area matching filter, multi-area relative matrix generator, Cholesky decomposer and a matrix inversion machine; multi-area equation resolver.

The invention provides a day-ahead robust scheduling method of a power system based on a traditional Benders decomposition method, belongs to the field of power grid scheduling, and aims at solving the dynamic economical scheduling problem of the power system including new energy. According to the day-ahead robust scheduling method, the load, wind-power and photovoltaic output nondeterminacy and probability correlation are considered in an integrated way. An improved robustness optimized scheduling model considering multiple nondeterminacy factors and the probability correlation is constructed; a Cholesky decomposition method is used to convert a random sample with correlation into random samples independent from each other, and a worst scene is determined directly based on sample features; and the model is solved by utilizing the Benders decomposition method. A simulation result based on an IEEE-118 node example shows that under multiple nondeterminacy factors, robustness of a day-ahead scheduling plan is ensured, the economic performance is improved effectively, the worst scene determining method based on Cholesky decomposition can be used to improve the compactness of a robust scheduling model effectively, and the calculation efficiency is improved substantially.

The invention discloses a lithium-ion battery SoC (State-of-Charge) estimation method based on improved and self-adaptive dual unscented-Kalman-filters. The method mainly comprises the following steps: 1) establishing a state filter and a parameter filter of a lithium ion power battery based on an equivalent circuit model; 2) coupling the state filter and the parameter filter of the lithium ion power battery based on the equivalent circuit model, and building dual unscented-Kalman-filters; and 3) inputting operation parameters of a to-be-detected lithium ion power battery into the dual unscented-Kalman-filters, and carrying out parameter correction and stage-of-charge (SoC) estimation on a lithium ion power battery equivalent circuit model. According to the method in the invention, the effectiveness of cholesky decomposition is guaranteed, the problem that iteration is stopped due to the fact that a covariance matrix is indefinite caused by reasons such as an initial value error, noisedisturbance, calculation module floating point error and the like is overcome, and the numerical stability of the filtering process and the robustness of the algorithm are enhanced.

The invention discloses an MIMO-OFDM channel estimation method considering same frequency interference. The method comprises the following steps that 1), an observation sample of MIMO-OFDM pilot signals is utilized, an estimation criterion of the least square method is based, and then initial estimation of a channel impulse response is obtained; 2), the initial estimation of the channel impulse response is utilized, a residual error estimation method is based, and then an initial estimation value of an interference covariance matrix is obtained; 3), the initial estimation value of the interference covariance matrix is utilized, Cholesky decomposition and time domain 'low-pass filtering' are based, and then an improved estimation value of the interference covariance matrix is obtained; 4), the improved estimation value of the interference covariance matrix is utilized, a maximum likelihood estimation method is based, and then an improved estimation value of the channel impulse response and an improved estimation value of the channel frequency response are obtained. The MIMO-OFDM channel estimation method considering the same frequency interference is low in computation complexity and good in estimation error performance, and the performance gain is larger as the number of antennas is increased.

A multiuser detector method and receiver that detects and decodes synchronous or asynchronous CDMA subchannels having different spreading factors with reduced computational complexity. The multiuser receiver is compatible with ZF-BLE, MMSE, decorrelating detectors and the like using Cholesky decomposition to minimize numeric operations. The receiver and method arranges the columns of system transmission response matrices representing the response characteristics of individual users into a total system transmission response matrix which represents a plurality of matched-filter responses for a given block of received data. The invention when used in conjunction with Cholesky decomposition reduces the number of required mathematic operations prior to parallel matched filtering.

The invention relates to a FPGA (Field Programmable Gate Array) implementation device for solving least square problem based on Cholesky decomposition, which is suitable for solving the least square problem and settles the problem that computation efficiency of PC (Personal Computer) cannot satisfy real-time and embedded applications; the device comprises a to-be-solved matrix input interface module, a decomposing module and a solving module, wherein an output end of the to-be-solved matrix input interface module is connected to an input end of the decomposing module, and the output end of the decomposing module is connected to the input end of the solving module in order to satisfy real-time, low-consumption and embedded applications.

An algorithm for computing an efficient, reduced complexity, windowed optimal linear time domain equalizer for a dispersive channel comprises the steps of determining a window of maximum energy in the impulse response of length equal to or less than a number of cyclic prefix samples associated with a received digital data signal, computing the corresponding inside and outside matrices, performing an inverse Cholesky decomposition of the inside matrix, creating a resultant matrix as the product of the outer and the upper and lower square root inner matrix, followed by Householder reduction and QL transformation to thereby compute the time domain equalizer as the linear transformation of the eigenvector corresponding to the smallest eigenvalue at the receiver. The smallest eigenvalue is determined using the aforementioned orthogonal transformations without determining all the eigenvalues efficiently but without the loss accuracy associated with iterative methods like the conventional power method. The algorithm may be most conveniently implemented, for example, in the form of a thirty-two bit digital signal processor at a data receiver.

The invention belongs to the field of power grid scheduling, specifically a power system day-ahead robust scheduling method which takes multi-uncertainty and correlation into consideration. The methodis used for solving the dynamic economic scheduling problem of a power system comprising new energy. The day-ahead robust scheduling method which takes loads, wind power and photovoltaic contributionuncertainty and probability correlation into comprehensive consideration is provided. The method comprises the steps of establishing an improved robust optimization scheduling model which takes the multi-uncertainty and probability correlation into consideration; converting random samples with correlation into mutually independent random samples through utilization of a Cholesky decomposition method, thereby directly determining the worst scene based on sample features; and solving the model through utilization of a Benders decomposition method. According to the method provided by the invention, under multi-uncertainty factors, the day-ahead scheduling plane robustness is ensured; moreover, the economy is effectively improved; and according to a worst scene determination method based on Cholesky decomposition, the compactness of a robust scheduling model is effectively improved and the calculation efficiency is clearly improved.

A method and system for reconstructing a three-dimensional model of a product are provided. The method includes the steps of after acquiring a product image and calculating a homography matrix of a plane at infinity corresponding to the product image, obtaining a mapping matrix between pixel points of the product image and coordinate points of an actual scene by a Cholesky decomposition method; then establishing a network flow corresponding to the product, and calculating an optimal value of an energy function to obtain depth information of the product surface; and finally, according to the depth information and the mapping matrix, establishing a cube, dividing the cube into voxels, then updating the TSDF of each cube voxel, and performing rendering and projection to realize the reconstruction of a three-dimensional model. According to the invention, no calibration object is required, the robustness is high, the influence of imaging distortion is reduced, and the three-dimensional model has a high reliability and robustness.

The invention discloses an ambiguity of whole cycle rapid obtaining method based on a real-coding adaptive genetic algorithm. The method includes the following steps: S1, observing data through a carrier wave phase, and establishing a carrier wave phase double difference observation equation; S2, after linearization of the observation equation, utilizing Kalman filtering to obtain a floating pointsolution of ambiguity of a whole cycle and a corresponding covariance matrix; S3, taking a known baseline length as a constraint condition and determining the search range of the ambiguity of the whole cycle; S4, performing decorrelation on the floating point solution and the covariance matrix through an improved Cholesky decomposition method; and S5, determining a fitness function on the basis of a target function, determining all running parameters in an IAGA, conducting real coding on a problem, and finally searching an optimal solution of the ambiguity of the whole cycle through the IAGA.Corresponding crossover and mutation probabilities can be adjusted in a non-linear manner on the basis of individual fitness, a Hamming cliff problem can be solved by means of real coding, and the solving efficiency of the ambiguity of the whole cycle can be increased.

The invention discloses a method and a system for precoding a multi-user multi-input multi-output downlink. The method comprises the following steps of: calculating a block diagonalizable precoding matrix by using a channel matrix by a BS (Base Station) to eliminate MUI (Multi-User Interference); obtaining an equivalent channel matrix at the same time; eliminating the precoding matrix of ISI (Data Stream Interference) as a MMSE (Minimum Mean Square Error) precoding matrix aiming at an equivalent channel; carrying out Cholesky decomposition, and carrying out MMSE vector disturbance on a data symbol vector according to a decomposition result; carrying out the left multiplication of disturbed vector on the obtained matrix cascade; summing the obtained K results; then, transmitting the obtained K results from an antenna terminal; carrying out the module taking operation on the received signals by an MS (Mobile Station) terminal; and estimating initial data. On the basis of a precoding technology of block diagonalization and MMSE vector disturbance, the method and the system for precoding the multi-user multi-input multi-output downlink do not need a mobile station to estimate an equivalent channel or do not need the base station to transmit collaborative information, thereby greatly reducing system overhead and a receiver structure and eliminating the MUI interference while ensuring system performance.

The method is capable of carrying out transient state emulation for hybrid power supply network by mixing tree and network in any proportion quickly. Comparing with general emulation method, the invention can treat irregular topological structure and multiple power supply source, considering input of resistance, capacitance, and inductance as stray parameter so as to provide high precision of emulation. Pretreatment technique and quick argument elimination technique based on incomplete Cholesky decomposition is adopted in the invention. Advantages are: raising efficiency greatly, high precision of emulation and stability of data, emulation of network of power source / ground wires in large scale independent on topology. The invention is also applicable to passive clock line, bus and signal network in IC.

The invention discloses a method for fast simulating wind fields on basis of stability and homogeneity of a time-space field and condition interpolation. The method is used for carrying efficient, high-precision and high-speed simulation on the wind fields on the basis of stability and homogeneity of time-space field and condition interpolation. The method is characterized by comprising the following steps: establishing the relationship between a multipoint homogeneity and stability random process and a multipoint homogeneity and stability time-space random field, determining the transformation condition therebetween as well as the statistical and ergodic characteristics of a multipoint wind field sample which is simulated on the basis of the time-space field; and carrying out condition interpolation on the wind speed at the non-equidistant point. By adopting the method provided by the invention, Cholesky decomposition which is frequently used in the traditional method is not used, and the assumption that related functions in the simulation method need to obey the index distribution is improved. Moreover, a two-dimensional FFT technology can be used for greatly improving the simulation efficiency. Numerical example analysis shows that the method provided by the invention has the characteristics of being easy to use, relatively high accuracy and high in simulation efficiency, and is capable of effectively solving the problem that the spectral representation method is low in simulation efficiency when the random wind field simulation point number is large.

A method for optimizing a multiple input multiple output (MIMO) receiver for multiple antenna configurations is disclosed. A noise covariance is determined based on a noise estimate of a wireless signal. A Cholesky decomposition matrix is determined based on the noise covariance. A whitening matrix is determined based on the Cholesky decomposition matrix. The wireless signal is whitened using the whitening matrix.

The present invention discloses a digital pre-distortion circuit capable of compensating an analog circuit for its non-linear characteristic. An embodiment of said digital pre-distortion circuit comprises: a pre-distortion training circuit and a pre-distortion circuit. Said pre-distortion training circuit is operable to process a digital feedback signal according to a Cholesky Decomposition related algorithm and thereby generate a plurality of coefficients in which the digital feedback signal is derived from an output signal of the analog circuit and the output signal of the analog circuit is derived from an original digital signal; and said pre-distortion circuit is operable to process the original digital signal according to the plurality of coefficients under a compensation mode and thereby generate a digital pre-distortion signal whose non-linear characteristic is able to compensate the non-linear characteristic of the analog circuit.

The present invention provides systems and methods for decoding signal vectors in multiple-input multiple-output (MIMO) systems, where the receiver has received one or more signal vectors from the same transmitted vector. The symbols of the received signal vectors are combine, forming a combined received signal vector that may be treated as a single received signal vector. The combined signal vector is then decoded using a maximum- likelihood decoder. In some embodiments, the combined received signal vector may be processed prior to decoding. Systems and methods are also provided for computing soft information from a combined signal vector based on a decoding metric. Computationally intensive calculations can be extracted from the critical path and implemented in preprocessors and / or postprocessors. One embodiment considers the MIMO HARQ reception using Cholesky decomposition or factoruzation of the metric.

The invention discloses an efficient mesh fusion method based on a reusable Laplace matrix. The method includes: setting the correspondence between the boundary vertices of the source mesh model and the target mesh model, calculating the local transformation from the boundary vertices of the source mesh to the boundary vertices of the corresponding target mesh, using Laplace operator to calculatethe rotation field and the scale factor field, reusing Laplace matrix to reconstruct the geometric coordinates, and so on. In this method, the interpolation problems of geometric fusion, rotating field and scale factor field are transformed into Laplacian (Poisson) equations, and only one Cholesky decomposition and multiple iterations are needed to obtain eight scalar fields, which is two orders of magnitude faster than the traditional geodesic-based interpolation method. This method can not only obtain the results comparable to the traditional Poisson fusion method, but also significantly improve the efficiency and achieve the speed of real-time response.

The invention discloses a method and a device for processing digital pre-distortion. The method comprises the following steps: according to output signals of a radio-frequency power amplifier and a pre-distortion coefficient intermediate transformation matrix acquired by calculation, acquiring elements of previous M rows of a pre-distortion coefficient second matrix in turn, initializing a pre-distortion coefficient third matrix, and acquiring elements of previous K rows above a diagonal in the pre-distortion coefficient third matrix according to the elements of the previous M rows of the pre-distortion coefficient second matrix; acquiring elements of other rows except the previous K rows above the diagonal in the pre-distortion coefficient third matrix according to a second property formula, and acquiring the elements under the diagonal in the pre-distortion coefficient third matrix according to a first property formula; and carrying out Cholesky decomposition for the pre-distortion coefficient third matrix to acquire a pre-distortion coefficient matrix, and carrying out pre-distortion processing for input signals. The method and the device greatly reduce the computing times, complexity and required storage space of the pre-distortion coefficient third matrix, save computing resources, and improve the computing efficiency.

The embodiment of the invention discloses a signal interference filtering method and a related device. The device comprises a searching module, a correcting module and a filtering module. The searching module is used for carrying out parameter estimation in a selected area in the time-frequency domain to obtain estimation parameters, and the estimation parameter serve as an index for searching for compensation factors from a preset compensation factor lookup table, wherein the compensation factor lookup table comprises the various preset estimation parameters and the corresponding compensation factors. The correcting module is used for calculating an autocorrelation matrix corresponding to interference data in the selected area, and correcting the diagonal element of the autocorrelation matrix on the basis of the compensation factors. The filtering module is used for carrying out Cholesky decomposition on the corrected autocorrelation matrix to obtain a whitening matrix, and carrying out interference filtering on received signals in the selected area through the whitening matrix. By adopting the signal interference filtering method and the related device, interference on the signals in the selected area is effectively eliminated, the signal detection performance is improved, and operation is simple.

The invention relates to a method for calibrating a parabolic refraction and reflection camera through the Veronese mapping and a checkerboard. The method is characterized in that the target of the method is formed by a known checkerboard in the space. The method comprises the specific steps of during the solving process of the internal parameters of a parabolic refraction and reflection camera, shooting 3 images by the parabolic refraction and reflection camera; obtaining three homography matrixes through the Veronese mapping so as to solve a vanishing point for any straight line; solving another vanishing point based on the relationship between antipodal points and harmonic conjugates, wherein the connecting line of the two vanishing points is a vanishing line; according to the intersection points of the vanishing line with the image of the straight line namely a quadratic curve, solving the image of three sets of circular points; based on the constraint of the image of circular points on the image of the absolute quadratic curve, solving the internal parameters of the parabolic refraction and reflection camera through the Cholesky decomposition.

The invention belongs to the field of random signal simulation, in particular to an efficient non-stationary random process simulation method. The simulation method is a spectral representation simulation method adopting fast fourier transform (FFT) based on proper orthogonal decomposition (POD) and comprises the following steps: firstly performing Cholesky decomposition on an evolutionary power spectrum matrix, then representing a decomposed time-varying spectrum as the sum of products of a plurality of time functions and frequency functions by adapting the proper orthogonal decomposition, decoupling a time-varying power spectrum and finally greatly improving the simulation efficiency by utilizing a FFT technology. When the efficient non-stationary random process simulation method is adopted for stimulating a non-stationary random process, the POD precision and the reconstruction precision are high, and the goodness of fit of a statistical correlation function for simulating a sample duration with a target correlation function is high; by the use of the FFT, the simulation speed of a sample is obviously improved; the efficient non-stationary random process simulation method effectively solves the problem of low calculation efficiency caused by the fact that a traditional non-stationary random process simulation method based on spectral representation cannot use the FFT and has the advantages of high efficiency, high precision and high easiness in use.

The invention discloses a fast simulation method for a non-stationary random process. The fast simulation method includes the steps: firstly, establishing the relationship between the multi-point non-stationary random process and a one-dimensional non-stationary random wave, namely, acquiring the two-dimensional evolutionary power spectrum density of a transformation random wave according to the evolutionary power spectrum and the coherence function of a given target random process; secondly, decoupling 2D EPSD by a proper orthogonal decomposition method; finally, simulating the transformation random wave by two-dimensional FFT (fast Fourier transform) to obtain the corresponding target non-stationary random process. Cholesky decomposition is omitted, decoupling time based on POD (proper orthogonal decomposition) is shortened, and the two-dimensional FFT technology is used, so that simulation efficiency is greatly improved. In addition, when parameters are properly selected, precision is still is high. In conclusion, the fast simulation method has the advantages of easiness in use and high precision and simulation efficiency, and can effectively solve the problem that the simulation efficiency of a spectral representation method is low when non-stationary random process simulation points are large.