314 results about "Matrix estimation" patented technology

A wireless receiver (30₁) for receiving multiple space time encoded signals from a plurality of transmit antenna sets (TAT₁ through TAT₂, and TAT₃ through TAT₄), wherein the multiple space time encoded signals comprise a set of symbols and wherein each transmit antenna set is coupled to a corresponding encoder (22₁, 22₂) at a single transmitter (12₁). The receiver comprises a plurality of receive antennas (RAT₁ through RAT_Q) and collection circuitry (32), coupled to the plurality of receive antennas, for collecting a plurality of signal samples for a plurality of successive time instances and from each of the plurality of receive antennas. The collected samples comprise samples of multipaths of the space time encoded signals. The receiver also comprises circuitry (34, 36), coupled to the plurality of receive antennas, for determining a linear time invariant multiple-input multiple-output matrix in response to pilot values in the received multiple space time encoded signals. Finally, the receiver comprise circuitry (40, 42₁, 44₁) for estimating at least selected ones of the symbols in response to the signal samples and the linear time invariant multiple-input multiple-output matrix.

A wireless communication system which performs data transmission using spatially multiplexed streams from a first terminal including N antennas to a second terminal including M antennas (N is an integer of 2 or more and M is an integer of 1 or more) is disclosed. The system includes notifying means, training means, channel matrix estimation means, beamforming information feedback means, transmission weight matrix setting means, and beamforming means.

A method and system for calculating data traffic flow in a communications network are disclosed. The communications network comprises a plurality of nodes including a plurality of source nodes, a plurality of destination nodes, and a plurality of intermediate nodes. Each of the intermediate nodes includes at least one elbow comprising one ingress interface and one egress interface of the intermediate node. The method includes obtaining local data traffic measurements at each of the elbows, wherein the local data traffic measurements comprise data traffic arriving at the intermediate node via the ingress interface and leaving the intermediate node via the egress interface. The local data traffic measurements are used in calculation of the traffic flow and may be used, for example, to generate data traffic matrix information using data traffic matrix inference or data traffic matrix estimation.

Channel estimation apparatus and method for interference cancellation in a mobile communication system are provided. The channel estimation method includes detecting a preamble from a received signal and estimating a primary channel using the detected preamble; calculating a short-term correlation matrix using the primary channel; and estimating a secondary channel using the calculated short-term correlation matrix according to a certain channel estimation scheme.

A method and apparatus derives an impairment correlation matrix to process signals received at a wireless receiver over multiple paths of a multi-path channel. The receiver includes first and second impairment correlation estimators for estimating first and second impairment correlation matrices based on despread symbols received over multiple paths of a multi-path channel. The receiver then derives the impairment correlation matrix based on the estimated first and second impairment correlation matrices. The receiver may combine traffic despread values to suppress interference using weighting factors calculated based on the derived impairment correlation matrix. Further, the receiver may estimate a signal-to-interference ratio based on the derived impairment correlation matrix.

The invention discloses a large-scale MIMO channel state information feedback method based on deep learning. The method comprises the following steps: firstly, carrying out two-dimensional discrete Fourier transform (DFT) on a channel matrix H-wave of MIMO channel state information in a spatial frequency domain on a user side, so that a channel matrix H which is sparse in an angle delay domain isobtained; secondly, constructing a model CsiNet comprising a coder and a decoder, wherein the coder belongs to the user side and is used for coding the channel matrix H into codons with a lower dimension, and the decoder belongs to a base station side and is used for reconstructing an original channel matrix estimation value H-arrow from the codons; thirdly, training the model CsiNet to obtain model parameters; fourthly, carrying out two-dimensional inverse DFT on a reconstructed channel matrix H-arrow which is output by the CsiNet, so that a reconstructed value of the original channel matrixH-wave in the spatial frequency domain is recovered; and finally, using the trained model CsiNet for compressed sensing and reconstruction of channel information. The method provided by the inventionhas the advantages that large-scale MIMO channel state information feedback expenditures can be reduced, and an extremely high channel reconstruction quality and an extremely high channel reconstruction speed can be achieved.

The inventors have envisioned a multihop network scenario in which nodes are equipped with advanced multi-antenna arrangements, and recognized the advantage of exploring the presence of such advanced antenna arrangements in multihop network nodes for the specific purpose of determining link cost for routing in the network. A basic idea of the invention is therefore to determine link cost for a wireless link between a pair of nodes in the network based on multi-channel characteristics between the nodes, where at least one of the nodes is configured for operation with multiple antennas to provide for multiple channels. These multi-channel characteristics may for example be determined based on explicit channel matrix estimation and/or the number of transmit and receive antennas or other information on the antenna capabilities of the involved nodes. The determined link cost information may subsequently be used together with additional routing cost information for route determination, and packet forwarding.

A communication node relays a transmission signal transmitted from a desired source node to a target destination node among multiple source nodes and multiple destination nodes. The communication node includes a first unitary matrix estimation unit that estimates a first unitary matrix by performing singular value decomposition involving one or more channel matrices between the relay node and the source nodes other than the desired source node; a second unitary matrix estimation unit that estimates a second unitary matrix by performing singular value decomposition involving one or more channel matrices between the relay node and the destination nodes other than the target destination node; and a transmission unit configured to transmit a relaying signal generated by multiplying a received signal by the first and second unitary matrices toward the target destination node.

The invention discloses a self-adaptive tracking loop, which comprises an unscented Kalman filter (UKF), an observation noise variance matrix detection module, a fuzzy inference system, an unscented transformation (UT) scale factor regulation module, a state compensator, a carrier wave numerical controlled oscillator (NCO), scale factors, a code NCO, an integration and zero-clearing module, a code loop phase discriminator and a second order code loop filter, and additionally discloses an implementation method for the self-adaptive tracking loop. The implementation method comprises a step 1 ofsignal correlation, integration and zero clearing; a step 2 of code phase tracking; a step 3 of UKF modeling; a step 4 of observation noise variance matrix estimation; a step 5 of process noise variance matrix estimation; a step 6 of UT scale factor regulation; a step 7 of state estimation deviation compensation; and a step 8 of assistance of the carrier wave NCO in the code NCO. According to theself-adaptive tracking loop, the UKF, the observation noise variance matrix detection module and the fuzzy inference system are designed in the carrier tracking loop, so not only can a contradiction between thermal noise vibration in the tracking loop and a dynamic stress error be solved, but a process noise variance matrix and an observation noise variance matrix can be regulated in a self-adaptive manner according to changes of the external environment, and thereby the self-adaptive ability of the tracking loop under complex changeable environments of high dynamic, strong interference, and the like is effectively improved.

A receiving apparatus is provided in which a single carrier signal is received by a plurality of receiving antennas, and multipath equalization and other cell interference suppression are carried out in a frequency domain at a same time. A plurality of antennas 1-1 to 1-N receives the single carrier signal. The DFT sections 3-1 to 3-N converts the reception signals into frequency domain signals. A channel estimating section 5 estimates a channel gain of a desired user signal by using pilot reception signals. An interference correlation matrix estimating section 6 estimates an interference correlation matrix from the pilot reception signals and a channel estimation value. A weight, calculating section. A weight calculating section 7 receives the channel estimates and the interference correlation matrix and calculates equalization weights. An equalising section 8 performs multipath equalization and other cell interference suppression to the desired user signal in a frequency domain. An IDFT section 9 converts an equalization signal into a signal in a time domain.

The invention relates to a method for predicting suburban rail transit passenger flow, which comprises the following steps: (1) constructing a prediction model basic data base through OD (Origin and Destination) matrix estimation and station attraction; (2) conducting the distribution network of the rail transit passenger flow according to the functional localization of suburban rail transit and by combining an operation connection mode; (3) conducting a prediction model respectively aiming at the operation connection mode between the suburban rail transit and the urban rail transit; (4) distributing suburban rail transit passenger flow on a corresponding rail transit distribution network by combining the prediction model and the basal data so as to obtain the whole line passenger flow, the station passenger flow, the branch passenger flow and the transferring passenger flow of the suburban rail transit; (5) and recommending a best operation planning scheme by comparing the passenger flow prediction results of different operation organization schemes, and performing the passenger flow prediction of a final scheme according to the recommended operation planning scheme. The invention overcomes the defect in the prior art that the prediction results are unsatisfactory and have lager difference with the ex-post real data, and further improves the accuracy of predicting.

The invention discloses an artery coordination signal control method based on dynamic O-D matrix estimation. According to the method, road segment flow of the detecting of import approaches and export approaches of crossings of an artery is adopted, dynamic O-D matrixes of crossings are estimated by the adoption of a Kalman filtering and a back propagation neural network algorithm, a Bayes combination method is designed to improve accuracy and stability of the estimated result, a single-crossing multi-target signal control model is built on the basis of the estimated result, and the calculated maximum of crossing signal cycles is served as a public cycle of the artery. The artery coordination signal control method that the maximum of the obstructing-free rate of artery vehicles serves as the target function is further designed, the green split of all crossing artery directions and the phase difference of the adjacent crossings are acquired through solving, and an artery coordination signal control scheme is formed. The artery coordination signal control method based on dynamic O-D matrix estimation has the advantages that on the premise of guaranteeing the optimizing passing of the artery vehicles, the passing efficiency of all the single crossings is balanced, the problem that the control scheme can not be adjusted timely according to the traffic flow in the prior art is solved, advantages of being high in accuracy, online in application and the like are achieved.

The invention provides a working modal identification method based on time-frequency domain single-source-point sparse component analysis. The working modal identification method specifically includes the following steps that vibration signals of an equipment target position under the working state are obtained through measurement; time-frequency domain conversion is conducted on the mixed vibration signals; a single-source-point method is used for extracting the mixed vibration signals used for estimating a hybrid matrix in a time-frequency domain; a hybrid matrix estimation method based on K hyperline clustering sparse component analysis is used for estimating the hybrid matrix; after the hybrid matrix is solved, the time-frequency domain is returned, the l1 minimization method is used for reconstructing each order of source signals, and modal vectors of a structure are extracted; then the working modal frequency and the damping ratio are obtained through signal index expression. According to the method, the calculation amount in the hybrid matrix estimation process is reduced, under the poor condition that the number of measuring points is less than that of the source signals, modal parameters are identified effectively, and the method has good anti-interference capacity for incomplete sparsity of noise, abnormal values and the source signals.

The invention discloses a self-adaptive channel estimation method based on compressed sensing and large-scale MIMO and belongs to the technical field of wireless communication. The self-adaptive channel estimation method includes the steps that decomposition values Ur and Ut of a channel matrix in an angle domain are obtained, and a corresponding measurement matrix phi and a corresponding perceptual measurement value Y are calculated; iterative computation is conducted on a shared channel parameter estimation value (shown in the description) based on an index set Gamma n-1 at a previous moment of a system, the measurement matrix phi and the perceptual measurement value Y; a sparse signal estimation value (shown in the description) is calculated through iteration of the shared channel parameter estimation value (shown in the description), the phi and the Y; finally, a channel matrix estimation value (shown in the description) is obtained according to a formula (shown in the description) based on the number M of transmitting antennas, the signal-to-noise ratio P of the transmitting antennas and the pilot frequency length T of the transmitting antennas. The self-adaptive channel estimation method does not need knowing shared channel information for channel estimation, utilizes the index set at the previous moment in a self-adaptive mode and produces smaller errors compared with a traditional subspace tracking algorithm.

A wireless communication apparatus for receiving spatially multiplexed signals in which a plurality of streams are spatially multiplexed, using a plurality of antennas, includes the following elements: a channel matrix estimator for estimating a channel matrix for multiplexed channels; a spatial decoder for spatially decoding received signals each of which has been received by a corresponding antenna so as to separate the received signals into individual stream signals by obtaining an antenna weight matrix from the estimated channel matrix, and then multiplying each received signal by the antenna weight matrix; a likelihood information estimator for estimating likelihood information using the estimated channel matrix, the antenna weight matrix, and estimated noise power; and a soft decision decoder for performing soft decision decoding on each stream signal on the basis of the estimated likelihood information.

The mosaicing curling effect which causes a mosaic strip to curl is effectively mitigated in a system for generating a mosaic image from sequential images which partially overlap with one another. The system has a matrix estimating unit, an adjustment element calculating unit, and an image projecting unit. The matrix estimating unit estimates a transformation matrix for transformation between two adjacent images taken out from among the sequential images. The adjustment element calculating unit calculates an adjustment element of the estimated transformation matrix by decomposing the transformation matrix into a rotational component and a perspective component. The image projecting unit projects each image on the mosaic plane using the transformation matrix to which the calculated adjustment element has been applied.

The invention discloses a video segmentation method based on depth recovery and motion estimation. The video segmentation method based on the depth recovery and motion estimation comprises the following steps of: (1) working out a background subtraction measure by using homography matrix estimation or using a camera motion and dense depth map recovered by a video sequence consistent depth recovery method; (2) performing dense motion estimation, and estimating dense motion fields d and occlusion maps o of continuous two frames of images; (3) calculating a video segmentation result according to an interactively generated combination strategy of multiple measures; and (4) repeating the step (3) for at least two times, and then, ending. Firstly, according to the video segmentation method based on the depth recovery and motion estimation disclosed by the invention, videos can be segmented by iterative optimization of motion, depth and segmentation information. Secondly, according to the video segmentation method based on the depth recovery and motion estimation disclosed by the invention videos of which the backgrounds do a planar motion can be segmented without estimating camera parameters and the depth information. Finally, the video segmentation method based on the depth recovery and motion estimation disclosed by the invention is a video segmentation method of combining multiple measures, the accuracy of various measures can be measured, and reliable measures are screened out to involve in video segmentation calculation.

The present invention discloses a method for calibrating a PTZ camera by using only two scene images. Firstly, two images with overlapped areas of any scene in a same focal length and different Pan-Tilt angles relative to the PTZ camera are photographed, the two images are subjected to feature point matching, a matching feature point pair between the two images is obtained, the influences of the uncertainty of matching feature point positioning and camera lens distortion are considered, a homography matrix estimation method with the combination of feature point positioning noise and lens distortion is created, the homography matrix between the two images and the radial distortion coefficient of the PTZ camera under the focal length are obtained, finally a Givens rotation matrix is constructed to decompose the obtained homography matrix, and thus the four internal references (focal length, principal point coordinate, and aspect ratio) of the camera is resolved and obtained. According to the method, the online calibration of five parameters of the PTZ camera can be realized.

The invention provides a detection method of an interference target for space-time adaptive processing, which solves the problem that the performance is influenced by the interference target when the traditional non-uniform detector NHD (nonhmogeneity detection) is used for removing non-uniform training samples. The detection method comprises the steps as follows: step I, estimation of a clutter covariance matrix based on an elliptical long ball wave function: firstly receiving a space-time clutter data model, then determining the relationship of the elliptical long ball wave function and clutter data, and finally estimating the clutter covariance matrix based on the elliptical long ball wave function; step II, calculating the detection statistic of the interference target of GIP (generalized inner products); and step III, removing the non-uniform training samples influenced by the interference target according to a set threshold value.

A multi-input multi-output (MIMO) radio communication apparatus and method are provided. The MIMO apparatus includes a plurality of reception antennas each having a plurality of antenna patterns; a channel matrix estimation unit for estimating a channel matrix between a plurality of transmission antennas and the plurality of reception antennas; a channel capacity calculation unit for calculating a channel capacity corresponding to a combination of antenna patterns of the plurality of reception antennas by using the estimated channel matrix; and an antenna pattern control unit for changing the antenna patterns of the plurality of reception antennas to maximize the channel capacity. According to the MIMO radio communication apparatus and method, direction can be controlled adaptively to a propagation environment.

The SATME method and system estimates source-to-destination traffic matrices using a simulated annealing algorithm, the traffic matrix estimation being represented as a probability distribution over the set of all possible matrices that satisfy a set of given constraints. The constraints explicitly encode information that the user knows about the network traffic as components of an objective function (a fitness function), that is then minimized using simulated annealing. With the method according to the invention, arbitrary constraints of any form can be included, and the case where there are no feasible solutions can be diagnosed by the objective function not converging to zero.

The invention discloses a wind turbine generator gearbox fault diagnosis method based on vibration signal blind source separation and sparse component analysis. The wind turbine generator gearbox fault diagnosis method mainly comprises three algorithms, which are an empirical mode decomposition (EMD), singular value decomposition (SVD) and K-means clustering (K-Means) based source signal number estimation algorithm, a fuzzy C-means clustering (FCM) based aliasing matrix estimation method, and a minimized l1-norm based source signal estimation and diagnosis algorithm. An idea of blind source separation (BSS) and sparse component analysis (SCA) is introduced into the whole algorithm process. The wind turbine generation gearbox fault diagnosis method takes simulated signals and actual vibration signals as a test object, detailed algorithm description is provided, and the effectiveness of the algorithms in the aspects of signal processing and fault analysis is verified through a series of experiments.

A communication apparatus, method, and system are provided. The communication apparatus receives through a transmission path a combined signal in which modulated signals are combined. The communication apparatus converts a channel matrix indicating transmission characteristics of the transmission path using a basis conversion matrix which converts column vectors forming the channel matrix to cross at right angles, estimates a transmission symbol using the converted channel matrix, and calculates a likelihood of each bit of the estimated transmission symbol being a “1” and a “0”, respectively, using an inverse matrix of the basis conversion matrix. The method includes converting a channel matrix; estimating a transmission symbol; and calculating a likelihood of each bit being a “1” and a “0”, respectively, of the estimated transmission symbol using a basis conversion matrix. The system includes a transmitter and receiver for transmitting and receiving, respectively, a combined signal according to the method.