718 results about "Observation matrix" patented technology

The invention provides a compressed sensing theory-based reconstruction method of a magnetic resonance random sampled K space data image. The reconstruction method applies a contourlet conversion and iterative soft thresholding method to realize reconstruction of a magnetic resonance image. The method comprises the following steps: collecting K space data in a magnetic resonance image scanner according to a preset observation matrix phi to generate a measurement value, and keeping y; acquiring y from a coil of the magnetic resonance image scanner, and transmitting y to a computer; and finallyconstructing a same phi, constructing any orthogonal transformation psi, and recovering from y by adopting a compressed sensing theory-based magnetic resonance random sampled K space data image reconstruction method according to reconstruction. According to the method, scanning time is saved, quick imaging is realized, high-quality reliable image information is provided to medical nuclear magnetic resonance imaging detection, and solid theoretical and practical foundation is established for further development and large-scale popularization and application of the medical imaging detection technology.

A method for sparse channel estimation in MIMO OFDM systems with a plurality of subchannels having the same sparsity structure is presented. The inventive method comprises initializing a plurality of residual vectors and observation generating matrices modeling the channel, sending a pilot signal for each subcarrier, converting the pilot signals to tap positions, detecting an optimal tap position, updating the residual vectors by removing the one residual vector having the optimal tap position, updating the generating matrices in accordance with the optimal residual vector, calculating weighted residuals based on the updated residual vectors, and repeating the steps, except initializing, until a stopping condition is met, wherein the updated observation matrices estimate the sparse channel. In one embodiment, the observation generating matrices are omitted. In one embodiment, multiple vectors are removed during one iteration. Pilot placement and pilot allocation techniques are presented to optimize the method.

This invention is an ultra-low frequency electromagnetic telemetry receiver which fuses multiple input receive sources to synthesize a decodable message packet from a noise corrupted telemetry message string. Each block of telemetry data to be sent to the surface receiver from a borehole tool is digitally encoded into a data packet prior to transmission. The data packet is modulated onto the ULF EM carrier wave and transmitted from the borehole to the surface and then are simultaneously detected by multiple receive sensors disbursed within the rig environment. The receive sensors include, but are not limited to, electric field and magnetic field sensors. The spacing of the surface receive elements is such that noise generators are unequally coupled to each receive element due to proximity and/or noise generator type (i.e. electric or magnetic field generators). The receiver utilizes a suite of decision metrics to reconstruct the original, non noise-corrupted data packet from the observation matrix via the estimation of individual data frames. The receiver will continue this estimation process until: 1) the message validates, or 2) a preset “confidence threshold” is reached whereby frames within the observation matrix are no longer “trusted”.

The invention discloses a new method for RAIM (receiver autonomous integrity monitoring) based on a satellite selecting algorithm in a multimode satellite navigation system. The method comprises the steps of first determining space position information of satellites according to a navigation message and eliminating satellites with a small elevation angle according to a shielding angle; determining an observation matrix including only one clock correction item according to clock correction conversion factors in the navigation message; selecting p satellites from N visible satellites so as to be used for positioning calculation of a receiver, acquiring a satellite combination, which enables the GDOP (geometric dilution of precision) to be minimum, through the satellite selecting algorithm to act as calculating satellites, and determining a weight matrix in WLS (weighted least squares) according to parameters such as the carrier-to-noise ratio, the loop bandwidth, pre-check integral time and the like of satellite signals; carrying out RAIM availability detection according to a false alarm rate and a missed alarm rate which are preset by the receiver, and calculating a pseudo-range residual error threshold value after positioning according to the false alarm rate and a degree of freedom in Chi-squared distribution; carrying out global detection at first, then carrying out local monitoring in a circumstance that a fault satellite exists, determining calculation satellites again through satellite selection, and finally carrying out positioning calculation through selecting satellite combinations within the threshold value. The method disclosed by the invention is simple, high in fault recognition rate, not only applicable to multi-mode and multi-fault satellite navigation systems, but also applicable to single-mode and multi-fault satellite navigation systems, thereby providing new ideas for carrying out RAIM by a modern GNSS (global navigation satellite system).

A method generates a synthetic textured data signal by first acquiring a time-invariant input textured data signal. The input textured data signal is sampled to construct an observation matrix. The observation matrix is eigen-coding and factoring to identify a linear dynamic system modeling the input textured data signal. Then, the linear dynamic system can be run forward from an initial state using a quadratic regulator and a random noise signal to generate the synthetic textured data signal.

The invention discloses a method for reconstructing partitioned images by compressive sensing on the basis of structural dictionaries, aiming to overcome the defects that the effect of reconstructing the partitioned images by compressive sensing is not ideal in the prior art because the image blocks with different structures can not be expressed most sparsely on the basis of the signal dictionaries. The method comprises the following steps: (1) constructing a training sample library; (2) classifying image blocks; (3) training the structural dictionaries; (4) constructing an observation matrix; (5) observing the image blocks; (6) reconstructing the structural dictionaries; (7) acquiring reconstruction errors; (8) reconstructing images; and (9) outputting the reconstructed images. The images are reconstructed on the basis of the image blocks under all the structural dictionaries by adopting the error weighting and summing method, and the reconstruction quality is improved.

The invention discloses a multi-spectral and panchromatic image super-resolution fusion method based on compressive sensing, which comprises the following steps: 1, respectively inputting a low-resolution multi-spectral image and a high-resolution panchromatic image; 2, obtaining a high-resolution joint sampling value matrix; 3, obtaining a high-low observation matrix; 4, obtaining a high-high observation matrix; 5, obtaining a high-resolution joint observation matrix; 6, studying a high-resolution over-complete dictionary; 7, obtaining a high-resolution rarefaction coefficient; and 8, reconstructing a high-resolution multi-spectral image. According to the method provided by the invention, the compression sensing technology is introduced into the fusion field of the multi-spectral images, the defects that the color is distorted and the spectrum is contorted in the prior art are solved, and the resolution of the fused multi-spectral image is about one time of that of the high-resolution panchromatic image.

The invention provides a stereoscopic display method, a stereoscopic display device and stereoscopic display electronic equipment used for virtual and reality scenes. The stereoscopic display method comprises the steps of: acquiring real-time tracking data of a head position of a user; converting an original observation matrix or an original projection matrix of a virtual scene according to the real-time tracking data when the head position of the user changes, and acquiring a new observation matrix or a new projection matrix; and constructing and displaying a stereoscopic image of the virtual scene according to the new observation matrix or the new projection matrix, thereby converting an observation viewing angle of the virtual scene, and achieving synchronization of the observation viewing angle in the virtual scene and the observation viewing angle after the change of the head position of the user. The stereoscopic display method, the stereoscopic display device and the stereoscopic display electronic equipment can be applied to 3D display equipment.

The invention discloses an intra prediction method, which comprises the following steps of: (1) defining N templates in different directions corresponding to N novel intra prediction modes, wherein N is an arbitrary value between 1 and 9, and searching m image blocks which are most similar to the current image blocks to be predicted in a coded image region in a template matching mode; (2) rearranging the most similar m image blocks which are searched in each template to construct a low-rank observation matrix; (3) performing low-rank matrix filling on observation matrixes obtained in N different prediction modes, and reconstructing unknown element values in the observation matrixes to obtain predicted values of the N current image blocks; (4) performing intra prediction on the current image blocks to be predicted by a standard intra prediction method in H.264; and (5) selecting an optimized predicted value from the predicted values obtained in the steps (3) and (4) through rate-distortion optimization, namely selecting an optimized predicted value from (N+1) predicted values. By the method, spatial correlation in an image can be more effectively utilized.

The invention discloses a voice secret communication system design method based on compressive sensing and information hiding, comprising the following steps: embedding secret voice into carrier voice by an embedded system to obtain mixed voice; designing a compressive sensing overcomplete dictionary aiming at the voice signal; sampling the secret voice by a compressive sensing self-adaption observation matrix to obtain a observation vector for reducing dimensions; quantizing the observation vector by an LBG (Linde-Buzo-Gray algorithm) vector, taking the quantized observation vector to serve as secret information to embed into the carrier voice, and carrying out two-stage transform on the carrier voice to obtain mixed voice; extracting the secret voice from the mixed voice by an extraction system; carrying out discrete cosine transform on mixed voice, and improving wavelet transform two-stage transform to obtain a wavelet transform coefficient; obtaining a secret bit stream by a scalar Costa decoding algorithm; obtaining a reconstructing observation vector by an LBG vector quantization decoder; reconstructing the secret voice by a compressive sensing orthogonal matching pursuit algorithm; and improving the quality of the reconstructed secret voice with a wavelet denoising method.

The invention relates to a rendering method and a rendering device for large-scale three-dimensional animations. The rendering method includes steps of 1), setting attributes of a current camera and obtaining a world matrix, an observation matrix and a projection matrix of a three-dimensional animation by changing coordinates; 2), cutting and testing by a viewing pyramid eliminating algorithm, a back face eliminating algorithm and an occlusion eliminating algorithm; 3), rendering characters, terrains and sky; and 4), rendering a user interface. The rendering device comprises a central processing unit, an image processor and a memory module, and the memory module is respectively connected with the central processing unit and the image processor. Compared with the prior art, the rendering method and the rendering device have the advantages that rendering efficiency is high, the large-scale three-dimensional animation can be rendered, and the like.

The invention discloses a radar target parameter estimation method based on AIC (automatic information center) compression information acquisition and FBMP (fast Bayesian matching pursuit), which mainly solves the problem that the existing compression sensing radar target parameter estimation method cannot simultaneously improve estimation precision and reduce time cost. The method comprises the implementation steps that low-dimension compression observation of radar echo signals is realized by AIC; a time shift sparse dictionary is designed on the basis of transmitted signals, so the radar echo signals can obtain sparse presentation on the time shift sparse dictionary; observation matrices needed in a compression sensing reconstruction theory are constructed according to AIC sampling sequences and the time shift sparse dictionary; sparse coefficient vectors of the radar echo signals are solved by a fast Bayesian matching pursuit FBMP algorithm so as to realize radar target parameterestimation. The invention has advantages that the number of non-zero coefficients in the sparse coefficient vectors of signals to be reconstructed is determined adaptively, the reconstruction precision can be improved when the time cost is reduced, and the method can be used for radar target recognition and radar imaging.

The invention discloses a sparsity self-adaptive OFDM (Orthogonal Frequency Division Multiplexing) system channel estimation method and belongs to the technical field of mobile communication. The method comprises the following steps of 1, obtaining channel response at a pilot frequency, 2, establishing a channel model, 3, constructing an observation matrix and an observation value both needed by signal reconstruction, and 4, recovering channel impulse response according to a reconstructing algorithm. The method achieves the goal of high channel estimation performance even though a prior condition that the channel sparsity is known is not satisfied, and therefore, a simple and efficient solution is provided for sparse channel estimation; the method is capable of reducing the number of the pilot frequencies, improving the spectrum efficiency and effectively reducing the error of channel estimation.

A moving image encoding and decoding system reduces compressed moving picture data amounts by producing a prediction signal suitable for moving pictures including dynamic textures. The system includes a moving picture encoding device 1 that includes a block divider 102 receiving a target pixel signal, a prediction signal generator 103 producing a prediction signal, a subtractor 105 producing a differential signal, and a transformer 106 and quantizer 107 producing an encoded differential signal. The encoding device 1 also includes an inverse quantizer 108 and inverse transformer 109 producing a decoded differential signal, an adder 110 producing a reproduction signal, a frame memory 104 storing the reproduction signal as a reference image, and a reference image generator 113. The reference image generator 113 may obtain an observation matrix C by using a pre-existing reference image. The encoding system 1 produces a new reference image y″_t+5 based on the observation matrix C and a state vector x_t+5, and stores the new reference image in the frame memory 104. The prediction signal generator 103 produces the prediction signal by using at least the new reference image y″_t+5.

The invention relates to a FiberBragg grating(FBG) signal self-adapting restoration method based on compressed sensing, and belongs to a signal restoration technology field of an optical fiber sensing system. The FBG signal self-adapting restoration method comprises steps that step 1: EMD combination mutual information is used for self-adapting denoising processing of spectral signals; step 2, segmented testing of a denoising signal is carried out, and the signal is divided into k segments, and sample databases corresponding to the signals are acquired by calculating Euclidean distances among various segments of signals and samples, and self-adapting dictionaries D corresponding to the signals are acquired by adopting a K-SVD dictionary learning method; step 3, measured signals are used to acquire observation matrixes R and observation signals xi; step 4, the observation signals are reconstructed by adopting an improved regularized orthogonal matching pursuit algorithm to acquire complete reconstructed signals. The FBG signal self-adapting restoration method is advantageous in that problems such as interferences of noises on the signals, targeted dictionary learning, and the signal self-adapting reconstruction are considered, and each part represents the self-adaptability of the algorithm, and can be flexibly used in practical engineering, and then influences caused by manual misoperation are reduced.

The invention discloses a six-axes robot kinetic parameter identification method based on a neural network. The six-axes robot kinetic parameter identification method comprises the following steps that firstly, robot kinetic modeling and linearization are conducted; secondly, motivation trajectory optimization is conducted, and specifically a motivation trajectory is optimized through an artificial immune algorithm; thirdly, experiment sampling is conducted, specifically a robot moves along the motivation trajectory, and multiple sets of observation matrices and joint torque are obtained as experiment data; fourthly, data processing is conducted, the data collected in an experiment are preprocessed through a three standard deviation norm and a median average filter method, and the influence brought by data noise is lowered; fifthly, kinetic parameter estimation is conducted, and kinetic parameters are estimated through the neural network; and sixthly, parameter verification is conducted, the robot follows an executable trajectory different from the motivation trajectory, experiment data are sampled again, theoretical joint torque is predicted according to kinetic parameters obtained by identification, and reliability of the identified kinetic parameters is evaluated with the torque residual root.

The invention discloses an independent integrity detection method of a receiver capable of simultaneously detecting and identifying multiple faults. The method comprises the steps of firstly determining a satellite position according to a navigation message, eliminating a satellite with a relatively low elevation angle, and determining an observation matrix which only contains a clock correction term; carrying out parity decomposition on the observation matrix to obtain a parity vector p and a multi-epoch cumulative parity vector P, calculating test statistics separately and carrying out weighting to obtain new test statistics sigma, and comparing the new test statistics sigma with a threshold T to obtain a fault detection result; and carrying out fault mode assumption according to the quantity of faults, and detecting the faults in various fault modes to identify multiple faults. The method is simple, the detection and recognition rate is high, abrupt faults and multiple faults can be detected and recognized, and the method also has a very high detection and recognition rate on small soft faults.

The invention provides a satellite hyper-spectral image compressed sensing reconstruction method based on image sparse regularization. The method comprises the following steps: Step 1, the three-dimensional cube of known hyper-spectral data is rearranged into a matrix; Step 2, a multi-vector measurement model is constructed with a stochastic convolution transform as a linear observation matrix, and each waveband is independently sampled to generate a measurement vector matrix; Step 3, a hyper-spectral image is decomposed in a sparse transform domain into a spectral association component and a difference component, and an image sparse regularization joint reconstruction model including the association component and the difference component is constructed; and Step 4, an alternating-direction multiplier iteration algorithm for solving the joint reconstruction model is put forward, the association component and the difference component of a transform domain are obtained, and then the association component and the difference component are merged to obtain reestablished hyper-spectral data. The method provided by the invention is high in degree of compression and high in precision during satellite hyper-spectral remote sensing data compression.

The invention discloses a frequency domain compressive sensing method aiming at sparse SAR (Synthetic Aperture Radar) images in airspace and belonging to the technical field of signal processing. The frequency domain compressive sensing method particularly comprises the following steps of: step 1: determining the directions of original SAR images, with sparsity; step 2: carrying out Fourier transform on the original SAR images along the directions with the sparsity to obtain frequency domain images of the directions; step 3: building frequency domain sparse reconstructed models, solving model parameters, establishing observation vectors, and reconstructing frequency domain signals so as to form reconstructed frequency domain images; and step 4: carrying out the Fourier transform on the reconstructed frequency domain images along the directions to obtain reconstructed images. In the invention, by analyzing the sparsity of the SAR images in the airspace, the frequency domain sparse reconstructed models are built by aiming at the frequency domain signals, the model parameters are estimated, projection is carried out on the basis of an appropriate observation matrix and the frequency domain signals are reconstructed by utilizing a small quantity of observed values.